about pages of machine learning phd students in America
http://www.mit.edu/~lom/
<a href"https://lorenzomasoero.com/">Lorenzo Masoero</a>
2017-01-01
None
<div><div>
<header>
</header>
<p>I'm a PhD student at MIT in the department of <a href="https://www.eecs.mit.edu/">Electrical Engineering and Computer Science</a> working in <a href="https://www.csail.mit.edu/">CSAIL</a> under the supervision of Professor <a href="http://www.tamarabroderick.com/">Tamara Broderick</a> in the <a href="http://machinelearning.mit.edu/">Machine Learning Group</a>. Before joining MIT for my graduate studies, I was a student at the <a href="http://www.carloalberto.org/">Collegio Carlo Alberto</a> and at the <a href="http://www.unito.it/">University of Torino</a>, where I completed my <a href="http://www.mit.edu/~lom/files/MasterThesisShort.pdf">master's thesis</a> under the supervision of <a href="http://didattica.unibocconi.eu/docenti/cv.php?rif=187032&amp;cognome=PRUENSTER&amp;nome=IGOR">Igor Prünster</a> and <a href="https://sites.google.com/a/carloalberto.org/pdeblasi/">Pierpaolo de Blasi</a>.
</p>
<p>
My current research interests are in machine learning, with an emphasis on Bayesian nonparametric models and network data. <br/> </p>
<p>
<a href="http://www.mit.edu/~lom/files/cv_masoero_2017.pdf">Here</a> you can find a .pdf version of my curriculum vitae.<br/>
You can contact me via email at <strong>lom</strong> at <strong> mit </strong> dot <strong> edu</strong>. 
</p>
</div></div>||||I|||| * 

 Lorenzo Masoero

 I'm a PhD student at MIT in the department of Electrical Engineering and Computer Science working in CSAIL under the supervision of Professor Tamara Broderick in the Machine Learning Group. Before joining MIT for my graduate studies, I was a student at the Collegio Carlo Alberto and at the University of Torino, where I completed my master's thesis under the supervision of Igor Prünster and Pierpaolo de Blasi.

 My current research interests are in machine learning, with an emphasis on Bayesian nonparametric models and network data.

 Here you can find a .pdf version of my curriculum vitae.
 You can contact me via email at lom at mit dot edu.

I'm a PhD student at MIT in the department of Electrical Engineering and Computer Science working in CSAIL under the supervision of Professor Tamara Broderick in the Machine Learning Group. Before joining MIT for my graduate studies, I was a student at the Collegio Carlo Alberto and at the University of Torino, where I completed my master's thesis under the supervision of Igor Prünster and Pierpaolo de Blasi.
My current research interests are in machine learning, with an emphasis on Bayesian nonparametric models and network data.  
Here you can find a .pdf version of my curriculum vitae.
You can contact me via email at lom at  mit  dot  edu.
<a href"https://lorenzomasoero.com/">Lorenzo Masoero</a>
http://www.mit.edu/~lom/
https://annaebair.github.io/
Anna Bair
2018-01-01
None
<div><div>
<div>
<ul>
<li> <a href="https://annaebair.github.io/index.html"> Home </a> </li>
<li> <a href="https://annaebair.github.io/research.html"> Research </a></li>
<li> <a href="https://annaebair.github.io/personal.html"> Personal </a></li>
</ul>
</div>
<div>
<p> </p>
<div>
<br/>
<h4> PhD Student in Machine Learning at Carnegie Mellon University </h4>
<h4> MIT MEng Computer Science and Engineering | 2019 </h4>
<h4> MIT SB Computer Science and Engineering | 2018 </h4>
<p><a href="https://github.com/annaebair"> Github</a> |
	<a href="https://www.linkedin.com/in/annamarie-bair/"> LinkedIn</a> |
	<a href="https://annaebair.github.io/files/Bair_CV.pdf"> CV </a> |
	<a href="https://scholar.google.com/citations?user=saWjkLQAAAAJ&amp;hl=en"> Google Scholar </a>
</p></div>
</div>
<div>
<p>
	I'm currently a PhD student working with Zico Kolter in the Machine Learning Department at Carnegie
	Mellon University. Previously, I completed a Fulbright research grant in
	Barcelona, Spain, and I received my Master's and Bachelor's degrees
	at MIT in Computer Science. My research interests broadly include the science of deep learning and empirical
 phenomena in machine learning. More specifically, I am currently working on sparsity and model compression,
 robustness, and generalization. 
	</p>
</div>
</div></div>||||I|||| * Home
 * Research
 * Personal

 Anna E. Bair


 PhD Student in Machine Learning at Carnegie Mellon University

 MIT MEng Computer Science and Engineering | 2019

 MIT SB Computer Science and Engineering | 2018


 Github | LinkedIn | CV | Google Scholar
 I'm currently a PhD student working with Zico Kolter in the Machine Learning Department at Carnegie Mellon University. Previously, I completed a Fulbright research grant in Barcelona, Spain, and I received my Master's and Bachelor's degrees at MIT in Computer Science. My research interests broadly include the science of deep learning and empirical phenomena in machine learning. More specifically, I am currently working on sparsity and model compression, robustness, and generalization.
Home  
  Research  
  Personal  
 
 PhD Student in Machine Learning at Carnegie Mellon University 
 MIT MEng Computer Science and Engineering | 2019 
 MIT SB Computer Science and Engineering | 2018 
  Github |
	 LinkedIn |
	 CV  |
	 Google Scholar 
 
	I'm currently a PhD student working with Zico Kolter in the Machine Learning Department at Carnegie
	Mellon University. Previously, I completed a Fulbright research grant in
	Barcelona, Spain, and I received my Master's and Bachelor's degrees
	at MIT in Computer Science. My research interests broadly include the science of deep learning and empirical
 phenomena in machine learning. More specifically, I am currently working on sparsity and model compression,
 robustness, and generalization.
Anna Bair
https://annaebair.github.io/
https://nakpinar.github.io/
Nil-Jana Akpinar
2021-02-05
Nil-Jana Akpinar
<div><div><p>
<br />
I am a PhD student in the joint program between the <a href="http://www.stat.cmu.edu/">Department of Statistics and Data Science</a> and the <a href="https://www.ml.cmu.edu/">Machine Learning Department</a> at Carnegie Mellon University, advised by Alexandra Chouldechova and Zachary Lipton.
</p><p>
My research interests lie broadly in machine learning in socially consequential domains with a particular focus on algorithmic fairness.
My current research takes a data and algorithm-centered perspective to study the impact of noise, proxies and dynamics on fairness in machine learning.
</p><p>
My work is generously supported by an <a href="https://www.amazon.science/academic-engagements/five-carnegie-mellon-students-named-amazon-graduate-research-fellows">Amazon Graduate Research Fellowship (2021)</a>. Prior to my PhD, I graduated with a Master of Science in Mathematics from the <a href="http://www.uni-freiburg.de/">University of Freiburg</a> in Germany in 2018. Before that, I obtained a Bachelor of Science in Economics (2017) and a Bachelor of Science in Mathematics (2015) from the University of Freiburg.
</p>
</div>||||I|||| Toggle navigation Nil-Jana Akpinar
 * About Me
 * Publications and CV
 * Contact

 Nil-Jana Akpinar

 PhD student in Statistics and Machine Learning at Carnegie Mellon University


 I am a PhD student in the joint program between the Department of Statistics and Data Science and the Machine Learning Department at Carnegie Mellon University, advised by Alexandra Chouldechova and Zachary Lipton.

 My research interests lie broadly in machine learning in socially consequential domains with a particular focus on algorithmic fairness. My current research takes a data and algorithm-centered perspective to study the impact of noise, proxies and dynamics on fairness in machine learning.

 My work is generously supported by an Amazon Graduate Research Fellowship (2021). Prior to my PhD, I graduated with a Master of Science in Mathematics from the University of Freiburg in Germany in 2018. Before that, I obtained a Bachelor of Science in Economics (2017) and a Bachelor of Science in Mathematics (2015) from the University of Freiburg.

 Curriculum Vitae
 Resume

 News


 * Oct 2022: I was invited to present at the session 'Advances in Responsible AI: From Theory to Applications' at the INFORMS 2022 annual meeting.

 * Oct 2022: I am presenting a poster on 'A Sandbox Tool to Bias(Stress)-Test Fairness Algorithms' at the EAAMO conference. The poster can be found here.

 * Aug 2022: I successfully proposed my thesis research and am now in All But Dissertation status :)

 * Aug 2022: I will be at AIES 2022 presenting our paper 'Long-term Dynamics of Fairness Intervention in Connection Recommender Systems'. A preprint of the paper is available here.

 * Jul 2022: I am presenting our poster on 'Counterfactual Metrics for Auditing Black-Box Recommender Systems' at the Responsible Decision Making in Dynamic Environments Workshop at ICML 2022. Paper and poster can be found here.

 * Apr 2022: New preprint 'A Sandbox Tool to Bias(Stress)-Test Fairness Algorithms' available here.

 * May 2021: Amazon awarded me a Graduate Research Fellowship.

 * Mar 2021: I am joining LinkedIn as a Machine Learning Engineering intern for this summer.

 * Feb 2021: Our FAccT 2021 paper got featured by MIT Technology Review here.

 * Jan 2021: I joined the editorial board of the ML@CMU blog.

 * Dec 2020: Our paper 'The effect of differential victim crime reporting on predictive policing systems' was accepted for publication at the Conference on Fairness, Accountability, and Transparency (FAccT 2021).

 * Jul 2020: I am presenting our paper 'Analyzing Student Strategies In Blended Courses Using Clickstream Data' at the Educational Data Mining conference 2020. A preprint can be found here, and a video of the presentation is also available.

 * May 2020: CMU just awarded me the degree of Master of Science in Statistics.

 * Feb 2020: I will be joining LinkedIn as a Fairness and Privacy Research Engineering Intern this summer.

 * Feb 2020: My poster presentation won the best 3-minute student presentation award at AAAI. The poster can be found here.

 * Dec 2019: I will be presenting a poster at AAAI-20 featuring joint work with Bernhard Kratzwald and Stefan Feuerriegel. A longer version of the paper can be found here.


 Last updated: October 2022
 * Email me
 * GitHub
 * Twitter
 * LinkedIn

 Nil-Jana Akpinar • 2022

 Theme by beautiful-jekyll

I am a PhD student in the joint program between the Department of Statistics and Data Science and the Machine Learning Department at Carnegie Mellon University, advised by Alexandra Chouldechova and Zachary Lipton.
 
My research interests lie broadly in machine learning in socially consequential domains with a particular focus on algorithmic fairness.
My current research takes a data and algorithm-centered perspective to study the impact of noise, proxies and dynamics on fairness in machine learning.
 
My work is generously supported by an Amazon Graduate Research Fellowship (2021). Prior to my PhD, I graduated with a Master of Science in Mathematics from the University of Freiburg in Germany in 2018. Before that, I obtained a Bachelor of Science in Economics (2017) and a Bachelor of Science in Mathematics (2015) from the University of Freiburg.
Nil-Jana Akpinar
https://nakpinar.github.io/
http://www.mit.edu/~shiffman/
Miriam Shiffman
2016-08-12
None
<div><div>
 <h2>Â» about</h2> 
 <p>
 I'm a Ph.D. student
 in the MIT <a href="http://machinelearning.mit.edu/">Machine Learning Group</a>
 and at the <a href="http://broadinstitute.org/">Broad Institute</a>,
 working on probabilistic methods for learning from single-cell studies.
 </p><p>
 
 I am co-advised by
 <a href="http://www.tamarabroderick.com/">Tamara Broderick</a>
 (MIT CSAIL) and
 <a href="https://www.broadinstitute.org/bios/aviv-regev">Aviv Regev</a>
 (Broad Institute of MIT &amp; Harvard).
 </p>
 <p>
 Before that, I was a Molecular Biology major at <a href="https://www.pomona.edu/">Pomona College</a>,
 a <a href="https://www.fulbright.org.au/alumni/profiles/2013-alumni/#Miriam-shiffman">Fulbright scholar</a> at the
 <a href="https://www.ecogenomic.org/">Australian Centre for Ecogenomics</a>,
 
 
 an
 intern at <a href="https://experiments.fastforwardlabs.com/">Fast Forward Labs</a>,
 
 and a cheese microbiologist at <a href="https://www.jasperhillfarm.com/lab/">Jasper Hill</a>.
 </p>
 </div><div>
 <h2>Â» projects</h2>
 <div>
 <p></p>
 <h3>Bayesian tree models of cellular differentiation</h3>
 <h5>[ MIT CSAIL, Broad Institute ]</h5><p>
 Inferring latent cell state dynamics
 (along a probabilistic branching continuum)
 from static snapshots (single-cell </p><p>
 profiles of gene expression).
 </p>
 <ul>
 <li>
 <u>Shiffman M</u>, WT Stephenson,<br />
 G Schiebinger, J Huggins, T Campbell,<br />
 A Regev, T Broderick (2018)
 Reconstructing probabilistic trees of cellular differentiation
 from single-cell RNA-seq data. <i>arXiv</i>:1811.11790
 <br />
 (<a href="https://arxiv.org/abs/1811.11790">paper</a>,
 <a href="posters/neurips18_printme.pdf">poster '18</a>,
 <a href="http://approximateinference.org/2017/accepted/ShiffmanEtAl2017_poster.pdf">poster '17</a>,
 <a href="https://www.youtube.com/watch?v=LdbKF_4Q0SQ">talk</a>)
 </li>
 <li>
 <a>
 Creative probabilistic programming <br /> for biology
 (</a><a href="pdfs/lmrl.pdf">abstract</a>,
 <a href="posters/neurips19.repencil.pdf">poster</a>)
 
 
 </li>
 </ul>
 </div>
 
 <div>
 <p></p><div>
 <h3>Microbial ecology and evolution</h3>
 <h5>[ Australian Centre for Ecogenomics, Jasper Hill Farm, Harvard Systems Biology ]</h5><p>
 Computational and experimental approaches to microbial
 communities in marsupial guts, the human microbiome, and
 cheese.
 </p><ul> 
 <li>
 <u>Shiffman M</u>, RM Soo, PG Dennis, M Morrison, GW
 Tyson, P Hugenholtz (2017) Gene and genome-centric
 analyses of koala and wombat fecal microbiomes point to
 metabolic specialization for Eucalyptus digestion.
 <i>PeerJ</i>. 
 <br />
 (<a href="https://doi.org/10.7717/peerj.4075">paper</a>,
 <a href="https://peerj.com/blog/post/115284879869/gut-instinct-how-can-koalas-eat-such-toxic-eucalyptus-tree-leaves/"> blogpost</a>)
 </li>
 <li>
 <u>Shiffman M</u>, J Button, RJ Dutton (2014)
 Cheese as a model microbial ecosystem. <i>International Society
 for Microbial Ecology</i> (ISME) conference.
 (<a href="posters/Shiffman_ISME14_poster.png">poster</a>)
 </li>
 <li>
 <u>Shiffman M</u>, LM Seligman (2013)
 Evolutionary dynamics of intein-encoded homing endonucleases.
 
 (<a href="pdfs/MOBI Thesis - Real Thang.pdf">thesis</a>,
 <a href="pdfs/MOBI Thesis - Real Thang Figures.pdf"> figs</a>)
 </li>
 <li>
 <u>Shiffman M</u>, BM Charalambous (2012) The search
 for archaeal pathogens. <i>Reviews in Medical Microbiology</i>.
 
 (<a href="pdfs/search-for-archaeal-pathogens.pdf">paper</a>)
 
 </li>
 </ul>
 </div>
 </div>
 </div>||||I|||| Miriam Shiffman

 * Ph.D. student (Computational & Systems Biology @ MIT)
 * email: shiffman [at] mit [dot] edu
 * cv: pdf (May 2020)
 * github: meereeum
 * twitter: meereeum

 Â» about

 I'm a Ph.D. student in the MIT Machine Learning Group and at the Broad Institute, working on probabilistic methods for learning from single-cell studies.

 I am co-advised by Tamara Broderick (MIT CSAIL) and Aviv Regev (Broad Institute of MIT & Harvard).

 Before that, I was a Molecular Biology major at Pomona College, a Fulbright scholar at the Australian Centre for Ecogenomics, an intern at Fast Forward Labs, and a cheese microbiologist at Jasper Hill.

 Â» projects

 Bayesian tree models of cellular differentiation

 [ MIT CSAIL, Broad Institute ]

 Inferring latent cell state dynamics (along a probabilistic branching continuum) from static snapshots (single-cell profiles of gene expression).
 * Shiffman M, WT Stephenson,
 G Schiebinger, J Huggins, T Campbell,
 A Regev, T Broderick (2018) Reconstructing probabilistic trees of cellular differentiation from single-cell RNA-seq data. arXiv:1811.11790
 (paper, poster '18, poster '17, talk)
 * Creative probabilistic programming
 for biology (abstract, poster)

 Variational autoencoders, in prose and code

 [ Fast Forward Labs ]

 Whitepaper-style blog series for FFL.
 * Introducing variational autoencoders (blogpost)
 * Under the hood of the variational autoencoder (blogpost)

 Microbial ecology and evolution

 [ Australian Centre for Ecogenomics, Jasper Hill Farm, Harvard Systems Biology ]

 Computational and experimental approaches to microbial communities in marsupial guts, the human microbiome, and cheese.
 * Shiffman M, RM Soo, PG Dennis, M Morrison, GW Tyson, P Hugenholtz (2017) Gene and genome-centric analyses of koala and wombat fecal microbiomes point to metabolic specialization for Eucalyptus digestion. PeerJ.
 (paper, blogpost)
 * Shiffman M, J Button, RJ Dutton (2014) Cheese as a model microbial ecosystem. International Society for Microbial Ecology (ISME) conference. (poster)
 * Shiffman M, LM Seligman (2013) Evolutionary dynamics of intein-encoded homing endonucleases. (thesis, figs)
 * Shiffman M, BM Charalambous (2012) The search for archaeal pathogens. Reviews in Medical Microbiology. (paper)
Â» about 
 
 I'm a Ph.D. student
 in the MIT Machine Learning Group
 and at the Broad Institute,
 working on probabilistic methods for learning from single-cell studies.
  
 
 I am co-advised by
 Tamara Broderick
 (MIT CSAIL) and
 Aviv Regev
 (Broad Institute of MIT &amp; Harvard).
 
 
 Before that, I was a Molecular Biology major at Pomona College,
 a Fulbright scholar at the
 Australian Centre for Ecogenomics,
 
 
 an
 intern at Fast Forward Labs,
 
 and a cheese microbiologist at Jasper Hill.
 
  
 Â» projects
 
  
 Bayesian tree models of cellular differentiation
 [ MIT CSAIL, Broad Institute ] 
 Inferring latent cell state dynamics
 (along a probabilistic branching continuum)
 from static snapshots (single-cell  
 profiles of gene expression).
 
 
 
 Shiffman M, WT Stephenson,
 G Schiebinger, J Huggins, T Campbell,
 A Regev, T Broderick (2018)
 Reconstructing probabilistic trees of cellular differentiation
 from single-cell RNA-seq data. arXiv:1811.11790
 
 (paper,
 poster '18,
 poster '17,
 talk)
 
 
 
 Creative probabilistic programming  for biology
 ( abstract,
 poster)
 
 
 
 
 
 
 
   
 Microbial ecology and evolution
 [ Australian Centre for Ecogenomics, Jasper Hill Farm, Harvard Systems Biology ] 
 Computational and experimental approaches to microbial
 communities in marsupial guts, the human microbiome, and
 cheese.
   
 
 Shiffman M, RM Soo, PG Dennis, M Morrison, GW
 Tyson, P Hugenholtz (2017) Gene and genome-centric
 analyses of koala and wombat fecal microbiomes point to
 metabolic specialization for Eucalyptus digestion.
 PeerJ. 
 
 (paper,
  blogpost)
 
 
 Shiffman M, J Button, RJ Dutton (2014)
 Cheese as a model microbial ecosystem. International Society
 for Microbial Ecology (ISME) conference.
 (poster)
 
 
 Shiffman M, LM Seligman (2013)
 Evolutionary dynamics of intein-encoded homing endonucleases.
 
 (thesis,
  figs)
 
 
 Shiffman M, BM Charalambous (2012) The search
 for archaeal pathogens. Reviews in Medical Microbiology.
 
 (paper)
Miriam Shiffman
http://www.mit.edu/~shiffman/
http://www.mit.edu/~vgarg/
Vikas K. Garg
2024-03-13
None
<div><div><p>
V. Garg. , Current Opinion in Structural Biology, 2024. <br/>
Y. Verma, M. Heinonen, and V. Garg. , ICLR, 2024 (<b>Oral</b>). <br/>
Y. Jiang, C. Zhou, V. Garg(+), and A. Oulasvirta(+). , CHI, 2024. <br/>
	J. Immonen(+), A. Souza(+), and V. Garg. , NeurIPS, 2023 (<b>Oral</b>). <br/> 
	T. Garipov, S. De Peuter, G. Yang, V. Garg, S. Kaski, and T. Jaakkola. , NeurIPS, 2023. <br/>
	 Y. Verma, M. Heinonen, and V. Garg. , ICML, 2023. <br/>
I. Moflic, V. Garg, and A. Paler. , APS, 2023. <br/>
	D. Alvarez-Melis (+*), V. Garg(+*), and A. Kalai (+*). , NeurIPS, 2022 (<b>Spotlight</b>). <br/> 
 Y. Verma, S. Kaski, M. Heinonen, and V. Garg. , NeurIPS, 2022. <br/>
A. Souza, D. Mesquita, S. Kaski, and V. Garg. , NeurIPS, 2022. <br/> 
G. Mercatali, A. Freitas, and V. Garg. , NeurIPS, 2022. <br/>
	V. K. Garg(*), A. Kalai(*), K. Ligett(*), and S. Wu(*). , AISTATS, 2021 (<b>Oral</b>). <br/>
 V. K. Garg, S. Jegelka, and T. Jaakkola. , ICML, 2020 (<b>Oral - Virtual</b>). <br/> 
	 V. K. Garg and T. Jaakkola. , ICML, 2020 (<b>Oral - Virtual</b>). <br/> 	J. Ingraham, V. K. Garg, R. Barzilay, and T. Jaakkola. , NeurIPS, 2019. <br/>	V. K. Garg and T. Jaakkola. , NeurIPS, 2019. <br/> 	
	V. K. Garg and T. Pichkhadze. , NeurIPS, 2019. <br/> 	
	V. K. Garg, O. Dekel, and L. Xiao. NeurIPS, 2018. <br/>
V. K. Garg and A. Kalai. NeurIPS, 2018 (<b>Spotlight</b>). <br/> 
 V. K. Garg, L. Xiao, and O. Dekel. UAI, 2018. <br/>
V. K. Garg and T. Jaakkola. NIPS, 2017. <br/>
	V. K. Garg and T. Jaakkola. NIPS, 2016. <br/> 
	V. K. Garg, C. Rudin, and T. Jaakkola. AISTATS, 2016. <br/>	S. Shankar, V. K. Garg, and R. Cipolla. CVPR, 2015. <br/> 	 R. Kondor, N. Teneva, and V. K. Garg. ICML, 2014 (<b>Oral</b>). <br/> 	V. K. Garg, T. S. Jayram, and B. Narayanaswamy. AAAI, 2013. <br/> 
 S. Kpotufe and V. K. Garg. NIPS, 2013. <br/>
 
 V. K. Garg, M. N. Murty, and Y. Narahari. TKDE, 2013. <br/> 
	 P. Agrawal(+), V. K. Garg(+), and R. Narayanam. IJCAI, 2013. </p><p>
[(+): Equal Contribution, (*): Alphabetical Order] </p></div></div>||||I|||| Vikas K. Garg
 I'm an Assistant Professor at Aalto University, Finland, and Chief Scientist at YaiYai, a company that provides cutting-edge deep tech (AI and Quantum) solutions. For business inquiries, contact info@yaiyai.fi
 vgarg@mit.edu, vgarg@csail.mit.edu Connect on LinkedIn

 Previously, I led research and engineering efforts at IBM Research, Amazon A9, and Microsoft Research. I hold a PhD in Computer Science (EECS) from MIT, where I worked closely with Tommi Jaakkola (PhD supervisor), Cynthia Rudin, Regina Barzilay, and Stefanie Jegelka. While at MIT, I was closely associated with the Machine Learning for Pharmaceutical Discovery and Synthesis (MLPDS) Consortium, and also served as a BP Technologies Energy Fellow. I also collaborated extensively with Adam Kalai, Ofer Dekel, and Lin Xiao.

 My research interests include (geometric and topological) deep learning, quantum computing, generative models, physics-inspired models, and learning under uncertainty or resource constraints along with their intersections with optimization and game theory. I'm particularly passionate about applications of generative AI in biodesign and sustainable energy (circular economy).


 Select Publications

 V. Garg. Generative AI for graph-based drug design: Recent advances and the way forward , Current Opinion in Structural Biology, 2024. (PDF)
 Y. Verma, M. Heinonen, and V. Garg. ClimODE: Climate and Weather Forecasting with Physics-informed Neural ODEs , ICLR, 2024 (Oral). (PDF)
 Y. Jiang, C. Zhou, V. Garg(+), and A. Oulasvirta(+). Graph4GUI: Graph Neural Networks for Representing Graphical User Interfaces , CHI, 2024. (PDF)
 J. Immonen(+), A. Souza(+), and V. Garg. Going beyond persistent homology using persistent homology , NeurIPS, 2023 (Oral). (PDF)
 T. Garipov, S. De Peuter, G. Yang, V. Garg, S. Kaski, and T. Jaakkola. Compositional Sculpting of Iterative Generative Processes , NeurIPS, 2023. (PDF)
 Y. Verma, M. Heinonen, and V. Garg. AbODE: Ab initio antibody design using conjoined ODEs , ICML, 2023. (PDF)
 I. Moflic, V. Garg, and A. Paler. Graph Neural Network Autoencoders for Efficient Quantum Circuit Optimisation , APS, 2023. (PDF)
 D. Alvarez-Melis (+*), V. Garg(+*), and A. Kalai (+*). Are GANs overkill for NLP? , NeurIPS, 2022 (Spotlight). (PDF)
 Y. Verma, S. Kaski, M. Heinonen, and V. Garg. Modular Flows: Differential Molecular Generation , NeurIPS, 2022. (PDF)
 A. Souza, D. Mesquita, S. Kaski, and V. Garg. Provably expressive temporal graph networks , NeurIPS, 2022. (PDF)
 G. Mercatali, A. Freitas, and V. Garg. Symmetry-induced Disentanglement on Graphs , NeurIPS, 2022. (PDF)
 V. K. Garg(*), A. Kalai(*), K. Ligett(*), and S. Wu(*). Learn to Expect the Unexpected: Probably Approximately Correct Domain Generalization , AISTATS, 2021 (Oral). (PDF)
 V. K. Garg, S. Jegelka, and T. Jaakkola. Generalization and Representational Limits of Graph Neural Networks , ICML, 2020 (Oral - Virtual). (PDF) V. K. Garg and T. Jaakkola. Predicting deliberative outcomes , ICML, 2020 (Oral - Virtual). (PDF) J. Ingraham, V. K. Garg, R. Barzilay, and T. Jaakkola. Generative Models for Graph-Based Protein Design , NeurIPS, 2019. (PDF) (Code) V. K. Garg and T. Jaakkola. Solving graph compression via optimal transport , NeurIPS, 2019. (PDF) V. K. Garg and T. Pichkhadze. Online Markov Decoding: Lower Bounds and Near-Optimal Approximation Algorithms , NeurIPS, 2019. (PDF) V. K. Garg, O. Dekel, and L. Xiao. Learning SMaLL Predictors, NeurIPS, 2018. (PDF) V. K. Garg and A. Kalai. Supervising Unsupervised Learning, NeurIPS, 2018 (Spotlight). (PDF)
 V. K. Garg, L. Xiao, and O. Dekel. Sparse Multi-Prototype Classification, UAI, 2018. (PDF) V. K. Garg and T. Jaakkola. Local Aggregative Games, NIPS, 2017. (PDF)
 V. K. Garg and T. Jaakkola. Learning Tree Structured Potential Games, NIPS, 2016. (PDF)
 V. K. Garg, C. Rudin, and T. Jaakkola. CRAFT: ClusteR-specific Assorted Feature selecTion, AISTATS, 2016. (PDF) (Code)
 S. Shankar, V. K. Garg, and R. Cipolla. Deep Carving: Discovering Visual Attributes by Carving Deep Neural Nets, CVPR, 2015. (PDF)
 R. Kondor, N. Teneva, and V. K. Garg. Multiresolution Matrix Factorization, ICML, 2014 (Oral). (PDF)
 V. K. Garg, T. S. Jayram, and B. Narayanaswamy. Online Optimization with Dynamic Temporal Uncertainty, AAAI, 2013. (PDF)
 S. Kpotufe and V. K. Garg. Adaptivity to Local Smoothness and Dimension in Kernel Regression, NIPS, 2013. (PDF)
 V. K. Garg, M. N. Murty, and Y. Narahari. Novel Biobjective Clustering (BiGC) Based on Cooperative Game Theory, TKDE, 2013. (PDF)
 P. Agrawal(+), V. K. Garg(+), and R. Narayanam. Link Label Prediction in Signed Social Networks, IJCAI, 2013. (PDF)

 [(+): Equal Contribution, (*): Alphabetical Order]
V. Garg. , Current Opinion in Structural Biology, 2024. 
Y. Verma, M. Heinonen, and V. Garg. , ICLR, 2024 (Oral). 
Y. Jiang, C. Zhou, V. Garg(+), and A. Oulasvirta(+). , CHI, 2024. 
	J. Immonen(+), A. Souza(+), and V. Garg. , NeurIPS, 2023 (Oral).  
	T. Garipov, S. De Peuter, G. Yang, V. Garg, S. Kaski, and T. Jaakkola. , NeurIPS, 2023. 
	 Y. Verma, M. Heinonen, and V. Garg. , ICML, 2023. 
I. Moflic, V. Garg, and A. Paler. , APS, 2023. 
	D. Alvarez-Melis (+*), V. Garg(+*), and A. Kalai (+*). , NeurIPS, 2022 (Spotlight).  
 Y. Verma, S. Kaski, M. Heinonen, and V. Garg. , NeurIPS, 2022. 
A. Souza, D. Mesquita, S. Kaski, and V. Garg. , NeurIPS, 2022.  
G. Mercatali, A. Freitas, and V. Garg. , NeurIPS, 2022. 
	V. K. Garg(*), A. Kalai(*), K. Ligett(*), and S. Wu(*). , AISTATS, 2021 (Oral). 
 V. K. Garg, S. Jegelka, and T. Jaakkola. , ICML, 2020 (Oral - Virtual).  
	 V. K. Garg and T. Jaakkola. , ICML, 2020 (Oral - Virtual).  	J. Ingraham, V. K. Garg, R. Barzilay, and T. Jaakkola. , NeurIPS, 2019. 	V. K. Garg and T. Jaakkola. , NeurIPS, 2019.  	
	V. K. Garg and T. Pichkhadze. , NeurIPS, 2019.  	
	V. K. Garg, O. Dekel, and L. Xiao. NeurIPS, 2018. 
V. K. Garg and A. Kalai. NeurIPS, 2018 (Spotlight).  
 V. K. Garg, L. Xiao, and O. Dekel. UAI, 2018. 
V. K. Garg and T. Jaakkola. NIPS, 2017. 
	V. K. Garg and T. Jaakkola. NIPS, 2016.  
	V. K. Garg, C. Rudin, and T. Jaakkola. AISTATS, 2016. 	S. Shankar, V. K. Garg, and R. Cipolla. CVPR, 2015.  	 R. Kondor, N. Teneva, and V. K. Garg. ICML, 2014 (Oral).  	V. K. Garg, T. S. Jayram, and B. Narayanaswamy. AAAI, 2013.  
 S. Kpotufe and V. K. Garg. NIPS, 2013. 
 
 V. K. Garg, M. N. Murty, and Y. Narahari. TKDE, 2013.  
	 P. Agrawal(+), V. K. Garg(+), and R. Narayanam. IJCAI, 2013.  
[(+): Equal Contribution, (*): Alphabetical Order]
Vikas K. Garg
http://www.mit.edu/~vgarg/
https://rishibommasani.github.io/
Rishi Bommasani
2021-06-07
None
<div><div>
<p>Hi! I'm a third-year Ph.D. student in the <a href="https://cs.stanford.edu/">Department of Computer Science</a> at <a href="https://www.stanford.edu/">Stanford University</a> advised by <a href="https://cs.stanford.edu/~pliang/">Percy Liang</a> and <a href="https://web.stanford.edu/~jurafsky/">Dan Jurafsky</a>.
<br /> I helped build and currently help organize the <a href="https://crfm.stanford.edu/">Stanford Center for Research on Foundation Models (CRFM)</a>. I am also affiliated with <a href="https://nlp.stanford.edu/">Stanford NLP</a>, <a href="https://ai.stanford.edu/">Stanford AI</a>, and <a href="https://hai.stanford.edu/">Stanford HAI</a>. 	
<br /> My graduate studies are funded by the <a href="https://www.nsfgrfp.org/resources/about_grfp">National Science Foundation Graduate Research Fellowship Program (NSF GRFP)</a>. <a href="https://www.cs.cornell.edu/information/news/newsitem11136/cs-graduate-students-receive-national-science-foundation-graduate"> [Press Release] </a> </p>
<p> I graduated with a B.A. (Mathematics, Computer Science; 2019) and M.S. (Computer Science; 2020) from <a href="https://www.cornell.edu/">Cornell University</a>, where I was advised by the incomparable <a href="http://www.cs.cornell.edu/home/cardie/">Claire Cardie</a>.
<br /> I am honored to have learned from the <a href="https://news.psu.edu/story/660710/2021/06/07/college-engineering-mourns-loss-faculty-member-arzoo-katiyar">late</a> <a href="https://sites.google.com/site/arzook99/home">Professor Arzoo Katiyar</a>, who I profoundly miss. 
<br /> During my time at Cornell, <a href="http://www.cs.cornell.edu/">Cornell CS</a> was my home. The department wrote this nice piece about my journey at Cornell. <a href="https://www.cs.cornell.edu/information/news/newsitem11229/profile-cornell-cs-ba-and-ms-graduate-rishi-bommasani"> [Profile] </a> <a href="https://www.cs.cornell.edu/information/news/newsitem11227/lillian-lee-and-tacl-cs-graduate-stanford-new-platform-data-privacy"> [Profile 2] </a> </p>
<p> I research the societal impact of AI systems. 
<br /> Primary research interests: <a href="https://arxiv.org/abs/2108.07258">foundation models</a>, <a href="https://arxiv.org/abs/2211.09110">evaluation</a>, <a href="https://arxiv.org/abs/2211.13972">inequity and systemic harm</a>, <a href="https://arxiv.org/abs/2206.03216">data</a>
<br /> Secondary research interests: computational social science, language acquisition, algorithms and TCS </p>
	
<div>
<h2> What have I been up to?</h2>
<ul>
<li><b>December 2022</b> I wrote two papers on evaluation: Evaluation for Change (E4C; position paper) and Trustworthy Social Bias Measurement (DivDist; w/ Percy). <a href="https://arxiv.org/abs/2212.11670">[E4C]</a> <a href="https://arxiv.org/abs/2212.11672">[DivDist]</a> </li>
<li><b>December 2022</b> Led by <a href="https://minalee.info/">Mina Lee</a>, we released Evaluating Human-Language Model Interaction! Mina is on the job market! <a href="https://arxiv.org/abs/2212.09746">[Paper]</a> </li>
<li><b>November 2022</b> I presented our paper at NeurIPS on systemic harms to individuals, coauthored with <a href="https://kathleenacreel.com/">Katie Creel</a>, <a href="https://ananyakumar.wordpress.com/">Ananya Kumar (on job market!)</a>, Dan, and Percy. <a href="https://arxiv.org/abs/2211.13972">[Paper]</a> </li>
<li><b>November 2022</b> We released HELM on the holistic evaluation of language models as CRFM's first flagship project with 50 coauthors, co-led with Percy and <a href="https://www.linkedin.com/in/tonyhlee">Tony Lee</a>. <a href="https://arxiv.org/abs/2211.09110">[Paper]</a> <a href="https://crfm.stanford.edu/helm">[Website]</a> <a href="https://crfm.stanford.edu/2022/11/17/helm.html">[Blog]</a></li> 
<li><b>September 2022</b> CRFM celebrated its 1 year anniversary, with <a href="https://venturebeat.com/author/sharongoldman/">Sharon Goldman</a> of VentureBeat writing on why 2022 is the year of foundation models. <a href="https://venturebeat.com/ai/foundation-models-2022s-ai-paradigm-shift/">[Press]</a> </li>
<li><b>August 2022</b> Our paper on the emergent abilities of language models, led by <a href="https://jasonwei20.github.io/">Jason Wei</a>, is published at TMLR. <a href="https://arxiv.org/abs/2206.07682">[arXiv]</a> <a href="https://venturebeat.com/2022/06/28/4-ai-research-trends-everyone-is-or-will-be-talking-about/">[Press]</a> </li>
<li><b>June 2022</b> HAI featured a story on my research journey and role in building the CRFM community. <a href="https://www.linkedin.com/pulse/rishi-bommasani-building-community-studying-foundation-models-?trk=public_post-content_share-article">[Story]</a> </li>
<li><b>May 2022</b> Percy, <a href="https://kathleenacreel.com/">Katie Creel</a>, <a href="http://robreich.stanford.edu/">Rob Reich</a>, and I argued for community norms to govern foundation models. <a href="https://crfm.stanford.edu/2022/05/17/community-norms.html">[Blog]</a> <a href="https://www.protocol.com/enterprise/foundation-models-ai-standards-stanford">[Press]</a> </li>
<li><b>August 2021</b> We inaugurated the <a href="https://crfm.stanford.edu/"> Center for Research on Foundation Models (CRFM)</a>, an amazing interdisciplinary initiative spanning 10+ departments! <a href="https://hai.stanford.edu/news/introducing-center-research-foundation-models-crfm">[Announcement Story]</a> </li>	 
<li><b>August 2021</b> We released our long report: <a href="https://arxiv.org/abs/2108.07258">On the Opportunties and Risks of Foundation Models</a>! Thanks to all 113 of my coauthors! <a href="https://crfm.stanford.edu/report.html">[Paper]</a> <a href="https://youtu.be/ZshcPdavsdU">[Explainer Video]</a> </li>	
	</ul></div>
	
</div>||||I|||| * Home
 * Papers
 * Collaborators
 * Teaching
 * Service
 * Awards
 * CV

 Rishi Bommasani

 Hi! I'm a third-year Ph.D. student in the Department of Computer Science at Stanford University advised by Percy Liang and Dan Jurafsky.
 I helped build and currently help organize the Stanford Center for Research on Foundation Models (CRFM). I am also affiliated with Stanford NLP, Stanford AI, and Stanford HAI.
 My graduate studies are funded by the National Science Foundation Graduate Research Fellowship Program (NSF GRFP). [Press Release]

 I graduated with a B.A. (Mathematics, Computer Science; 2019) and M.S. (Computer Science; 2020) from Cornell University, where I was advised by the incomparable Claire Cardie.
 I am honored to have learned from the late Professor Arzoo Katiyar, who I profoundly miss.
 During my time at Cornell, Cornell CS was my home. The department wrote this nice piece about my journey at Cornell. [Profile] [Profile 2]

 I research the societal impact of AI systems.
 Primary research interests: foundation models, evaluation, inequity and systemic harm, data
 Secondary research interests: computational social science, language acquisition, algorithms and TCS

 What have I been up to?

 * December 2022 I wrote two papers on evaluation: Evaluation for Change (E4C; position paper) and Trustworthy Social Bias Measurement (DivDist; w/ Percy). [E4C] [DivDist]
 * December 2022 Led by Mina Lee, we released Evaluating Human-Language Model Interaction! Mina is on the job market! [Paper]
 * November 2022 I presented our paper at NeurIPS on systemic harms to individuals, coauthored with Katie Creel, Ananya Kumar (on job market!), Dan, and Percy. [Paper]
 * November 2022 We released HELM on the holistic evaluation of language models as CRFM's first flagship project with 50 coauthors, co-led with Percy and Tony Lee. [Paper] [Website] [Blog]
 * September 2022 CRFM celebrated its 1 year anniversary, with Sharon Goldman of VentureBeat writing on why 2022 is the year of foundation models. [Press]
 * August 2022 Our paper on the emergent abilities of language models, led by Jason Wei, is published at TMLR. [arXiv] [Press]
 * June 2022 HAI featured a story on my research journey and role in building the CRFM community. [Story]
 * May 2022 Percy, Katie Creel, Rob Reich, and I argued for community norms to govern foundation models. [Blog] [Press]
 * August 2021 We inaugurated the Center for Research on Foundation Models (CRFM), an amazing interdisciplinary initiative spanning 10+ departments! [Announcement Story]
 * August 2021 We released our long report: On the Opportunties and Risks of Foundation Models! Thanks to all 113 of my coauthors! [Paper] [Explainer Video]
 * Email
 * Twitter
 * Google Scholar
 * Github
 * CV
Hi! I'm a third-year Ph.D. student in the Department of Computer Science at Stanford University advised by Percy Liang and Dan Jurafsky.
 I helped build and currently help organize the Stanford Center for Research on Foundation Models (CRFM). I am also affiliated with Stanford NLP, Stanford AI, and Stanford HAI. 	
 My graduate studies are funded by the National Science Foundation Graduate Research Fellowship Program (NSF GRFP).  [Press Release]  
 I graduated with a B.A. (Mathematics, Computer Science; 2019) and M.S. (Computer Science; 2020) from Cornell University, where I was advised by the incomparable Claire Cardie.
 I am honored to have learned from the late Professor Arzoo Katiyar, who I profoundly miss. 
 During my time at Cornell, Cornell CS was my home. The department wrote this nice piece about my journey at Cornell.  [Profile]   [Profile 2]  
 I research the societal impact of AI systems. 
 Primary research interests: foundation models, evaluation, inequity and systemic harm, data
 Secondary research interests: computational social science, language acquisition, algorithms and TCS 
	
 What have I been up to?
 December 2022 I wrote two papers on evaluation: Evaluation for Change (E4C; position paper) and Trustworthy Social Bias Measurement (DivDist; w/ Percy). [E4C] [DivDist] 
 December 2022 Led by Mina Lee, we released Evaluating Human-Language Model Interaction! Mina is on the job market! [Paper] 
 November 2022 I presented our paper at NeurIPS on systemic harms to individuals, coauthored with Katie Creel, Ananya Kumar (on job market!), Dan, and Percy. [Paper] 
 November 2022 We released HELM on the holistic evaluation of language models as CRFM's first flagship project with 50 coauthors, co-led with Percy and Tony Lee. [Paper] [Website] [Blog]  
 September 2022 CRFM celebrated its 1 year anniversary, with Sharon Goldman of VentureBeat writing on why 2022 is the year of foundation models. [Press] 
 August 2022 Our paper on the emergent abilities of language models, led by Jason Wei, is published at TMLR. [arXiv] [Press] 
 June 2022 HAI featured a story on my research journey and role in building the CRFM community. [Story] 
 May 2022 Percy, Katie Creel, Rob Reich, and I argued for community norms to govern foundation models. [Blog] [Press] 
 August 2021 We inaugurated the  Center for Research on Foundation Models (CRFM), an amazing interdisciplinary initiative spanning 10+ departments! [Announcement Story] 	 
 August 2021 We released our long report: On the Opportunties and Risks of Foundation Models! Thanks to all 113 of my coauthors! [Paper] [Explainer Video]
Rishi Bommasani
https://rishibommasani.github.io/
https://w4ngatang.github.io/
Alex Wang
2016-06-01
Alex Wang
<div><div>
 <div><p>I work on machine learning at <a href="https://cohere.ai/">Cohere</a>.
</p></div>
<p>If you need to contact me, send me a message at wangalexc <em>at</em> gmail or on <a href="https://twitter.com/W4ngatang">Twitter</a>.
If you aren’t sure you have the right Alex Wang, try <a href="https://www.cs.cmu.edu/~alw1/disambiguation.html">this directory</a>.</p>
<h3>Papers</h3>
<p><strong>What Do NLP Researchers Believe? Results of the NLP Community Metasurvey</strong> <br />
Julian Michael, Ari Holtzman, Alicia Parrish, Aaron Mueller, <strong>Alex Wang</strong>, Angelica Chen, Divyam Madaan, Nikita Nangia, Richard Yuanzhe Pang, Jason Phang, Samuel R. Bowman <br />
arXiv preprint <br />
[<a href="https://arxiv.org/abs/2208.12852">paper</a>] [<a href="https://nlpsurvey.net/">site</a>]</p>
<p><strong>SQuALITY: Building a Long-Document Summarization Dataset the Hard Way</strong> <br />
<strong>Alex Wang</strong>, Richard Yuanzhe Pang, Angelica Chen, Jason Phang, Samuel R. Bowman <br />
arXiv preprint <br />
[<a href="https://w4ngatang.github.io/static/papers/squality.pdf">paper</a>] [<a href="https://github.com/nyu-mll/SQuALITY">code</a>]</p>
<p><strong>QuestEval: Summarization Asks for Fact-Based Evaluation.</strong> <br />
Thomas Scialom, Paul-Alexis Dray, Patrick Gallinari, Sylvain Lamprier, Benjamin Piwowarski, Jacopo Staiano, <strong>Alex Wang</strong> <br />
EMNLP 2022 <br />
[<a href="https://arxiv.org/abs/2103.12693">paper</a>]</p>
<p><strong>Label Representations in Modeling Classification as Text Generation</strong> <br />
Xinyi Chen, Jiangxian Xu, <strong>Alex Wang</strong><br />
AACL-IJCNLP SRW 2020 (<strong>Best Paper Award Runner-Up</strong>). <br />
[<a href="https://www.aclweb.org/anthology/2020.aacl-srw.23/">paper</a>]</p>
<p><strong>Asking and Answering Questions to Evaluate the Factual Consistency of Summaries</strong> <br />
<strong>Alex Wang</strong>, Kyunghyun Cho, Mike Lewis <br />
ACL 2020 <br />
[<a href="https://arxiv.org/abs/2004.04228">paper</a>] [<a href="https://github.com/w4ngatang/qags">code</a>]</p>
<p><strong>jiant: A Software Toolkit for Research on General-Purpose Text Understanding Models</strong> <br />
Yada Pruksachatkun, Phil Yeres, Haokun Liu, Jason Phang, Phu Mon Htut, <strong>Alex Wang</strong>, Ian Tenney, Samuel R. Bowman <br />
ACL 2020 (demo) <br />
[<a href="https://arxiv.org/abs/2003.02249">paper</a>] [<a href="https://jiant.info/">site</a>]</p>
<p><strong>A Generalized Framework of Sequence Generation with Application to Undirected Sequence Models</strong> <br />
Elman Mansimov, <strong>Alex Wang</strong>, Sean Welleck, Kyunghyun Cho <br />
Northern European Journal of Language Technology <br />
[<a href="https://arxiv.org/abs/1905.12790">paper</a>] [<a href="https://github.com/nyu-dl/dl4mt-seqgen">code</a>]</p>
<p><strong>SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems</strong> <br />
<strong>Alex Wang</strong>, Yada Pruksachatkun, Nikita Nangia, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, Samuel R. Bowman <br />
NeurIPS 2019 <br />
[<a href="https://arxiv.org/abs/1905.00537">paper</a>] [<a href="super.gluebenchmark.com">site</a>]</p>
<p><strong>Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling</strong> <br />
<strong>Alex Wang</strong>, Jan Hula, Patrick Xia, Raghavendra Pappagari, R. Thomas McCoy, Roma Patel, Najoung Kim, Ian Tenney, Yinghui Huang, Katherin Yu, Shuning Jin, Berlin Chen, Benjamin Van Durme, Edouard Grave, Ellie Pavlick, Samuel R. Bowman <br />
ACL 2019 <br />
[<a href="https://arxiv.org/abs/1812.10860">paper</a>] [<a href="https://github.com/nyu-mll/jiant/releases/tag/r2">code</a>]</p>
<p><strong>Bert has a Mouth, and It Must Speak: BERT as a Markov Random Field Language Model</strong> <br />
<strong>Alex Wang</strong>, Kyunghyun Cho <br />
NeuralGen 2019 <br />
[<a href="https://arxiv.org/abs/1902.04094">paper</a>] [<a href="https://colab.research.google.com/drive/1MxKZGtQ9SSBjTK5ArsZ5LKhkztzg52RV">demo</a>]</p>
<p><strong>Probing What Different NLP Tasks Teach Machines about Function Word Comprehension</strong> <br />
Najoung Kim, Roma Patel, Adam Poliak, <strong>Alex Wang</strong>, Patrick Xia, R. Thomas McCoy, Ian Tenney, Alexis Ross, Tal Linzen, Benjamin Van Durme, Sameul R. Bowman, Ellie Pavlick <br />
StarSem 2019 (<strong>Best Paper Award</strong>) <br />
[<a href="https://www.aclweb.org/anthology/S19-1026">paper</a>] [<a href="https://github.com/nyu-mll/jiant">code</a>]</p>
<p><strong>On Measuring Social Biases in Sentence Encoders</strong> <br />
Chandler May, <strong>Alex Wang</strong>, Shikha Bordia, Samuel R. Bowman, Rachel Rudinger <br />
NAACL 2019 <br />
[<a href="https://arxiv.org/abs/1903.10561">paper</a>] [<a href="https://github.com/W4ngatang/sent-bias">code</a>]</p>
<p><strong>GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding</strong> <br />
<strong>Alex Wang</strong>, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, Samuel R. Bowman <br />
ICLR 2019 <br />
[<a href="https://arxiv.org/abs/1804.07461">paper</a>] [<a href="https://gluebenchmark.com/">site</a>]</p>
<p><strong>What do you learn from context? Probing for sentence structure in contextualized word representations</strong> <br />
Ian Tenney, Patrick Xia, Berlin Chen, <strong>Alex Wang</strong>, Adam Poliak, Benjamin Van Durme, Sam Bowman, Dipanjan Das, Ellie Pavlick <br />
ICLR 2019 <br />
[<a href="https://openreview.net/forum?id=SJzSgnRcKX">paper</a>] [<a href="https://github.com/nyu-mll/jiant/tree/master/probing">code</a>]</p>
<p><strong>Learning Linguistic Descriptors of User Roles in Online Communities</strong> <br />
<strong>Alex Wang</strong>, William L. Hamilton, Jure Leskovec <br />
NLP+CSS Workshop @ EMNLP 2016 <br />
[<a href="https://arxiv.org/abs/1804.07461">paper</a>]</p>
<h3>Other Projects</h3>
<ul>
 <li><a href="https://nyu-mll.github.io/nyu-ai-school-2021/">NYU AI School</a>: formerly NYC AI Workshop, the NYU AI School is a free introductory workshop on machine learning organized by students and faculty from the NYU ML<sup>2</sup> lab. The workshop is designed for early undergraduate students and features a week of lectures from experts in the field and hands-on coding experience. For more info see <a href="https://nyudatascience.medium.com/cds-members-organize-and-present-at-the-nyu-ai-school-1db8464bf504">here</a>.</li>
 <li><a href="https://github.com/W4ngatang/MatchingNets">A Neural Framework for One-Shot Learning</a>: thorough examination in the use of <a href="https://arxiv.org/abs/1606.04080">matching networks</a>, a neural network and nonparametric model hybrid, for one-shot learning in various settings. See <a href="/static/papers/thesis.pdf">paper</a> for more details. Completed for my senior thesis, earning high honors.</li>
 <li><a href="https://github.com/W4ngatang/TrafficSwarmOptimization">Traffic Swarm Optimization</a>: investigation in the use of swarm optimization methods for optimizing traffic light cycles. For more info see this <a href="https://www.seas.harvard.edu/news/2016/06/ants-go-marching-on-to-optimize-traffic-lights">article</a> or this <a href="/static/papers/swarm-optimization-traffic.pdf">writeup</a>.</li>
 <li><a href="https://github.com/kandluis/crime-prediction">Gaussian Processes for Crime Prediction</a>: an exploration into the use of Gaussian processes to predict future crime rates in cities. See the <a href="/static/papers/gaussian-processes-crime.pdf">writeup</a> for details.</li>
 <li><a href="https://github.com/mgentili/TwitterPlaysChess">Twitter Plays Chess</a>: crowdsourced chess playing against an AI where users vote for the human team’s next move via Twitter, à la Twitch Plays Pokemon.</li>
</ul>
<h3>Miscellaneous</h3>
<ul>
 <li><a href="https://vimeo.com/206087559">O Brother What Art Thou</a></li>
 <li><a href="http://harvardpolitics.com/united-states/watching-watchman/">Watching the Watchman</a></li>
</ul>
	 
 </div>||||I|||| Toggle navigation Alex Wang
 * Blog

 Alex Wang

 I work on machine learning at Cohere.


 I recently graduated with a PhD in computer science from New York University, focusing on machine learning and NLP. I was jointly advised by Professors Sam Bowman and Kyunghyun Cho, and was part of the Machine Learning for Language group at NYU. My dissertation is available here. Previously, I graduated from Harvard University with a bachelor’s in applied mathematics and a master’s in computer science, where I was advised by Alexander Rush and spent time with the Harvard Natural Language Processing group. See my CV, Google Scholar, GitHub for more details.


 If you need to contact me, send me a message at wangalexc at gmail or on Twitter. If you aren’t sure you have the right Alex Wang, try this directory.

 Papers

 What Do NLP Researchers Believe? Results of the NLP Community Metasurvey
 Julian Michael, Ari Holtzman, Alicia Parrish, Aaron Mueller, Alex Wang, Angelica Chen, Divyam Madaan, Nikita Nangia, Richard Yuanzhe Pang, Jason Phang, Samuel R. Bowman
 arXiv preprint
 [paper] [site]

 SQuALITY: Building a Long-Document Summarization Dataset the Hard Way
 Alex Wang, Richard Yuanzhe Pang, Angelica Chen, Jason Phang, Samuel R. Bowman
 arXiv preprint
 [paper] [code]

 QuestEval: Summarization Asks for Fact-Based Evaluation.
 Thomas Scialom, Paul-Alexis Dray, Patrick Gallinari, Sylvain Lamprier, Benjamin Piwowarski, Jacopo Staiano, Alex Wang
 EMNLP 2022
 [paper]

 Label Representations in Modeling Classification as Text Generation
 Xinyi Chen, Jiangxian Xu, Alex Wang
 AACL-IJCNLP SRW 2020 (Best Paper Award Runner-Up).
 [paper]

 Asking and Answering Questions to Evaluate the Factual Consistency of Summaries
 Alex Wang, Kyunghyun Cho, Mike Lewis
 ACL 2020
 [paper] [code]

 jiant: A Software Toolkit for Research on General-Purpose Text Understanding Models
 Yada Pruksachatkun, Phil Yeres, Haokun Liu, Jason Phang, Phu Mon Htut, Alex Wang, Ian Tenney, Samuel R. Bowman
 ACL 2020 (demo)
 [paper] [site]

 A Generalized Framework of Sequence Generation with Application to Undirected Sequence Models
 Elman Mansimov, Alex Wang, Sean Welleck, Kyunghyun Cho
 Northern European Journal of Language Technology
 [paper] [code]

 SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems
 Alex Wang, Yada Pruksachatkun, Nikita Nangia, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, Samuel R. Bowman
 NeurIPS 2019
 [paper] [site]

 Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling
 Alex Wang, Jan Hula, Patrick Xia, Raghavendra Pappagari, R. Thomas McCoy, Roma Patel, Najoung Kim, Ian Tenney, Yinghui Huang, Katherin Yu, Shuning Jin, Berlin Chen, Benjamin Van Durme, Edouard Grave, Ellie Pavlick, Samuel R. Bowman
 ACL 2019
 [paper] [code]

 Bert has a Mouth, and It Must Speak: BERT as a Markov Random Field Language Model
 Alex Wang, Kyunghyun Cho
 NeuralGen 2019
 [paper] [demo]

 Probing What Different NLP Tasks Teach Machines about Function Word Comprehension
 Najoung Kim, Roma Patel, Adam Poliak, Alex Wang, Patrick Xia, R. Thomas McCoy, Ian Tenney, Alexis Ross, Tal Linzen, Benjamin Van Durme, Sameul R. Bowman, Ellie Pavlick
 StarSem 2019 (Best Paper Award)
 [paper] [code]

 On Measuring Social Biases in Sentence Encoders
 Chandler May, Alex Wang, Shikha Bordia, Samuel R. Bowman, Rachel Rudinger
 NAACL 2019
 [paper] [code]

 GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
 Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, Samuel R. Bowman
 ICLR 2019
 [paper] [site]

 What do you learn from context? Probing for sentence structure in contextualized word representations
 Ian Tenney, Patrick Xia, Berlin Chen, Alex Wang, Adam Poliak, Benjamin Van Durme, Sam Bowman, Dipanjan Das, Ellie Pavlick
 ICLR 2019
 [paper] [code]

 Learning Linguistic Descriptors of User Roles in Online Communities
 Alex Wang, William L. Hamilton, Jure Leskovec
 NLP+CSS Workshop @ EMNLP 2016
 [paper]

 Other Projects

 * NYU AI School: formerly NYC AI Workshop, the NYU AI School is a free introductory workshop on machine learning organized by students and faculty from the NYU ML2 lab. The workshop is designed for early undergraduate students and features a week of lectures from experts in the field and hands-on coding experience. For more info see here.
 * A Neural Framework for One-Shot Learning: thorough examination in the use of matching networks, a neural network and nonparametric model hybrid, for one-shot learning in various settings. See paper for more details. Completed for my senior thesis, earning high honors.
 * Traffic Swarm Optimization: investigation in the use of swarm optimization methods for optimizing traffic light cycles. For more info see this article or this writeup.
 * Gaussian Processes for Crime Prediction: an exploration into the use of Gaussian processes to predict future crime rates in cities. See the writeup for details.
 * Twitter Plays Chess: crowdsourced chess playing against an AI where users vote for the human team’s next move via Twitter, à la Twitch Plays Pokemon.

 Miscellaneous

 * O Brother What Art Thou
 * Watching the Watchman
 * 
 * 
 * 
 * 

 Alex Wang • 2023

 Theme by beautiful-jekyll

I work on machine learning at Cohere.
 
If you need to contact me, send me a message at wangalexc at gmail or on Twitter.
If you aren’t sure you have the right Alex Wang, try this directory.
Papers
 What Do NLP Researchers Believe? Results of the NLP Community Metasurvey 
Julian Michael, Ari Holtzman, Alicia Parrish, Aaron Mueller, Alex Wang, Angelica Chen, Divyam Madaan, Nikita Nangia, Richard Yuanzhe Pang, Jason Phang, Samuel R. Bowman 
arXiv preprint 
[paper] [site]
 SQuALITY: Building a Long-Document Summarization Dataset the Hard Way 
Alex Wang, Richard Yuanzhe Pang, Angelica Chen, Jason Phang, Samuel R. Bowman 
arXiv preprint 
[paper] [code]
 QuestEval: Summarization Asks for Fact-Based Evaluation. 
Thomas Scialom, Paul-Alexis Dray, Patrick Gallinari, Sylvain Lamprier, Benjamin Piwowarski, Jacopo Staiano, Alex Wang 
EMNLP 2022 
[paper]
 Label Representations in Modeling Classification as Text Generation 
Xinyi Chen, Jiangxian Xu, Alex Wang 
AACL-IJCNLP SRW 2020 (Best Paper Award Runner-Up). 
[paper]
 Asking and Answering Questions to Evaluate the Factual Consistency of Summaries 
Alex Wang, Kyunghyun Cho, Mike Lewis 
ACL 2020 
[paper] [code]
 jiant: A Software Toolkit for Research on General-Purpose Text Understanding Models 
Yada Pruksachatkun, Phil Yeres, Haokun Liu, Jason Phang, Phu Mon Htut, Alex Wang, Ian Tenney, Samuel R. Bowman 
ACL 2020 (demo) 
[paper] [site]
 A Generalized Framework of Sequence Generation with Application to Undirected Sequence Models 
Elman Mansimov, Alex Wang, Sean Welleck, Kyunghyun Cho 
Northern European Journal of Language Technology 
[paper] [code]
 SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems 
Alex Wang, Yada Pruksachatkun, Nikita Nangia, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, Samuel R. Bowman 
NeurIPS 2019 
[paper] [site]
 Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling 
Alex Wang, Jan Hula, Patrick Xia, Raghavendra Pappagari, R. Thomas McCoy, Roma Patel, Najoung Kim, Ian Tenney, Yinghui Huang, Katherin Yu, Shuning Jin, Berlin Chen, Benjamin Van Durme, Edouard Grave, Ellie Pavlick, Samuel R. Bowman 
ACL 2019 
[paper] [code]
 Bert has a Mouth, and It Must Speak: BERT as a Markov Random Field Language Model 
Alex Wang, Kyunghyun Cho 
NeuralGen 2019 
[paper] [demo]
 Probing What Different NLP Tasks Teach Machines about Function Word Comprehension 
Najoung Kim, Roma Patel, Adam Poliak, Alex Wang, Patrick Xia, R. Thomas McCoy, Ian Tenney, Alexis Ross, Tal Linzen, Benjamin Van Durme, Sameul R. Bowman, Ellie Pavlick 
StarSem 2019 (Best Paper Award) 
[paper] [code]
 On Measuring Social Biases in Sentence Encoders 
Chandler May, Alex Wang, Shikha Bordia, Samuel R. Bowman, Rachel Rudinger 
NAACL 2019 
[paper] [code]
 GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding 
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, Samuel R. Bowman 
ICLR 2019 
[paper] [site]
 What do you learn from context? Probing for sentence structure in contextualized word representations 
Ian Tenney, Patrick Xia, Berlin Chen, Alex Wang, Adam Poliak, Benjamin Van Durme, Sam Bowman, Dipanjan Das, Ellie Pavlick 
ICLR 2019 
[paper] [code]
 Learning Linguistic Descriptors of User Roles in Online Communities 
Alex Wang, William L. Hamilton, Jure Leskovec 
NLP+CSS Workshop @ EMNLP 2016 
[paper]
Other Projects
  NYU AI School: formerly NYC AI Workshop, the NYU AI School is a free introductory workshop on machine learning organized by students and faculty from the NYU ML2 lab. The workshop is designed for early undergraduate students and features a week of lectures from experts in the field and hands-on coding experience. For more info see here.
  A Neural Framework for One-Shot Learning: thorough examination in the use of matching networks, a neural network and nonparametric model hybrid, for one-shot learning in various settings. See paper for more details. Completed for my senior thesis, earning high honors.
  Traffic Swarm Optimization: investigation in the use of swarm optimization methods for optimizing traffic light cycles. For more info see this article or this writeup.
  Gaussian Processes for Crime Prediction: an exploration into the use of Gaussian processes to predict future crime rates in cities. See the writeup for details.
  Twitter Plays Chess: crowdsourced chess playing against an AI where users vote for the human team’s next move via Twitter, à la Twitch Plays Pokemon.
Miscellaneous
  O Brother What Art Thou 
  Watching the Watchman
Alex Wang
https://w4ngatang.github.io/
https://angie-chen55.github.io/
Angelica Chen
2022-01-01
None
<div><div>
<header>
<p><a href="https://github.com/angie-chen55">View My GitHub Profile</a></p>
</header>
<section>
<h2>
 Angelica Chen
</h2>
<p>
 angelica[dot]chen[at]nyu.edu | <a href="https://www.semanticscholar.org/author/Angelica-Chen/13336152">Semantic Scholar profile</a>
</p>
<p>Hi! I’m a PhD student at NYU Center for Data Science in the <a href="https://wp.nyu.edu/ml2/">Machine Learning for Language</a> group, advised by <a href="https://kyunghyuncho.me/">Kyunghyun Cho</a>. I’m primarily interested in deep learning for natural language processing, especially <b>learning from rich feedback</b> (<em>e.g.</em> <a href="https://arxiv.org/abs/2303.16749">natural language</a> <a href="https://arxiv.org/abs/2204.14146">feedback</a> and <a href="https://arxiv.org/abs/2302.14838">interactions with the environment</a>) and <b><a href="https://arxiv.org/abs/2309.07311">understanding</a> + <a href="https://arxiv.org/abs/2110.08193">evaluating</a> how language models (LMs) learn</b>. Recently, I’ve also become interested in the use of <b>LLMs for biological applications</b>.</p>
<p>I have previously worked as a student researcher at Google Research (Jun-Dec. 2021) on <a href="https://arxiv.org/abs/2205.00620">streaming models for disfluency detection</a> and at Google Brain (Jun. 2022-Mar. 2023) on <a href="https://arxiv.org/abs/2302.14838">LMs for neural architecture search (NAS)</a>. Prior to NYU, I worked as a software engineer at Google and graduated with high honors from Princeton Computer Science. <a href="https://www.cs.princeton.edu/people/profile/sseung">Sebastian Seung</a> advised my senior thesis at Princeton, for which I received an Outstanding Computer Science Thesis award.</p>
<p>Outside of my research, I enjoy running, baking more pastries than I can feasibly eat, and cooking. I also volunteer as a rape and domestic violence crisis counselor/victim advocate for the <a href="https://www.cvtcnyc.org/">NYC Crime Victims Treatment Center</a> (at Lenox Health Greenwich Village and Brookdale Hospital) and a crisis counselor for <a href="https://www.crisistextline.org/">Crisis Text Line</a>.</p>
<h2>
 Selected Papers
</h2>
<div><p>For a more complete list, see my <a href="https://www.semanticscholar.org/author/Angelica-Chen/13336152">Semantic Scholar profile</a>. </p><p>
<b><u>Sudden Drops in the Loss: Syntax Acquisition, Phase Transitions, and Simplicity Bias in MLMs</u></b><br/>
<i>ICLR 2024 (Spotlight)</i><br/>
<b>Chen, Angelica</b>, Ravid Shwartz-Ziv, Kyunghyun Cho, Matthew L. Leavitt, Naomi Saphra.<br/>
[<a href="https://openreview.net/forum?id=MO5PiKHELW">OpenReview</a>] [<a href="https://arxiv.org/abs/2309.07311">Arxiv</a>] </p><p>
<b><u>Learning from Natural Language Feedback</u></b><br/>
<i>Transactions on Machine Learning Research</i><br/>
<b>Chen, Angelica<sup>*</sup></b>, Jérémy Scheurer<sup>*</sup>, Tomasz Korbak, Jon Ander Campos, Jun Shern Chan, Samuel R. Bowman, Kyunghyun Cho, Ethan Perez<br/>
[<a href="https://openreview.net/forum?id=xo3hI5MwvU">OpenReview</a>] [<a href="https://github.com/nyu-mll/ILF-for-code-generation">GitHub</a>]</p><p>
<b><u>EvoPrompting: Language Models for Code-Level Neural Architecture Search</u></b><br/>
<i>NeurIPS 2023 (poster)</i><br/>
<b>Chen, Angelica</b>, David M. Dohan and David R. So<br/>
[<a href="https://openreview.net/forum?id=ifbF4WdT8f">OpenReview</a>] [<a href="https://arxiv.org/abs/2302.14838">Arxiv</a>]</p><p>
<b><u>Latent State Models of Training Dynamics</u></b><br/>
<i>Transactions on Machine Learning Research</i><br/>
Michael Y. Hu, <b>Angelica Chen</b>, Naomi Saphra, Kyunghyun Cho<br/>
[<a href="https://arxiv.org/abs/2308.09543">Arxiv</a>] [<a href="https://openreview.net/forum?id=NE2xXWo0LF&amp;referrer=%5BAuthor%20Console%5D(%2Fgroup%3Fid%3DTMLR%2FAuthors%23your-submissions)">OpenReview</a>]</p><p>
<b><u>Two Failures of Self-Consistency in the Multi-Step Reasoning of LLMs</u></b><br/>
<i>Transactions on Machine Learning Research</i><br/>
<b>Chen, Angelica</b>, Jason Phang, Alicia Parrish, Vishakh Padmakumar, Chen Zhao, Samuel R. Bowman, Kyunghyun Cho.<br/> 
[<a href="https://arxiv.org/abs/2305.14279">Arxiv</a>] [<a href="https://openreview.net/forum?id=5nBqY1y96B">OpenReview</a>]</p><p>
<b><u>Pretraining Language Models with Human Preferences</u></b><br/>
<i>ICML 2023 (oral)</i><br/>
Korbak, Tomasz, Kejian Shi, <b>Angelica Chen</b>, Rasika Bhalerao, Christopher L. Buckley, Jason Phang, Sam Bowman and Ethan Perez<br/>
[<a href="https://arxiv.org/abs/2302.08582.pdf">Arxiv</a>]</p><p>
<b><u>Teaching BERT to Wait: Balancing Accuracy and Latency for Streaming Disfluency Detection</u></b><br/>
<i>NAACL 2022 (oral)</i><br/>
<b>Chen, Angelica</b>, Victoria Zayats, Daniel David Walker and Dirk Ryan Padfield<br/>
[<a href="https://www.aclanthology.org/2022.naacl-main.60.pdf">ACL Anthology</a>]</p><p>
<b><u>Training Language Models with Natural Language Feedback</u></b><br/>
<i>ACL 2022 Workshop on Learning with Natural Language Supervision</i><br/>
Scheurer, J’er’emy, Jon Ander Campos, Jun Shern Chan, <b>Angelica Chen</b>, Kyunghyun Cho and Ethan Perez<br/>
[<a href="https://arxiv.org/abs/2204.14146">Arxiv</a>]</p><p>
<b><u>BBQ: A hand-built bias benchmark for question answering</u></b><br/>
<i>ACL Findings 2022</i><br/>
Parrish, Alicia, <b>Angelica Chen</b>, Nikita Nangia, Vishakh Padmakumar, Jason Phang, Jana Thompson, Phu Mon Htut and Sam Bowman<br/>
[<a href="https://aclanthology.org/2022.findings-acl.165/">Arxiv</a>]</p><p>
<b><u>Seasonal dynamics of bacterial meningitis: a time-series analysis</u></b><br/>
<i>The Lancet Global Health</i><br/>
Paireau, Juliette<sup>*</sup>, <b>Angelica Chen<sup>*</sup></b>, Hélène Broutin, Bryan T. Grenfell and Nicole E. Basta.<br/>
[<a href="https://www.thelancet.com/journals/langlo/article/PIIS2214-109X(16)30064-X/fulltext">The Lancet</a>]</p></div>
</section>
</div></div>||||I|||| Angelica Chen

 View My GitHub Profile

 Angelica Chen

 angelica[dot]chen[at]nyu.edu | Semantic Scholar profile

 Hi! I’m a PhD student at NYU Center for Data Science in the Machine Learning for Language group, advised by Kyunghyun Cho. I’m primarily interested in deep learning for natural language processing, especially learning from rich feedback (e.g. natural language feedback and interactions with the environment) and understanding + evaluating how language models (LMs) learn. Recently, I’ve also become interested in the use of LLMs for biological applications.

 I have previously worked as a student researcher at Google Research (Jun-Dec. 2021) on streaming models for disfluency detection and at Google Brain (Jun. 2022-Mar. 2023) on LMs for neural architecture search (NAS). Prior to NYU, I worked as a software engineer at Google and graduated with high honors from Princeton Computer Science. Sebastian Seung advised my senior thesis at Princeton, for which I received an Outstanding Computer Science Thesis award.

 Outside of my research, I enjoy running, baking more pastries than I can feasibly eat, and cooking. I also volunteer as a rape and domestic violence crisis counselor/victim advocate for the NYC Crime Victims Treatment Center (at Lenox Health Greenwich Village and Brookdale Hospital) and a crisis counselor for Crisis Text Line.

 Selected Papers

 For a more complete list, see my Semantic Scholar profile.

 Sudden Drops in the Loss: Syntax Acquisition, Phase Transitions, and Simplicity Bias in MLMs
 ICLR 2024 (Spotlight)
 Chen, Angelica, Ravid Shwartz-Ziv, Kyunghyun Cho, Matthew L. Leavitt, Naomi Saphra.
 [OpenReview] [Arxiv]

 Learning from Natural Language Feedback
 Transactions on Machine Learning Research
 Chen, Angelica*, Jérémy Scheurer*, Tomasz Korbak, Jon Ander Campos, Jun Shern Chan, Samuel R. Bowman, Kyunghyun Cho, Ethan Perez
 [OpenReview] [GitHub]

 EvoPrompting: Language Models for Code-Level Neural Architecture Search
 NeurIPS 2023 (poster)
 Chen, Angelica, David M. Dohan and David R. So
 [OpenReview] [Arxiv]

 Latent State Models of Training Dynamics
 Transactions on Machine Learning Research
 Michael Y. Hu, Angelica Chen, Naomi Saphra, Kyunghyun Cho
 [Arxiv] [OpenReview]

 Two Failures of Self-Consistency in the Multi-Step Reasoning of LLMs
 Transactions on Machine Learning Research
 Chen, Angelica, Jason Phang, Alicia Parrish, Vishakh Padmakumar, Chen Zhao, Samuel R. Bowman, Kyunghyun Cho.
 [Arxiv] [OpenReview]

 Pretraining Language Models with Human Preferences
 ICML 2023 (oral)
 Korbak, Tomasz, Kejian Shi, Angelica Chen, Rasika Bhalerao, Christopher L. Buckley, Jason Phang, Sam Bowman and Ethan Perez
 [Arxiv]

 Teaching BERT to Wait: Balancing Accuracy and Latency for Streaming Disfluency Detection
 NAACL 2022 (oral)
 Chen, Angelica, Victoria Zayats, Daniel David Walker and Dirk Ryan Padfield
 [ACL Anthology]

 Training Language Models with Natural Language Feedback
 ACL 2022 Workshop on Learning with Natural Language Supervision
 Scheurer, J’er’emy, Jon Ander Campos, Jun Shern Chan, Angelica Chen, Kyunghyun Cho and Ethan Perez
 [Arxiv]

 BBQ: A hand-built bias benchmark for question answering
 ACL Findings 2022
 Parrish, Alicia, Angelica Chen, Nikita Nangia, Vishakh Padmakumar, Jason Phang, Jana Thompson, Phu Mon Htut and Sam Bowman
 [Arxiv]

 Seasonal dynamics of bacterial meningitis: a time-series analysis
 The Lancet Global Health
 Paireau, Juliette*, Angelica Chen*, Hélène Broutin, Bryan T. Grenfell and Nicole E. Basta.
 [The Lancet]


 Hosted on GitHub Pages — Theme by orderedlist

View My GitHub Profile 
 Angelica Chen
 angelica[dot]chen[at]nyu.edu | Semantic Scholar profile
Hi! I’m a PhD student at NYU Center for Data Science in the Machine Learning for Language group, advised by Kyunghyun Cho. I’m primarily interested in deep learning for natural language processing, especially learning from rich feedback (e.g. natural language feedback and interactions with the environment) and  understanding + evaluating how language models (LMs) learn. Recently, I’ve also become interested in the use of LLMs for biological applications.
I have previously worked as a student researcher at Google Research (Jun-Dec. 2021) on streaming models for disfluency detection and at Google Brain (Jun. 2022-Mar. 2023) on LMs for neural architecture search (NAS). Prior to NYU, I worked as a software engineer at Google and graduated with high honors from Princeton Computer Science. Sebastian Seung advised my senior thesis at Princeton, for which I received an Outstanding Computer Science Thesis award.
Outside of my research, I enjoy running, baking more pastries than I can feasibly eat, and cooking. I also volunteer as a rape and domestic violence crisis counselor/victim advocate for the NYC Crime Victims Treatment Center (at Lenox Health Greenwich Village and Brookdale Hospital) and a crisis counselor for Crisis Text Line.
 Selected Papers
 For a more complete list, see my Semantic Scholar profile.  
 Sudden Drops in the Loss: Syntax Acquisition, Phase Transitions, and Simplicity Bias in MLMs  
ICLR 2024 (Spotlight) 
Chen, Angelica, Ravid Shwartz-Ziv, Kyunghyun Cho, Matthew L. Leavitt, Naomi Saphra.
[OpenReview] [Arxiv]  
 Learning from Natural Language Feedback  
Transactions on Machine Learning Research 
Chen, Angelica* , Jérémy Scheurer*, Tomasz Korbak, Jon Ander Campos, Jun Shern Chan, Samuel R. Bowman, Kyunghyun Cho, Ethan Perez
[OpenReview] [GitHub] 
 EvoPrompting: Language Models for Code-Level Neural Architecture Search  
NeurIPS 2023 (poster) 
Chen, Angelica, David M. Dohan and David R. So
[OpenReview] [Arxiv] 
 Latent State Models of Training Dynamics  
Transactions on Machine Learning Research 
Michael Y. Hu, Angelica Chen, Naomi Saphra, Kyunghyun Cho
[Arxiv] [OpenReview] 
 Two Failures of Self-Consistency in the Multi-Step Reasoning of LLMs  
Transactions on Machine Learning Research 
Chen, Angelica, Jason Phang, Alicia Parrish, Vishakh Padmakumar, Chen Zhao, Samuel R. Bowman, Kyunghyun Cho. 
[Arxiv] [OpenReview] 
 Pretraining Language Models with Human Preferences  
ICML 2023 (oral) 
Korbak, Tomasz, Kejian Shi, Angelica Chen, Rasika Bhalerao, Christopher L. Buckley, Jason Phang, Sam Bowman and Ethan Perez
[Arxiv] 
 Teaching BERT to Wait: Balancing Accuracy and Latency for Streaming Disfluency Detection  
NAACL 2022 (oral) 
Chen, Angelica, Victoria Zayats, Daniel David Walker and Dirk Ryan Padfield
[ACL Anthology] 
 Training Language Models with Natural Language Feedback  
ACL 2022 Workshop on Learning with Natural Language Supervision 
Scheurer, J’er’emy, Jon Ander Campos, Jun Shern Chan, Angelica Chen, Kyunghyun Cho and Ethan Perez
[Arxiv] 
 BBQ: A hand-built bias benchmark for question answering  
ACL Findings 2022 
Parrish, Alicia, Angelica Chen, Nikita Nangia, Vishakh Padmakumar, Jason Phang, Jana Thompson, Phu Mon Htut and Sam Bowman
[Arxiv] 
 Seasonal dynamics of bacterial meningitis: a time-series analysis  
The Lancet Global Health 
Paireau, Juliette*, Angelica Chen* , Hélène Broutin, Bryan T. Grenfell and Nicole E. Basta.
[The Lancet]
Angelica Chen
https://angie-chen55.github.io/
https://lambdaviking.com/
Will Merrill
2022-04-15
Will Merrill
<div>
 
 
<nav>
 
</nav>
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 <div>
 
 <h2>About</h2>
 <p>I am interested in the formal theory of language and computation—spanning applications in NLP and linguistics. Much of my research has focused on analyzing how neural networks learn to represent the structure and meaning of natural language, and where they fall short. I believe that formal theory is an indispensible tool for truly understanding the capabilities of large-scale language models.</p>
<p>I am a Ph.D. student at the <a href="https://cds.nyu.edu/">CDS</a> at <a href="https://www.nyu.edu/">NYU</a>, where I am supported by an <a href="https://www.nsfgrfp.org/resources/about-grfp/">NSF graduate research fellowship</a>. I am thankful to have previously worked with <a href="https://homes.cs.washington.edu/~nasmith/">Noah Smith</a>, <a href="https://www.cs.bgu.ac.il/~yoavg/uni/">Yoav Goldberg</a>, and <a href="https://roys174.github.io//">Roy Schwartz</a> as a <a href="https://allenai.org/predoctoral-young-investigators">PYI</a> on the <a href="https://allennlp.org/">AllenNLP</a> team at <a href="https://allenai.org/">AI2</a>. As an undergrad at Yale, I was a member of <a href="http://clay.yale.edu/">CLAY</a> and wrote a <a href="/publication/sequential-neural-networks-as-automata/">thesis</a> with <a href="https://bobfrank1.github.io/">Bob Frank</a> and <a href="https://cpsc.yale.edu/people/dana-angluin">Dana Angluin</a>, both of whom were inspiring mentors.</p>
 <div>
 
 <div>
 <h3>Interests</h3>
 <ul>
 
 <li>Computational Linguistics</li>
 
 <li>Deep Learning</li>
 
 <li>Formal Languages</li>
 
 <li>Computational Complexity</li>
 
 </ul>
 </div>
 
 
 <div>
 <h3>Education</h3>
 <ul>
 
 <li>
 <i></i>
 <div>
 <p>Ph.D. at Center for Data Science, 2021-</p>
 <p>New York University</p>
 </div>
 </li>
 
 <li>
 <i></i>
 <div>
 <p>B.Sc. in Computer Science, 2019</p>
 <p>Yale University</p>
 </div>
 </li>
 
 <li>
 <i></i>
 <div>
 <p>B.A. in Linguistics, 2019</p>
 <p>Yale University</p>
 </div>
 </li>
 
 </ul>
 </div>
 
 </div>
 </div>
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 <div>
 <p>
 </p><h2>Recent Publications</h2>
 
 <p></p>
 
</div>
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 <div>
 <p>
 </p><h2>Recent Posts</h2>
 
 <p></p>
 
</div>
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
||||I|||| Search

 Will Merrill
 * About
 * Publications
 * Blog
 * Personal
 * 
 * 

 Will Merrill

 Ph.D. Student

 New York University

 * 
 * 
 * 

 About

 I am interested in the formal theory of language and computation—spanning applications in NLP and linguistics. Much of my research has focused on analyzing how neural networks learn to represent the structure and meaning of natural language, and where they fall short. I believe that formal theory is an indispensible tool for truly understanding the capabilities of large-scale language models.

 I am a Ph.D. student at the CDS at NYU, where I am supported by an NSF graduate research fellowship. I am thankful to have previously worked with Noah Smith, Yoav Goldberg, and Roy Schwartz as a PYI on the AllenNLP team at AI2. As an undergrad at Yale, I was a member of CLAY and wrote a thesis with Bob Frank and Dana Angluin, both of whom were inspiring mentors.

 Interests

 * Computational Linguistics
 * Deep Learning
 * Formal Languages
 * Computational Complexity

 Education

 * Ph.D. at Center for Data Science, 2021-

 New York University

 * B.Sc. in Computer Science, 2019

 Yale University

 * B.A. in Linguistics, 2019

 Yale University

 Recent Publications

 Saturated Transformers are Constant-Depth Threshold Circuits

 Saturated transformers can be simulated in TC0.
 Will Merrill, Ashish Sabharwal, Noah A. Smith
 PDF Cite

 Competency Problems: On Finding and Removing Artifacts in Language Data

 Finding and removing artifacts in NLP data.
 Matt Gardner, Will Merrill, Jesse Dodge, Matthew Peters, Alexis Ross, Sameer Singh, Noah A. Smith
 PDF Cite

 Effects of Parameter Norm Growth During Transformer Training: Inductive Bias from Gradient Descent

 T5 and other transformers become saturated because their parameters grow in norm over time.
 Will Merrill, William Merrill, Vivek Ramanujan, Yoav Goldberg, Roy Schwartz, Noah A. Smith
 PDF Cite Code

 Provable Limitations of Acquiring Meaning from Ungrounded Form: What Will Future Language Models Understand?

 Limits on ability of LMs to understand code.
 Will Merrill, Yoav Goldberg, Roy Schwartz, Noah A. Smith
 PDF Cite

 Formal Language Theory Meets Modern NLP

 Formal language theory tutorial for an NLP audience.
 Will Merrill
 PDF Cite

 A Formal Hierarchy of RNN Architectures

 We formally characterize RNN architectures in terms of their capacity.
 Will Merrill, Gail Weiss, Yoav Goldberg, Roy Schwartz, Noah A. Smith, Eran Yahav
 PDF Cite Code

 CORD-19: The COVID-19 Open Research Dataset

 I helped out a bit with this large collaboration between AI2 and other institutions.
 Lucy Lu Wang, Kyle Lo, Yoganand Chandrasekhar, Russell Reas, Jiangjiang Yang, Doug Burdick, Darrin Eide, Kathryn Funk, Yannis Katsis, Rodney Kinney, Yunyao Li, Ziyang Liu, Will Merrill, Paul Mooney, Dewey Murdick, Devvret Rishi, Jerry Sheehan, Zhihong Shen, Brandon Stilson, Alex Wade, Kuansan Wang, Nancy Xin Ru Wang, Chris Wilhelm, Boya Xie, Douglas Raymond, Daniel S. Weld, Oren Etzioni, Sebastian Kohlmeier
 PDF Cite Dataset

 Detecting Syntactic Change Using a Neural Part-of-Speech Tagger

 We find that a neural network part-of-speech tagger implicity learns to model syntactic change.
 Will Merrill, Gigi Felice Stark, Robert Frank
 PDF Cite Code

 Sequential Neural Networks as Automata

 I formally characterize the capacity and memory of various RNNs, given asymptotic assumptions.
 Will Merrill
 PDF Cite Code Slides Full Thesis

 Finding Syntactic Representations in Neural Stacks

 Stack-augmented RNNs that are trained to perform language modeling learn to exploit linguistic structure without any supervision.
 Will Merrill, Lenny Khazan, Noah Amsel, Yiding Hao, Simon Mendelsohn, Robert Frank
 PDF Cite Code

 Context-Free Transductions with Neural Stacks

 This paper analyzes the behavior of stack-augmented recurrent neural network models.
 Yiding Hao, Will Merrill, Dana Angluin, Robert Frank, Noah Amsel, Andrew Benz, Simon Mendelsohn
 PDF Cite Code

 End-to-End Graph-Based TAG Parsing with Neural Networks

 We present a graph-based Tree Adjoining Grammar parser that uses BiLSTMs, highway connections, and character-level CNNs.
 Jungo Kasai, Robert Frank, Pauli Xu, Will Merrill, Owen Rambow
 PDF Cite Code

 Sense Abstraction: A Unified Framework of Intensionality, Predicate Abstraction, and Alternative Semantics

 I propose a unified semantic treatment of several English subordinate clause types.
 Will Merrill
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 Recent Posts

 Improved Adversarial Robustness via Abstract Interpretation

 Final project for Foundations of Machine Learning at NYU.
 Will Merrill
 Apr 15, 2022 1 min read

 A Formal Hierarchy of RNN Architectures

 Summary of the ACL 2020 paper.
 Will Merrill
 Apr 16, 2020 4 min read NLP

 Theory of Saturated Neural Networks

 Summarizing Sequential Neural Networks as Automata.
 Will Merrill
 Oct 26, 2019 5 min read NLP

 The State of Interpretability in NLP

 Thoughts from my experience at ACL 2019.
 Will Merrill
 Aug 6, 2019 8 min read NLP

 The Wanderer

 Translation of the Old English poem The Wanderer.
 Will Merrill
 Jul 13, 2019 5 min read Translation , Old English
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About
 I am interested in the formal theory of language and computation—spanning applications in NLP and linguistics. Much of my research has focused on analyzing how neural networks learn to represent the structure and meaning of natural language, and where they fall short. I believe that formal theory is an indispensible tool for truly understanding the capabilities of large-scale language models.
I am a Ph.D. student at the CDS at NYU, where I am supported by an NSF graduate research fellowship. I am thankful to have previously worked with Noah Smith, Yoav Goldberg, and Roy Schwartz as a PYI on the AllenNLP team at AI2. As an undergrad at Yale, I was a member of CLAY and wrote a thesis with Bob Frank and Dana Angluin, both of whom were inspiring mentors.
 
 
 
 Interests
 
 
 Computational Linguistics
 
 Deep Learning
 
 Formal Languages
 
 Computational Complexity
 
 
 
 
 
 
 Education
 
 
 
  
 
 Ph.D. at Center for Data Science, 2021-
 New York University
 
 
 
 
  
 
 B.Sc. in Computer Science, 2019
 Yale University
 
 
 
 
  
 
 B.A. in Linguistics, 2019
 Yale University
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  Recent Publications
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  Recent Posts
Will Merrill
https://lambdaviking.com/
https://cs.stanford.edu/people/baxelrod/
Brian Axelrod
2019-01-01
Brian Axelrod
<div><div>
<p>
Brian Axelrod is a PhD student with the <a href="http://theory.stanford.edu/main/index.shtml">Stanford Theory Group</a> advised by <a href="http://theory.stanford.edu/~valiant/">Greg Valiant</a> and <a href="https://omereingold.wordpress.com">Omer Reingold</a>.</p>
<p>From 2016-2017 he was a MEng student with the Learning and Intelligent Systems Group (LIS) under Leslie Pack Kaelbling and Tomás Lozano-Pérez. His masters thesis can be found <a href="https://cs.stanford.edu/people/baxelrod/pdfs/baxelrod-meng-thesis.pdf">here</a></p>
<p>From 2013-2016 he was an undergraduate researcher with the Learning and Intelligent Systems Group while studying EECS (6-2) and math (18). He has interned at NASA Ames, Softigent, Willow Garage, NREC, Google Robotics and Microsoft Research.</p>
<p>He is also a Siebel Scholar (awarded 2016) and NSF Fellow (awarded 2017).</p>
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 Brian Axelrod

 Brian Axelrod is currently a CS PhD student in the Stanford Theory Group.

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 Brian Axelrod is a PhD student with the Stanford Theory Group advised by Greg Valiant and Omer Reingold.

 From 2016-2017 he was a MEng student with the Learning and Intelligent Systems Group (LIS) under Leslie Pack Kaelbling and Tomás Lozano-Pérez. His masters thesis can be found here

 From 2013-2016 he was an undergraduate researcher with the Learning and Intelligent Systems Group while studying EECS (6-2) and math (18). He has interned at NASA Ames, Softigent, Willow Garage, NREC, Google Robotics and Microsoft Research.

 He is also a Siebel Scholar (awarded 2016) and NSF Fellow (awarded 2017).

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 © 2019 Brian Axelrod. Powered by Jekyll & Minimal Mistakes.
Brian Axelrod is a PhD student with the Stanford Theory Group advised by Greg Valiant and Omer Reingold.
From 2016-2017 he was a MEng student with the Learning and Intelligent Systems Group (LIS) under Leslie Pack Kaelbling and Tomás Lozano-Pérez. His masters thesis can be found here 
From 2013-2016 he was an undergraduate researcher with the Learning and Intelligent Systems Group while studying EECS (6-2) and math (18). He has interned at NASA Ames, Softigent, Willow Garage, NREC, Google Robotics and Microsoft Research.
He is also a Siebel Scholar (awarded 2016) and NSF Fellow (awarded 2017).
Brian Axelrod
https://cs.stanford.edu/people/baxelrod/
http://web.stanford.edu/~jbrakens/
Joshua Brakensiek
2021-03-02
None
<div>
 
 
 <p>
 
 I am a PhD student in <a href="https://cs.stanford.edu/">Computer Science</a> at <a href="https://www.stanford.edu/">Stanford University.</a> I am fortunate to be co-advised by <a href="https://cs.stanford.edu/~aviad/">Aviad Rubinstein</a> and <a href="https://profiles.stanford.edu/moses-charikar">Moses Charikar</a>. My CV is available <a href="cv.pdf">here</a>.
 </p><p>
 
 
 I graduated from <a href="https://en.wikipedia.org/wiki/Carnegie_Mellon_University">Carnegie Mellon University</a> with a B.S. and M.S. in <a href="http://math.cmu.edu/index.php">Mathematical Sciences</a> with a minor in <a href="https://www.cmu.edu/dietrich/history/undergraduate/minors/science-technology.html">Science, Technology and Society</a> as a Knaster-McWilliams Scholar. My advisor in the <a href="https://www.math.cmu.edu/undergraduate/honors.html">Honors Program</a> was <a href="http://www.cs.cmu.edu/~venkatg/">Venkatesan Guruswami</a>.
 </p><h2>Research</h2>
 My primary research agenda is to tackle fundamental questions in theoretical computer science and adjacent areas, particularly questions inspired by modern challenges in computing. Towards this goal, I apply theoretical and computational techniques from many areas including combinatorics, algebra, convex optimization, and formal methods.
 <p>
 
 My most recent works with various collaborators include:
 </p><ul>
 <li> Formulating the theory of <i>higher order <a href="https://en.wikipedia.org/wiki/MDS_code#MDS_codes">MDS</a> codes</i> which unify a number of concepts in coding theory including connecting the recoverability of a real-world distributed storage architecture with fundamental questions in list-decoding.</li>
 <br />
 <li> Understanding the structure of <i><a href="https://en.wikipedia.org/wiki/Edit_distance">edit distance</a></i>, including finding improved approximation algorithms for computing edit distance and finding better error-correcting codes which can withstand edit distance errors. </li>
 <br />
 <li> Advancing the theory of <i>(promise) <a href="https://en.wikipedia.org/wiki/Constraint_satisfaction_problem">constraint satisfaction problems</a></i>, which has led to exciting connections between the theory of approximation algorithms and universal algebra.</li>
 <br />
 <li> Using SAT-solving/formal methods to resolve a 90-year-old question in mathematics (<a href="https://en.wikipedia.org/wiki/Keller's_conjecture">Keller's conjecture</a> in seven dimensions), as featured in <a href="https://www.quantamagazine.org/computer-search-settles-90-year-old-math-problem-20200819/">Quanta</a>, <a href="https://www.wired.com/story/a-fleet-of-computers-helps-settle-a-90-year-old-math-problem/">Wired</a>, and <a href="https://www.popularmechanics.com/science/math/a33820013/keller-conjecture-old-math-problem-solved/">Popular Mechanics</a>.</li>
 </ul>
 
 <br />
 <h3>Publications and Manuscripts</h3>
 <ul>
 <li>
	<b>Brakensiek, J.</b> and <a href="https://samidavies.com/">Davies, S.</a><a> Robust Factorizations and Colorings of Tensor Graphs. Manuscript. (</a><a href="https://arxiv.org/abs/2207.08913">arXiv</a>)
 </li>
 <br />
 
 <li>
	<b>Brakensiek, J.</b>, Gopi, S., and Makam, V. Generic Reed-Solomon Codes Achieve List-decoding Capacity. Manuscript. (<a href="https://arxiv.org/abs/2206.05256">arXiv</a>)
 </li>
 <br />
 
 <li>
	<b>Brakensiek, J.</b>, Gopi, S., and <a href="https://scholar.google.com/citations?user=OBwu3kEAAAAJ">Makam, V.</a> Lower Bounds for Maximally Recoverable Tensor Codes and Higher Order MDS Codes. IEEE Transactions on Information Theory, 2022. (<a href="https://arxiv.org/abs/2107.10822">arXiv</a>)
 </li>
 <br />
 
 <li><b>Brakensiek, J.</b>, <a href="https://www.cs.cmu.edu/~mheule/">Heule, M.</a>, <a href="https://www.cmu.edu/math/people/faculty/mackey.html">Mackey, J.</a>, and <a href="https://www.cs.rit.edu/~den9562/">NarvÃ¡ez, D.</a> The Resolution of Keller's Conjecture. <b>Special Issue</b>, Journal of Automated Reasoning, 2022. (Conference version: IJCAR 2020. <b>Best Paper Award.</b>) (<a href="https://arxiv.org/abs/1910.03740">arXiv</a>, <a href="https://www.cs.cmu.edu/~mheule/Keller/">website</a>) </li>
 <br />
 <li><b>Brakensiek, J.</b>, <a href="https://www.microsoft.com/en-us/research/people/sigopi/">Gopi, S.</a>, and Guruswami, V. Constraint Satisfaction Problems with Global Modular Constraints: Algorithms and Hardness via Polynomial Representations. SIAM Journal on Computing, 2022. (Conference version: STOC 2019.) (<a href="https://arxiv.org/abs/1902.04740">arXiv</a>, <a href="https://eccc.weizmann.ac.il/report/2019/013">ECCC</a>)</li>
 <br />
 <li><b>Brakensiek, J.</b> and Guruswami, V. The Quest for Strong Inapproximability Results with Perfect Completeness. ACM Transactions on Algorithms, 2021. (Conference version: APPROX 2017.) (<a href="https://eccc.weizmann.ac.il/report/2017/080/">ECCC</a>)</li>
 <br />
 <li><b>Brakensiek, J.</b> and Guruswami, V. Promise Constraint Satisfaction: Algebraic Structure and a Symmetric Boolean Dichotomy. SIAM Journal on Computing, 2021. (Conference version: SODA 2018.) (<a href="https://arxiv.org/abs/1704.01937">arXiv</a>)</li>
 <br />
 <li>
	<b>Brakensiek, J.</b>, Guruswami, V., and <a href="https://spallerl.github.io/">Sandeep, S.</a> Conditional Dichotomy of Boolean Ordered Promise CSPs. ICALP 2021. (<a href="https://arxiv.org/abs/2102.11854">arXiv</a>)
 </li>
 <br />
 <li>
	<b>Brakensiek, J.</b>, <a href="https://people.cs.uchicago.edu/~nenghuang/">Huang, N.</a>, Potechin, A., and <a href="https://www.math.tau.ac.il/~zwick/">Zwick, U.</a> On the Mysteries of MAX NAE-SAT. SODA 2021. (<a href="https://arxiv.org/abs/2009.10677">arXiv</a>)
 </li>
 <br />
 <li>
	<b>Brakensiek, J.</b>, Charikar, M., and Rubinstein, A. A Simple Sublinear Algorithm for Gap Edit Distance. Manuscript. (<a href="https://arxiv.org/abs/2007.14368">arXiv</a>)
 </li>
 <br />
 <li>
	<b>Brakensiek, J.</b>, Guruswami, V., <a href="https://mimuw.edu.pl/~mwrochna/">Wrochna, M.</a>, and <a href="https://www.cs.ox.ac.uk/standa.zivny/">Zivny, S.</a> The Power of the Combined Basic LP and Affine Relaxation for Promise CSPs. SIAM Journal on Computing, 2020. (Conference version: <b>Brakensiek, J.</b> and Guruswami, V. Symmetric Polymorphisms and Efficient Decidability of Promise CSPs. SODA 2020.) (<a href="http://arxiv.org/abs/1907.04383">arXiv</a>)</li>
 <br />
 <li> <a href="https://shant.boodaghians.com/">Boodaghians, S.</a>, <b>Brakensiek, J.</b>, <a href="https://www.samuelbhopkins.com/">Hopkins, S.</a>, and Rubinstein, A. Smoothed Complexity of 2-player Nash Equilibria. FOCS 2020. (<a href="https://arxiv.org/abs/2007.10857">arXiv</a>) </li>
 <br />
 <li><b>Brakensiek, J.</b>, <a href="http://theory.stanford.edu/~rayyli/">Li, R.</a>, and <a href="https://brucespang.com/">Spang, B.</a> Coded Trace Reconstruction in a Constant Number of Traces. FOCS 2020. (<a href="https://arxiv.org/abs/1908.03996">arXiv</a>)</li>
 
 <br />
 <li><b>Brakensiek, J.</b> and Rubinstein, A. Constant-factor Approximation of Near-linear Edit Distance in Near-linear Time. STOC 2020. (<a href="http://arxiv.org/abs/1904.05390">arXiv</a>)</li>
 <br />
 <li><b>Brakensiek, J.</b> and Guruswami, V. Bridging between 0/1 and Linear Programming via Random Walks. STOC 2019. (<a href="http://arxiv.org/abs/1904.04860">arXiv</a>)</li>
 <br />
 <li><b>Brakensiek, J.</b> and Guruswami, V. An Algorithmic Blend of LPs and Ring Equations for Promise CSPs. SODA 2019. (<a href="https://eccc.weizmann.ac.il/report/2018/059/">ECCC</a>)</li>
 <br />
 <li><b>Brakensiek, J.</b> and Guruswami, V. A Family of Dictatorship Tests with Perfect Completeness for 2âtoâ2 Label Cover. Manuscript. (<a href="https://eccc.weizmann.ac.il/report/2017/141/">ECCC</a>)</li>
 <br />
 <li><b>Brakensiek, J.</b>, Guruswami, V., and Zbarsky, S. Efficient Low-Redundancy Codes for Correcting Multiple Deletions. IEEE Transactions on Information Theory, 2017. (Conference version: SODA 2016.) (<a href="https://arxiv.org/abs/1507.06175">arXiv</a>)</li>
 <br />
 <li><b>Brakensiek, J.</b>
Vertex Isoperimetry and Independent Set Stability for Tensor Powers of Cliques. RANDOM 2017. (<a href="https://arxiv.org/abs/1702.04432">arXiv</a>)</li>
 <br />
 <li><b>Brakensiek, J.</b> and Guruswami, V. New Hardness Results for Graph and Hypergraph Colorings. CCC 2016. (<a href="https://eccc.weizmann.ac.il/report/2016/029">ECCC</a>)</li>
 <br />
 <li><b>Brakensiek, J</b> and <a href="https://www.physics.byu.edu/department/directory/ragozzine">Ragozzine, D.</a> Efficient Geometric Probabilities of
Multi-Transiting Exoplanetary Systems from CORBITS.
	The Astrophysical Journal, 2016, 821, 47. (<a href="https://arxiv.org/abs/1602.07014">arXiv</a>, <a href="https://github.com/jbrakensiek/CORBITS">code</a>)</li>
 <br />
 <li><b>Brakensiek, J.</b> and <a href="https://www.ias.edu/scholars/aaron-potechin">Potechin, A.</a> Bounds on the Size of Sound Monotone Switching Networks Accepting Permutation Sets of Directed Trees. Manuscript. (<a href="https://arxiv.org/abs/1301.3780">arXiv</a>)</li>
 </ul>
 
 <h2>Teaching</h2>
 <ul>
 <li> In Spring 2021, I was a Course Assistant for Stanford's CS 269I: Incentives in Computer Science. </li>
 
 <li> In Spring 2019, I was Course Assitant for Stanford's <a href="http://web.stanford.edu/class/cs354">CS 354: Unfulfilled Algorithmic Fantasies</a>.</li>
 <li>In Fall 2017, I was a Teaching Assistant for the CMU course <a href="http://brickisland.net/DDGFall2017/">15-458/858B: Discrete Differential Geometry</a> taught by Keenan Crane.</li>
 <li>
 I was a grader at the 2017 Math Olympiad Program directed by <a href="http://math.cmu.edu/~ploh/">Po-Shen Loh</a>. While there, I led a <a href="Teaching/MOP17_coding.pdf">problem session</a> on coding theory.</li>
 <li>
 From 2014-17, I have helped grade the Team Selection Tests which decide the USA team for the <a href="https://en.wikipedia.org/wiki/International_Mathematical_Olympiad">International Mathematical Olympiad</a>.</li>
 </ul>
 <h2>Professional Service</h2>
 <ul>
 <li>Subreviewer/reviewer for many journals and conferences including STOC/FOCS/SODA, the Journal of the ACM, the IEEE Transactions on Information Theory, and the SIAM Journal on Computing (see CV for full list).</li>
 <li>Webmaster of <a>theory.stanford.edu</a> (2019-present)</li>
 <li>Undergraduate representative in the <a href="http://www.cmu.edu/mcs/people/faculty/collegecouncil/">MCS College Council</a> (2017-18)</li>
 <li>Reviewer for Mathematical Reviews/Mathscinet</li>
 </ul>
 <h2>Selected Awards</h2>
 <ul>
 <li> 2021 <a href="https://www.microsoft.com/en-us/research/academic-program/phd-fellowship/#!fellows">Microsoft Research PhD Fellowship</a>
 </li><li> 2020 IJCAR Best Paper Award (co-winner)
 </li><li> 2018 <a href="https://www.nsfgrfp.org/">NSF Graduate Research Fellowship</a>
 </li><li>2018 <a href="https://cra.org/about/awards/outstanding-undergraduate-researcher-award/">Computing Research Association Outstanding Undergraduate Research Award</a>
 </li><li> 2017 Phi Beta Kappa
 </li><li>2016 <a href="https://www.maa.org/math-competitions/putnam-competition">Putnam Fellow</a> (top 5 out of over 4000 students) and <a href="https://www.cmu.edu/news/stories/archives/2017/april/putnam-mathematics-competition.html">first place team</a></li>
 <li>2016 <a href="http://www.cmu.edu/news/stories/archives/2016/april/goldwater-scholar.html">Goldwater Scholar</a></li>
 <li>2014 <a href="https://www.akamai.com/us/en/about/corporate-responsibility/akamai-foundation.jsp">Akamai Foundation</a> Scholarship</li>
 <li>2013 <a href="http://www.davidsongifted.org/Fellows-Scholarship">Davidson Fellow</a></li>
 </ul>
 In high school, I participated in various mathematics and computer science competitions.
 <ul>
 <li>2-time Gold medalist: 2013/2014 International Olympiad in Informatics</li>
 <li>Silver medalist: 2014 International Mathematical Olympiad</li>
 <li>Samuel L. Greitzer/Murray S. Klamkin Award for Mathematical Excellence: Sole perfect scorer on the 2014 USA Mathematical Olympiad</li>
 </ul>
 
 <h2>Other</h2>
 My PhD quals have been published as a pair of blog posts. (<a href="https://theorydish.blog/2021/03/02/automated-design-of-error-correcting-codes-part-1/">Part 1</a>, <a href="https://theorydish.blog/2021/03/09/automated-design-of-error-correcting-codes-part-2/">Part 2</a>)
 <p>
 
 At CMU, I was a member of <a href="http://www.cs.cmu.edu/~kmcrane/">Keenan Crane</a>'s <a href="http://geometry.cs.cmu.edu/">Geometry Collective</a>.
 </p><p>
 
 I am a class of 2012 alumnus of the <a href="https://en.wikipedia.org/wiki/Research_Science_Institute">Research Science Institute</a>.
 </p><p>
 
 The following photograph I took of <a href="http://www.cgc.maricopa.edu/Pages/Default.aspx">Chandler-Gilbert Community College</a> in Arizona appears in the <a href="https://www.maa.org/">Mathematical Association of America's</a> 100th anniversary calendar:</p>
 
||||I||||Joshua Brakensiek



I am a PhD student in Computer Science at Stanford University. I am fortunate to be co-advised by Aviad Rubinstein and Moses Charikar. My CV is available here.


I graduated from Carnegie Mellon University with a B.S. and M.S. in Mathematical Sciences with a minor in Science, Technology and Society as a Knaster-McWilliams Scholar. My advisor in the Honors Program was Venkatesan Guruswami.


Research

My primary research agenda is to tackle fundamental questions in theoretical computer science and adjacent areas, particularly questions inspired by modern challenges in computing. Towards this goal, I apply theoretical and computational techniques from many areas including combinatorics, algebra, convex optimization, and formal methods.

My most recent works with various collaborators include:
 * Formulating the theory of higher order MDS codes which unify a number of concepts in coding theory including connecting the recoverability of a real-world distributed storage architecture with fundamental questions in list-decoding.

 * Understanding the structure of edit distance, including finding improved approximation algorithms for computing edit distance and finding better error-correcting codes which can withstand edit distance errors.

 * Advancing the theory of (promise) constraint satisfaction problems, which has led to exciting connections between the theory of approximation algorithms and universal algebra.

 * Using SAT-solving/formal methods to resolve a 90-year-old question in mathematics (Keller's conjecture in seven dimensions), as featured in Quanta, Wired, and Popular Mechanics.


Publications and Manuscripts

 * Brakensiek, J. and Davies, S. Robust Factorizations and Colorings of Tensor Graphs. Manuscript. (arXiv)

 * Brakensiek, J., Gopi, S., and Makam, V. Generic Reed-Solomon Codes Achieve List-decoding Capacity. Manuscript. (arXiv)

 * Brakensiek, J., Gopi, S., and Makam, V. Lower Bounds for Maximally Recoverable Tensor Codes and Higher Order MDS Codes. IEEE Transactions on Information Theory, 2022. (arXiv)

 * Brakensiek, J., Heule, M., Mackey, J., and NarvÃ¡ez, D. The Resolution of Keller's Conjecture. Special Issue, Journal of Automated Reasoning, 2022. (Conference version: IJCAR 2020. Best Paper Award.) (arXiv, website)

 * Brakensiek, J., Gopi, S., and Guruswami, V. Constraint Satisfaction Problems with Global Modular Constraints: Algorithms and Hardness via Polynomial Representations. SIAM Journal on Computing, 2022. (Conference version: STOC 2019.) (arXiv, ECCC)

 * Brakensiek, J. and Guruswami, V. The Quest for Strong Inapproximability Results with Perfect Completeness. ACM Transactions on Algorithms, 2021. (Conference version: APPROX 2017.) (ECCC)

 * Brakensiek, J. and Guruswami, V. Promise Constraint Satisfaction: Algebraic Structure and a Symmetric Boolean Dichotomy. SIAM Journal on Computing, 2021. (Conference version: SODA 2018.) (arXiv)

 * Brakensiek, J., Guruswami, V., and Sandeep, S. Conditional Dichotomy of Boolean Ordered Promise CSPs. ICALP 2021. (arXiv)

 * Brakensiek, J., Huang, N., Potechin, A., and Zwick, U. On the Mysteries of MAX NAE-SAT. SODA 2021. (arXiv)

 * Brakensiek, J., Charikar, M., and Rubinstein, A. A Simple Sublinear Algorithm for Gap Edit Distance. Manuscript. (arXiv)

 * Brakensiek, J., Guruswami, V., Wrochna, M., and Zivny, S. The Power of the Combined Basic LP and Affine Relaxation for Promise CSPs. SIAM Journal on Computing, 2020. (Conference version: Brakensiek, J. and Guruswami, V. Symmetric Polymorphisms and Efficient Decidability of Promise CSPs. SODA 2020.) (arXiv)

 * Boodaghians, S., Brakensiek, J., Hopkins, S., and Rubinstein, A. Smoothed Complexity of 2-player Nash Equilibria. FOCS 2020. (arXiv)

 * Brakensiek, J., Li, R., and Spang, B. Coded Trace Reconstruction in a Constant Number of Traces. FOCS 2020. (arXiv)

 * Brakensiek, J. and Rubinstein, A. Constant-factor Approximation of Near-linear Edit Distance in Near-linear Time. STOC 2020. (arXiv)

 * Brakensiek, J. and Guruswami, V. Bridging between 0/1 and Linear Programming via Random Walks. STOC 2019. (arXiv)

 * Brakensiek, J. and Guruswami, V. An Algorithmic Blend of LPs and Ring Equations for Promise CSPs. SODA 2019. (ECCC)

 * Brakensiek, J. and Guruswami, V. A Family of Dictatorship Tests with Perfect Completeness for 2âtoâ2 Label Cover. Manuscript. (ECCC)

 * Brakensiek, J., Guruswami, V., and Zbarsky, S. Efficient Low-Redundancy Codes for Correcting Multiple Deletions. IEEE Transactions on Information Theory, 2017. (Conference version: SODA 2016.) (arXiv)

 * Brakensiek, J. Vertex Isoperimetry and Independent Set Stability for Tensor Powers of Cliques. RANDOM 2017. (arXiv)

 * Brakensiek, J. and Guruswami, V. New Hardness Results for Graph and Hypergraph Colorings. CCC 2016. (ECCC)

 * Brakensiek, J and Ragozzine, D. Efficient Geometric Probabilities of Multi-Transiting Exoplanetary Systems from CORBITS. The Astrophysical Journal, 2016, 821, 47. (arXiv, code)

 * Brakensiek, J. and Potechin, A. Bounds on the Size of Sound Monotone Switching Networks Accepting Permutation Sets of Directed Trees. Manuscript. (arXiv)

Teaching

 * In Spring 2021, I was a Course Assistant for Stanford's CS 269I: Incentives in Computer Science.
 * In Spring 2019, I was Course Assitant for Stanford's CS 354: Unfulfilled Algorithmic Fantasies.
 * In Fall 2017, I was a Teaching Assistant for the CMU course 15-458/858B: Discrete Differential Geometry taught by Keenan Crane.
 * I was a grader at the 2017 Math Olympiad Program directed by Po-Shen Loh. While there, I led a problem session on coding theory.
 * From 2014-17, I have helped grade the Team Selection Tests which decide the USA team for the International Mathematical Olympiad.

Professional Service

 * Subreviewer/reviewer for many journals and conferences including STOC/FOCS/SODA, the Journal of the ACM, the IEEE Transactions on Information Theory, and the SIAM Journal on Computing (see CV for full list).
 * Webmaster of theory.stanford.edu (2019-present)
 * Undergraduate representative in the MCS College Council (2017-18)
 * Reviewer for Mathematical Reviews/Mathscinet

Selected Awards

 * 2021 Microsoft Research PhD Fellowship
 * 2020 IJCAR Best Paper Award (co-winner)
 * 2018 NSF Graduate Research Fellowship
 * 2018 Computing Research Association Outstanding Undergraduate Research Award
 * 2017 Phi Beta Kappa
 * 2016 Putnam Fellow (top 5 out of over 4000 students) and first place team
 * 2016 Goldwater Scholar
 * 2014 Akamai Foundation Scholarship
 * 2013 Davidson Fellow
In high school, I participated in various mathematics and computer science competitions.
 * 2-time Gold medalist: 2013/2014 International Olympiad in Informatics
 * Silver medalist: 2014 International Mathematical Olympiad
 * Samuel L. Greitzer/Murray S. Klamkin Award for Mathematical Excellence: Sole perfect scorer on the 2014 USA Mathematical Olympiad

Other

My PhD quals have been published as a pair of blog posts. (Part 1, Part 2)

At CMU, I was a member of Keenan Crane's Geometry Collective.

I am a class of 2012 alumnus of the Research Science Institute.

The following photograph I took of Chandler-Gilbert Community College in Arizona appears in the Mathematical Association of America's 100th anniversary calendar:

I am a PhD student in Computer Science at Stanford University. I am fortunate to be co-advised by Aviad Rubinstein and Moses Charikar. My CV is available here.
  
 
 
 I graduated from Carnegie Mellon University with a B.S. and M.S. in Mathematical Sciences with a minor in Science, Technology and Society as a Knaster-McWilliams Scholar. My advisor in the Honors Program was Venkatesan Guruswami.
  Research
 My primary research agenda is to tackle fundamental questions in theoretical computer science and adjacent areas, particularly questions inspired by modern challenges in computing. Towards this goal, I apply theoretical and computational techniques from many areas including combinatorics, algebra, convex optimization, and formal methods.
 
 
 My most recent works with various collaborators include:
  
  Formulating the theory of higher order MDS codes which unify a number of concepts in coding theory including connecting the recoverability of a real-world distributed storage architecture with fundamental questions in list-decoding.
 
  Understanding the structure of  edit distance , including finding improved approximation algorithms for computing edit distance and finding better error-correcting codes which can withstand edit distance errors. 
 
  Advancing the theory of (promise) constraint satisfaction problems , which has led to exciting connections between the theory of approximation algorithms and universal algebra.
 
  Using SAT-solving/formal methods to resolve a 90-year-old question in mathematics (Keller's conjecture in seven dimensions), as featured in Quanta, Wired, and Popular Mechanics.
 
 
 
 Publications and Manuscripts
 
 
	Brakensiek, J. and Davies, S.  Robust Factorizations and Colorings of Tensor Graphs. Manuscript. ( arXiv)
 
 
 
 
	Brakensiek, J., Gopi, S., and Makam, V. Generic Reed-Solomon Codes Achieve List-decoding Capacity. Manuscript. (arXiv)
 
 
 
 
	Brakensiek, J., Gopi, S., and Makam, V. Lower Bounds for Maximally Recoverable Tensor Codes and Higher Order MDS Codes. IEEE Transactions on Information Theory, 2022. (arXiv)
 
 
 
  Brakensiek, J., Heule, M., Mackey, J., and NarvÃ¡ez, D. The Resolution of Keller's Conjecture. Special Issue, Journal of Automated Reasoning, 2022. (Conference version: IJCAR 2020. Best Paper Award.) (arXiv, website) 
 
  Brakensiek, J., Gopi, S., and Guruswami, V. Constraint Satisfaction Problems with Global Modular Constraints: Algorithms and Hardness via Polynomial Representations. SIAM Journal on Computing, 2022. (Conference version: STOC 2019.) (arXiv, ECCC)
 
  Brakensiek, J. and Guruswami, V. The Quest for Strong Inapproximability Results with Perfect Completeness. ACM Transactions on Algorithms, 2021. (Conference version: APPROX 2017.) (ECCC)
 
  Brakensiek, J. and Guruswami, V. Promise Constraint Satisfaction: Algebraic Structure and a Symmetric Boolean Dichotomy. SIAM Journal on Computing, 2021. (Conference version: SODA 2018.) (arXiv)
 
 
	Brakensiek, J., Guruswami, V., and Sandeep, S. Conditional Dichotomy of Boolean Ordered Promise CSPs. ICALP 2021. (arXiv)
 
 
 
	Brakensiek, J., Huang, N., Potechin, A., and Zwick, U. On the Mysteries of MAX NAE-SAT. SODA 2021. (arXiv)
 
 
 
	Brakensiek, J., Charikar, M., and Rubinstein, A. A Simple Sublinear Algorithm for Gap Edit Distance. Manuscript. (arXiv)
 
 
 
	Brakensiek, J., Guruswami, V., Wrochna, M., and Zivny, S. The Power of the Combined Basic LP and Affine Relaxation for Promise CSPs. SIAM Journal on Computing, 2020. (Conference version: Brakensiek, J. and Guruswami, V. Symmetric Polymorphisms and Efficient Decidability of Promise CSPs. SODA 2020.) (arXiv)
 
  Boodaghians, S., Brakensiek, J., Hopkins, S., and Rubinstein, A. Smoothed Complexity of 2-player Nash Equilibria. FOCS 2020. (arXiv) 
 
  Brakensiek, J., Li, R., and Spang, B. Coded Trace Reconstruction in a Constant Number of Traces. FOCS 2020. (arXiv)
 
 
  Brakensiek, J. and Rubinstein, A. Constant-factor Approximation of Near-linear Edit Distance in Near-linear Time. STOC 2020. (arXiv)
 
  Brakensiek, J. and Guruswami, V. Bridging between 0/1 and Linear Programming via Random Walks. STOC 2019. (arXiv)
 
  Brakensiek, J. and Guruswami, V. An Algorithmic Blend of LPs and Ring Equations for Promise CSPs. SODA 2019. (ECCC)
 
  Brakensiek, J. and Guruswami, V. A Family of Dictatorship Tests with Perfect Completeness for 2âtoâ2 Label Cover. Manuscript. (ECCC)
 
  Brakensiek, J., Guruswami, V., and Zbarsky, S. Efficient Low-Redundancy Codes for Correcting Multiple Deletions. IEEE Transactions on Information Theory, 2017. (Conference version: SODA 2016.) (arXiv)
 
  Brakensiek, J.
Vertex Isoperimetry and Independent Set Stability for Tensor Powers of Cliques. RANDOM 2017. (arXiv)
 
  Brakensiek, J. and Guruswami, V. New Hardness Results for Graph and Hypergraph Colorings. CCC 2016. (ECCC)
 
  Brakensiek, J and Ragozzine, D. Efficient Geometric Probabilities of
Multi-Transiting Exoplanetary Systems from CORBITS.
	The Astrophysical Journal, 2016, 821, 47. (arXiv, code)
 
  Brakensiek, J. and Potechin, A. Bounds on the Size of Sound Monotone Switching Networks Accepting Permutation Sets of Directed Trees. Manuscript. (arXiv)
 
 
 Teaching
 
  In Spring 2021, I was a Course Assistant for Stanford's CS 269I: Incentives in Computer Science. 
 
  In Spring 2019, I was Course Assitant for Stanford's CS 354: Unfulfilled Algorithmic Fantasies.
 In Fall 2017, I was a Teaching Assistant for the CMU course 15-458/858B: Discrete Differential Geometry taught by Keenan Crane.
 
 I was a grader at the 2017 Math Olympiad Program directed by Po-Shen Loh. While there, I led a problem session on coding theory.
 
 From 2014-17, I have helped grade the Team Selection Tests which decide the USA team for the International Mathematical Olympiad.
 
 Professional Service
 
 Subreviewer/reviewer for many journals and conferences including STOC/FOCS/SODA, the Journal of the ACM, the IEEE Transactions on Information Theory, and the SIAM Journal on Computing (see CV for full list).
 Webmaster of theory.stanford.edu (2019-present)
 Undergraduate representative in the MCS College Council (2017-18)
 Reviewer for Mathematical Reviews/Mathscinet
 
 Selected Awards
 
  2021 Microsoft Research PhD Fellowship
   2020 IJCAR Best Paper Award (co-winner)
   2018 NSF Graduate Research Fellowship
  2018 Computing Research Association Outstanding Undergraduate Research Award
   2017 Phi Beta Kappa
  2016 Putnam Fellow (top 5 out of over 4000 students) and first place team 
 2016 Goldwater Scholar 
 2014 Akamai Foundation Scholarship
 2013 Davidson Fellow 
 
 In high school, I participated in various mathematics and computer science competitions.
 
 2-time Gold medalist: 2013/2014 International Olympiad in Informatics
 Silver medalist: 2014 International Mathematical Olympiad
 Samuel L. Greitzer/Murray S. Klamkin Award for Mathematical Excellence: Sole perfect scorer on the 2014 USA Mathematical Olympiad
 
 
 Other
 My PhD quals have been published as a pair of blog posts. (Part 1, Part 2)
 
 
 At CMU, I was a member of Keenan Crane's Geometry Collective.
  
 
 I am a class of 2012 alumnus of the Research Science Institute.
  
 
 The following photograph I took of Chandler-Gilbert Community College in Arizona appears in the Mathematical Association of America's 100th anniversary calendar:
Joshua Brakensiek
http://web.stanford.edu/~jbrakens/
https://kyunghyuncho.me/
About me
2011-01-01
None
<div><div><main><article><div><p><strong>Kyunghyun Cho</strong></p><p>Associate Professor at <a href="https://cilvr.nyu.edu/">CILVR Group</a><br /> <a href="https://cs.nyu.edu/">Computer Science</a> (<a href="https://cims.nyu.edu/">Courant Institute</a>) and <a href="https://cds.nyu.edu/">Center for Data Science</a><br /> Interim Deputy Director at the <a href="https://cds.nyu.edu/">Center for Data Science</a><br /> <a href="https://www.nyu.edu/">New York University</a></p><p>Co-Founder &amp; Senior Director of Frontier Research<br /> <a href="https://www.prescient.design/">Prescient Design</a> in <a href="https://www.gene.com/">Genentech</a> <a href="https://www.gene.com/careers/professional-areas/research-early-development">Research &amp; Early Development (gRED)</a></p><p>CIFAR Fellow of <a href="https://www.cifar.ca/research/program/learning-in-machines-brains">Learning in Machines &amp; Brains</a><br /> (former <a href="https://www.cifar.ca/next-gen/global-scholars">CIFAR Azrieli Global Scholar</a>)</p><p>Associate Member of the <a href="https://www.naek.or.kr/home_eng/">National Academy of Engineering of Korea</a></p><p>Twitter: <a href="https://twitter.com/kchonyc">@kchonyc</a></p><p><strong>Bio</strong>: Kyunghyun Cho <strong>is</strong> an associate professor of computer science and data science at New York University and CIFAR Fellow of Learning in Machines &amp; Brains. He is also a senior director of frontier research at the Prescient Design team within Genentech Research &amp; Early Development (gRED). He was a research scientist at Facebook AI Research from June 2017 to May 2020 and a postdoctoral fellow at University of Montreal until Summer 2015 under the supervision of Prof. <a href="https://yoshuabengio.org/">Yoshua Bengio</a>, after receiving MSc and PhD degrees from <a href="https://www.aalto.fi/en/department-of-computer-science">Aalto University</a> April 2011 and April 2014, respectively, under the supervision of Prof. <a href="https://users.ics.aalto.fi/juha/">Juha Karhunen</a>, Dr. <a href="https://www.linkedin.com/in/tapaniraiko/?originalSubdomain=ch">Tapani Raiko</a> and Dr. <a href="https://users.aalto.fi/~alexilin/">Alexander Ilin</a>. He received the Samsung Ho-Am Prize in Engineering in 2021. He <strong>tries</strong> his best to find a balance among machine learning, natural language processing, and life, but almost always fails to do so.</p><h2>Academic Background</h2><ul><li>D.Sc., <a href="https://www.aalto.fi/en">Aalto University</a> <a href="https://www.aalto.fi/en/school-of-science">School of Science</a>, 2014<ul><li>Thesis: <a href="https://aaltodoc.aalto.fi/handle/123456789/12729">Foundations and Advances in Deep Learning</a></li></ul></li><li>M.Sc. with distinction, Aalto University School of Science, 2011<ul><li>Programme: <a href="https://drive.google.com/file/d/1pyiP1ESB_nMVt3u-ZSeS6DllI0c1rg1Q/view?usp=sharing">Master’s Programme in Machine Learning and Data Mining (MACADAMIA)</a></li><li>Thesis: <a href="https://aaltodoc.aalto.fi/handle/123456789/3666">Improved Learning Algorithms for Restricted Boltzmann Machines</a></li></ul></li><li>B.Sc., <a href="https://www.kaist.ac.kr/en/">Korea Advanced Institute of Science and Technology</a>, 2009<ul><li>Major: <a href="https://cs.kaist.ac.kr/">Computer Science</a></li></ul></li></ul></div></article></main></div>||||I|||| Skip to content

 Kyunghyun Cho

 About me

 Kyunghyun Cho

 Associate Professor at CILVR Group
 Computer Science (Courant Institute) and Center for Data Science
 Interim Deputy Director at the Center for Data Science
 New York University

 Co-Founder & Senior Director of Frontier Research
 Prescient Design in Genentech Research & Early Development (gRED)

 CIFAR Fellow of Learning in Machines & Brains
 (former CIFAR Azrieli Global Scholar)

 Associate Member of the National Academy of Engineering of Korea

 Twitter: @kchonyc

 Bio: Kyunghyun Cho is an associate professor of computer science and data science at New York University and CIFAR Fellow of Learning in Machines & Brains. He is also a senior director of frontier research at the Prescient Design team within Genentech Research & Early Development (gRED). He was a research scientist at Facebook AI Research from June 2017 to May 2020 and a postdoctoral fellow at University of Montreal until Summer 2015 under the supervision of Prof. Yoshua Bengio, after receiving MSc and PhD degrees from Aalto University April 2011 and April 2014, respectively, under the supervision of Prof. Juha Karhunen, Dr. Tapani Raiko and Dr. Alexander Ilin. He received the Samsung Ho-Am Prize in Engineering in 2021. He tries his best to find a balance among machine learning, natural language processing, and life, but almost always fails to do so.

 Academic Background

 * D.Sc., Aalto University School of Science, 2014
 + Thesis: Foundations and Advances in Deep Learning
 * M.Sc. with distinction, Aalto University School of Science, 2011
 + Programme: Master’s Programme in Machine Learning and Data Mining (MACADAMIA)
 + Thesis: Improved Learning Algorithms for Restricted Boltzmann Machines
 * B.Sc., Korea Advanced Institute of Science and Technology, 2009
 + Major: Computer Science
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Kyunghyun Cho  Associate Professor at CILVR Group  Computer Science (Courant Institute) and Center for Data Science  Interim Deputy Director at the Center for Data Science  New York University  Co-Founder &amp; Senior Director of Frontier Research Prescient Design in Genentech Research &amp; Early Development (gRED)  CIFAR Fellow of Learning in Machines &amp; Brains  (former CIFAR Azrieli Global Scholar) Associate Member of the National Academy of Engineering of Korea  Twitter: @kchonyc   Bio: Kyunghyun Cho is an associate professor of computer science and data science at New York University and CIFAR Fellow of Learning in Machines &amp; Brains. He is also a senior director of frontier research at the Prescient Design team within Genentech Research &amp; Early Development (gRED). He was a research scientist at Facebook AI Research from June 2017 to May 2020 and a postdoctoral fellow at University of Montreal until Summer 2015 under the supervision of Prof. Yoshua Bengio, after receiving MSc and PhD degrees from Aalto University April 2011 and April 2014, respectively, under the supervision of Prof. Juha Karhunen, Dr. Tapani Raiko and Dr. Alexander Ilin. He received the Samsung Ho-Am Prize in Engineering in 2021. He tries his best to find a balance among machine learning, natural language processing, and life, but almost always fails to do so. Academic Background  D.Sc., Aalto University School of Science, 2014 Thesis: Foundations and Advances in Deep Learning    M.Sc. with distinction, Aalto University School of Science, 2011 Programme: Master’s Programme in Machine Learning and Data Mining (MACADAMIA)  Thesis: Improved Learning Algorithms for Restricted Boltzmann Machines    B.Sc., Korea Advanced Institute of Science and Technology, 2009 Major: Computer Science
About me
https://kyunghyuncho.me/
https://cims.nyu.edu/~sbowman/
Sam Bowman
2016-01-01
None
<div><div>
 
<p>Basics</p>
<p>Interests</p>
<p>
I study artificial neural network models for natural language understanding, with a focus on language model alignment, building high-quality training and evaluation data, and applying neural network models to scientific questions in syntax and semantics.
</p><p>
I think you should join <a href="https://www.givingwhatwecan.org/">Giving What We Can</a>.</p>
<p>Impact</p>
<p>
You're most likely to have encountered my group by way of our <a href="https://nlp.stanford.edu/projects/snli/">SNLI</a>, <a href="https://cims.nyu.edu/~sbowman/multinli/">MultiNLI</a> datasets, our <a href="https://gluebenchmark.com/">GLUE and </a><a href="https://super.gluebenchmark.com/">SuperGLUE</a> benchmark competitions, our <a href="https://jiant.info/">jiant</a> software toolkit, or our <a href="pubs.shtml">papers</a> on topics like the inductive biases of language models or the viability of transfer learning for NLP.</p>
<p>Contact</p>
<p>
If we haven't been in contact previously, please look through this <a href="faq.shtml">FAQ</a> before emailing me. You can reach me at <a href="mailto:bowman@nyu.edu">bowman@nyu.edu</a>.
</p>
<p>News</p>
<ul>
<li>I'm organizing a new <a href="https://wp.nyu.edu/arg/">AI safety research group</a> at NYU, and I wrote up a <a href="https://wp.nyu.edu/arg/why-ai-safety/">blog post</a> explaining what we're up to and why.</li>
<li>I got tenure, and am an associate professor as of fall! Thanks to the many, many people who made this possible.</li>
<li>I have new major funding for work on language model alignment! Watch this space.</li>
<li>I'm excited about our large specialist-written <a href="https://github.com/nyu-mll/quality">QuALITY</a> (multiple-choice) and <a href="https://github.com/nyu-mll/SQuALITY">SQuALITY</a> (long-text answer) benchmarks for machine reading comprehension over long input texts. Both are available now, and QuALITY will be presented at NAACL.</li>
<li>I'll be on leave and away from NYU for a sabbatical year from Summer 2022 through Summer 2023 while taking on a visiting researcher role at <a href="https://www.anthropic.com/">Anthropic</a> in San Francisco. I'll still be advising researchers at NYU, but my availability for other service will be very limited.</li>
<li>I have new funding for data collection/crowdsourcing work from the NSF CAREER program and from Google's Collabs program. Thanks to everyone who reviewed the proposals!</li>
 
<li>I joined the <a href="http://cs.nyu.edu/">Department of Computer Science at NYU's Courant Institute</a> as an associated faculty member, in addition to my primary appointment in Linguistics and Data Science.</li>
<li>I'm a co-founder and co-director of NYU's new Data Science for Social Good program, a 12-week research training program for graduate students and postdocs. Applications for summer 2021 are now open!</li>
<li>My group's software toolkit for end-to-end training and evaluation of NLP models, <a href="https://jiant.info/">jiant</a>, was selected as one of the top 10 systems in the 2020 <a href="https://repeval2019.github.io/">RepEval</a> Shared Task on Replicable Evaluation in NLP.</li>
<li>Those last two news items aren't real. They were generated by <a href="https://openai.com/blog/openai-api/">GPT-3</a>, conditioning on the rest of this page.</li>
</ul>
</div>||||I||||Sam Bowman

 Home

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 CV (PDF)

 FAQ

 People (ML²)

 Follow @sleepinyourhat

 NYU Web Accessibility Feedback Form

 Basics

 Visiting Researcher (Sabbatical Year 2022–2023)
 Anthropic

 Associate Professor of Linguistics, Data Science & Computer Science
 Co-PI, Alignment Research Group, ML² Group & CILVR Lab
 New York University

 PhD 2016, Stanford NLP Group & Stanford Linguistics

 Interests

 I study artificial neural network models for natural language understanding, with a focus on language model alignment, building high-quality training and evaluation data, and applying neural network models to scientific questions in syntax and semantics.

 I think you should join Giving What We Can.

 Impact

 You're most likely to have encountered my group by way of our SNLI, MultiNLI datasets, our GLUE and SuperGLUE benchmark competitions, our jiant software toolkit, or our papers on topics like the inductive biases of language models or the viability of transfer learning for NLP.

 Contact

 If we haven't been in contact previously, please look through this FAQ before emailing me. You can reach me at bowman@nyu.edu.

 News

 * I'm organizing a new AI safety research group at NYU, and I wrote up a blog post explaining what we're up to and why.
 * I got tenure, and am an associate professor as of fall! Thanks to the many, many people who made this possible.
 * I have new major funding for work on language model alignment! Watch this space.
 * I'm excited about our large specialist-written QuALITY (multiple-choice) and SQuALITY (long-text answer) benchmarks for machine reading comprehension over long input texts. Both are available now, and QuALITY will be presented at NAACL.
 * I'll be on leave and away from NYU for a sabbatical year from Summer 2022 through Summer 2023 while taking on a visiting researcher role at Anthropic in San Francisco. I'll still be advising researchers at NYU, but my availability for other service will be very limited.
 * I have new funding for data collection/crowdsourcing work from the NSF CAREER program and from Google's Collabs program. Thanks to everyone who reviewed the proposals!
 * I joined the Department of Computer Science at NYU's Courant Institute as an associated faculty member, in addition to my primary appointment in Linguistics and Data Science.
 * I'm a co-founder and co-director of NYU's new Data Science for Social Good program, a 12-week research training program for graduate students and postdocs. Applications for summer 2021 are now open!
 * My group's software toolkit for end-to-end training and evaluation of NLP models, jiant, was selected as one of the top 10 systems in the 2020 RepEval Shared Task on Replicable Evaluation in NLP.
 * Those last two news items aren't real. They were generated by GPT-3, conditioning on the rest of this page.
Basics
Interests
I study artificial neural network models for natural language understanding, with a focus on language model alignment, building high-quality training and evaluation data, and applying neural network models to scientific questions in syntax and semantics.
 
I think you should join Giving What We Can.
Impact
You're most likely to have encountered my group by way of our SNLI, MultiNLI datasets, our GLUE and  SuperGLUE benchmark competitions, our jiant software toolkit, or our papers on topics like the inductive biases of language models or the viability of transfer learning for NLP.
Contact
If we haven't been in contact previously, please look through this FAQ before emailing me. You can reach me at bowman@nyu.edu.
News
I'm organizing a new AI safety research group at NYU, and I wrote up a blog post explaining what we're up to and why.
I got tenure, and am an associate professor as of fall! Thanks to the many, many people who made this possible.
I have new major funding for work on language model alignment! Watch this space.
I'm excited about our large specialist-written QuALITY (multiple-choice) and SQuALITY (long-text answer) benchmarks for machine reading comprehension over long input texts. Both are available now, and QuALITY will be presented at NAACL.
I'll be on leave and away from NYU for a sabbatical year from Summer 2022 through Summer 2023 while taking on a visiting researcher role at Anthropic in San Francisco. I'll still be advising researchers at NYU, but my availability for other service will be very limited.
I have new funding for data collection/crowdsourcing work from the NSF CAREER program and from Google's Collabs program. Thanks to everyone who reviewed the proposals!
 
I joined the Department of Computer Science at NYU's Courant Institute as an associated faculty member, in addition to my primary appointment in Linguistics and Data Science.
I'm a co-founder and co-director of NYU's new Data Science for Social Good program, a 12-week research training program for graduate students and postdocs. Applications for summer 2021 are now open!
My group's software toolkit for end-to-end training and evaluation of NLP models, jiant, was selected as one of the top 10 systems in the 2020 RepEval Shared Task on Replicable Evaluation in NLP.
Those last two news items aren't real. They were generated by GPT-3, conditioning on the rest of this page.
Sam Bowman
https://cims.nyu.edu/~sbowman/
https://cocolab.stanford.edu/ndg.html
Noah D. Goodman
2011-01-01
None
<div><div>
 <h2>Noah D. Goodman</h2>
<p></p>
<p>Associate Professor of <a href="https://www.stanford.edu/dept/psychology/">Psychology</a> and <a href="http://www-cs.stanford.edu/">Computer Science</a>, and <a href="http://linguistics.stanford.edu/">Linguistics</a> (by courtesy), at <a href="http://stanford.edu/">Stanford University</a>.</p>
<p><a href="ndg-cv.html">Curriculum Vitae</a></p>
<p><a href="https://scholar.google.com/citations?user=OUpIbcQAAAAJ">Scholar metrics and bibliography</a></p>
<p>Email: ngoodman at stanford dot edu</p>
<p>Office: 356 Jordan Hall, Stanford.</p>
<h2>Research Interests</h2>
<ul>
 <li>Computational models of cognition.</li>
 <li>Probabilistic programming languages.</li>
 <li>Natural language semantics and pragmatics.</li>
 <li>Concepts, categorization, and intuitive theories.</li>
 <li>Social cognition: reasoning about othersâ traits, states, and actions.</li>
 <li>Cognitive development, especially the acquisition of abstract knowledge.</li>
</ul>
<h2>Teaching</h2>
<h3>Language and Thought (Psych 132)</h3>
<p><a href="https://docs.google.com/document/d/1FNk6-blLkWNYKntHvQ7s6x6CBTBtGLFowTvk00hMt7E/edit?usp=sharing">Spring 2019 syllabus</a></p>
<h3>Computation and Cognition: the Probabilistic Approach (Psych 204 / CS 428)</h3>
<p><a href="psych204-fall2018.html">Fall 2018 Course Website</a></p>
<p>Previously taught: <a href="psych204-fall2017.html">Fall 2017</a>, <a href="psych204-fall2016.html">Fall 2016</a> <a href="psych204-spr2015.html">Spring 2015</a>, <a href="http://www.stanford.edu/~ngoodman/psych204/">Fall 2013</a>, Fall 2012, Winter 2012, Spring 2011.</p>
<h3>Foundations of Cognition (Psych 205)</h3>
<p>Taught: Spring 2015. <a href="https://docs.google.com/document/d/1KV4VaA_ixBJO6JtNe5yt9ckKpyzA_Vhme84SCxYUaNE">Website</a></p>
<h3>Introduction to Cognitive Science (Psych 035, SymSys 100, Ling 144, Phil 190)</h3>
<p>Taught: Winter 2014, Winter 2013, Spring 2012.</p>
<h3>Seminars</h3>
<p>Fall 2018: Topics in Natural and Artificial Intelligence</p>
<p>Spring 2018: Seminar on the Science of Meditation</p>
<p>Spring 2014: <a href="http://explorecourses.stanford.edu/search?view=catalog&amp;filter-coursestatus-Active=on&amp;q=PSYCH%20241:%20Probabilistic%20Models%20of%20Social%20Behavior%20and%20Affect&amp;academicYear=20132014">Probabilistic Models of Social Behavior and Affect</a> (co-taught with Mike Frank and Jamil Zaki).</p>
<p>Spring 2013: <a href="http://www.stanford.edu/class/linguist236/index.html">Representations of Meaning</a> (co-taught with Chris Potts).</p>
<p>Spring 2011: <a href="http://www.stanford.edu/~ngoodman/Psych239.html">Formal and Computational Approaches in Psychology and Cognitive Science</a> (co-taught with Jay McClelland).</p>
<h3>ESLLI 2014</h3>
<p><a href="http://dippl.org/">The Design and Implementation of Probabilistic Programming Languages</a></p>
<h3>IPAM GSS 2011</h3>
<p>I co-organized the 2011 IPAM <a href="http://www.ipam.ucla.edu/programs/gss2011/">Graduate Summer School on Probabilistic Models of Cognition</a>.</p>
 </div>||||I|||| Home
 News
 People
 Publications
 Join Us

 Noah D. Goodman

 Associate Professor of Psychology and Computer Science, and Linguistics (by courtesy), at Stanford University.

 Curriculum Vitae

 Scholar metrics and bibliography

 Email: ngoodman at stanford dot edu

 Office: 356 Jordan Hall, Stanford.

 Research Interests

 * Computational models of cognition.
 * Probabilistic programming languages.
 * Natural language semantics and pragmatics.
 * Concepts, categorization, and intuitive theories.
 * Social cognition: reasoning about othersâ traits, states, and actions.
 * Cognitive development, especially the acquisition of abstract knowledge.

 Teaching

 Language and Thought (Psych 132)

 Spring 2019 syllabus

 Computation and Cognition: the Probabilistic Approach (Psych 204 / CS 428)

 Fall 2018 Course Website

 Previously taught: Fall 2017, Fall 2016 Spring 2015, Fall 2013, Fall 2012, Winter 2012, Spring 2011.

 Foundations of Cognition (Psych 205)

 Taught: Spring 2015. Website

 Introduction to Cognitive Science (Psych 035, SymSys 100, Ling 144, Phil 190)

 Taught: Winter 2014, Winter 2013, Spring 2012.

 Seminars

 Fall 2018: Topics in Natural and Artificial Intelligence

 Spring 2018: Seminar on the Science of Meditation

 Spring 2014: Probabilistic Models of Social Behavior and Affect (co-taught with Mike Frank and Jamil Zaki).

 Spring 2013: Representations of Meaning (co-taught with Chris Potts).

 Spring 2011: Formal and Computational Approaches in Psychology and Cognitive Science (co-taught with Jay McClelland).

 ESLLI 2014

 The Design and Implementation of Probabilistic Programming Languages

 IPAM GSS 2011

 I co-organized the 2011 IPAM Graduate Summer School on Probabilistic Models of Cognition.

Noah D. Goodman
 
Associate Professor of Psychology and Computer Science, and Linguistics (by courtesy), at Stanford University.
 Curriculum Vitae 
 Scholar metrics and bibliography 
Email: ngoodman at stanford dot edu
Office: 356 Jordan Hall, Stanford.
Research Interests
 Computational models of cognition.
 Probabilistic programming languages.
 Natural language semantics and pragmatics.
 Concepts, categorization, and intuitive theories.
 Social cognition: reasoning about othersâ traits, states, and actions.
 Cognitive development, especially the acquisition of abstract knowledge.
Teaching
Language and Thought (Psych 132)
 Spring 2019 syllabus 
Computation and Cognition: the Probabilistic Approach (Psych 204 / CS 428)
 Fall 2018 Course Website 
Previously taught: Fall 2017, Fall 2016 Spring 2015, Fall 2013, Fall 2012, Winter 2012, Spring 2011.
Foundations of Cognition (Psych 205)
Taught: Spring 2015. Website 
Introduction to Cognitive Science (Psych 035, SymSys 100, Ling 144, Phil 190)
Taught: Winter 2014, Winter 2013, Spring 2012.
Seminars
Fall 2018: Topics in Natural and Artificial Intelligence
Spring 2018: Seminar on the Science of Meditation
Spring 2014: Probabilistic Models of Social Behavior and Affect (co-taught with Mike Frank and Jamil Zaki).
Spring 2013: Representations of Meaning (co-taught with Chris Potts).
Spring 2011: Formal and Computational Approaches in Psychology and Cognitive Science (co-taught with Jay McClelland).
ESLLI 2014
 The Design and Implementation of Probabilistic Programming Languages 
IPAM GSS 2011
I co-organized the 2011 IPAM Graduate Summer School on Probabilistic Models of Cognition.
Noah D. Goodman
https://cocolab.stanford.edu/ndg.html
https://thashim.github.io/
Tatsunori Hashimoto
2004-01-01
Tatsunori Hashimoto
<div><div>
 
 
 <div>
 <p><b>Diffusion-LM Improves Controllable Text Generation</b>
 
 <a href="https://arxiv.org/pdf/2205.14217.pdf">PDF</a>
 
 
 
 
 
 </p>
 <p>Lisa Li, John Thickstun, Ishaan Gulrajani, Percy Liang, Tatsunori B Hashimoto</p>
 <p><i>ArXiv preprint</i></p>
 
 </div>
 
 <div>
 <p><b>Undersampling is a Minimax Optimal Robustness Intervention in Nonparametric Classification</b>
 
 <a href="https://arxiv.org/pdf/2205.13094.pdf">PDF</a>
 
 
 
 
 
 </p>
 <p>Niladri Chatterji, Saminul Haque, Tatsunori B Hashimoto</p>
 <p><i>ArXiv preprint</i></p>
 
 </div>
 
 <div>
 <p><b>TempLM: Distilling Language Models into Template-Based Generators</b>
 
 <a href="https://arxiv.org/pdf/2205.11055.pdf">PDF</a>
 
 
 
 
 
 </p>
 <p>Tianyi Zhang, Mina Lee, Lisa Li, Ende Shen, Tatsunori B Hashimoto</p>
 <p><i>ArXiv preprint</i></p>
 
 </div>
 
 <div>
 <p><b>Jury learning: Integrating dissenting voices into machine learning models</b>
 
 <a href="https://dl.acm.org/doi/pdf/10.1145/3491102.3502004">PDF</a>
 
 
 
 
 
 </p>
 <p>Mitchell Gordon, Michelle Lam, Joon Park, Kayur Patel, Jeff Hancock, Tatsunori B Hashimoto, Michael Bernstein</p>
 <p><i>Conference on Human Factors in Computing Systems (CHI 2022, Best paper)</i></p>
 
 </div>
 
 <div>
 <p><b>Spurious Correlations in Reference-Free Evaluation of Text Generation</b>
 
 <a href="https://arxiv.org/pdf/2204.09890.pdf">PDF</a>
 
 
 
 
 
 </p>
 <p>Esin Durmus, Faisal Ladhak, Tatsunori B Hashimoto</p>
 <p><i>Annual Meeting of the Association of Computational Linguistics (ACL 2022)</i></p>
 
 </div>
 
 <div>
 <p><b>Large Language Models Can Be Strong Differentially Private Learners</b>
 
 <a href="https://arxiv.org/pdf/2110.05679.pdf">PDF</a>
 
 
 
 
 
 </p>
 <p>Xuechen Li, Florian Tramer, Percy Liang, Tatsunori B Hashimoto</p>
 <p><i>International Conference on Learning Representations (ICLR 2022 Oral)</i></p>
 
 </div>
 
 <div>
 <p><b>Language Modeling via Stochastic Processes</b>
 
 <a href="https://openreview.net/forum?id=pMQwKL1yctf">PDF</a>
 
 
 
 
 
 </p>
 <p>Rose E Wang, Esin Durmus, Noah Goodman, Tatsunori B Hashimoto</p>
 <p><i>International Conference on Learning Representations (ICLR 2022 Oral)</i></p>
 
 </div>
 
 <div>
 <p><b>Is Importance Weighting Incompatible with Interpolating Classifiers?</b>
 
 <a href="https://arxiv.org/pdf/2112.12986.pdf">PDF</a>
 
 
 
 
 
 </p>
 <p>Ke A Wang, Niladri S Chatterji, Saminul Haque, Tatsunori B Hashimoto</p>
 <p><i>International Conference on Learning Representations (ICLR 2022)</i></p>
 
 </div>
 
 <div>
 <p><b>Distributionally Robust Models with Parametric Likelihood Ratios</b>
 
 <a href="https://openreview.net/pdf?id=a34GrNaYEcS">PDF</a>
 
 
 
 
 
 </p>
 <p>Paul Michel, Tatsunori B Hashimoto, Graham Neubig</p>
 <p><i>International Conference on Learning Representations (ICLR 2022)</i></p>
 
 </div>
 
 <div>
 <p><b>Extending the WILDS Benchmark for Unsupervised Adaptation</b>
 
 <a href="https://openreview.net/forum?id=z7p2V6KROVO">PDF</a>
 
 
 
 
 
 </p>
 <p>Shiori Sagawa, Pang Wei Koh [...] Tatsunori Hashimoto, Sergey Levine, Chelsea Finn, Percy Liang</p>
 <p><i>International Conference on Learning Representations (ICLR 2022 Oral)</i></p>
 
 </div>
 
 <div>
 <p><b>Model Performance Scaling with Multiple Data Sources</b>
 
 <a href="http://proceedings.mlr.press/v139/hashimoto21a.html">PDF</a>
 
 
 
 
 
 </p>
 <p>Tatsunori B Hashimoto</p>
 <p><i>International Conference on Machine Learning (ICML 2021)</i></p>
 
 </div>
 
 <div>
 <p><b>DReCa: A general task augmentation strategy for few-shot natural language inference</b>
 
 <a href="https://www.aclweb.org/anthology/2021.naacl-main.88.pdf">PDF</a>
 
 
 
 
 
 </p>
 <p>Shikhar Murty, Tatsunori B Hashimoto, Christopher D Manning</p>
 <p><i>Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2021)</i></p>
 
 </div>
 
 <div>
 <p><b>On the Inductive Bias of Masked Language Modeling: From Statistical to Syntactic Dependencies</b>
 
 <a href="https://arxiv.org/pdf/2104.05694.pdf">PDF</a>
 
 
 
 
 
 </p>
 <p>Tianyi Zhang, Tatsunori B Hashimoto</p>
 <p><i>Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2021)</i></p>
 
 </div>
 
 <div>
 <p><b>Modeling the Second Player in Distributionally Robust Optimization</b>
 
 <a href="https://openreview.net/pdf?id=ZDnzZrTqU9N">PDF</a>
 
 
 
 
 
 </p>
 <p>Paul Michel, Tatsunori B. Hashimoto, Graham Neubig</p>
 <p><i>International Conference on Learning Representations (ICLR 2021)</i></p>
 
 </div>
 
 <div>
 <p><b>Approximate Selection with Guarantees using Proxies</b>
 
 <a href="https://arxiv.org/abs/2004.00827">PDF</a>
 
 
 
 
 
 </p>
 <p>Daniel Kang<b>*</b>, Edward Gan<b>*</b>, Peter Bailis, Tatsunori B Hashimoto, Matei Zaharia</p>
 <p><i>International Conference on Very Large Data Bases (VLDB 2020)</i></p>
 
 </div>
 
 <div>
 <p><b>Improved Natural Language Generation via Loss Truncation</b>
 
 <a href="https://arxiv.org/abs/2004.14589">PDF</a>
 
 
 
 
 
 </p>
 <p>Daniel Kang, Tatsunori B Hashimoto</p>
 <p><i>Annual Meeting of the Association of Computational Linguistics (ACL 2020)</i></p>
 
 </div>
 
 <div>
 <p><b>Robustness to Spurious Correlations via Human Annotations</b>
 
 <a href="https://arxiv.org/abs/2007.06661">PDF</a>
 
 
 
 
 
 </p>
 <p>Megha Srivastava, Tatsunori B Hashimoto, Percy Liang</p>
 <p><i>International Conference on Machine Learning (ICML 2020)</i></p>
 
 </div>
 
 <div>
 <p><b>Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-case Generalization</b>
 
 <a href="https://arxiv.org/abs/1911.08731">PDF</a>
 
 
 
 
 
 </p>
 <p>Shiori Sagawa<b>*</b>, Pang Wei Koh<b>*</b>, Tatsunori B Hashimoto, Percy Liang</p>
 <p><i>International Conference on Learning Representations (ICLR 2020)</i></p>
 
 </div>
 
 <div>
 <p><b>Distributionally Robust Language Modeling</b>
 
 <a href="https://arxiv.org/abs/1909.02060">PDF</a>
 
 
 
 
 
 </p>
 <p>Yonatan Oren<b>*</b>, Shiori Sagawa <b>*</b>, Tatsunori B Hashimoto <b>*</b>, Percy Liang</p>
 <p><i>Empirical Methods in Natural Language Processing (EMNLP 2019)</i></p>
 
 </div>
 
 <div>
 <p><b>Distributionally Robust Losses Against Mixture Covariate Shifts</b>
 
 <a href="/assets/publications/condrisk.pdf">PDF</a>
 
 
 
 
 
 </p>
 <p>John C Duchi, Tatsunori B Hashimoto, Hongseok Namkoong</p>
 <p><i>Preprint</i></p>
 
 </div>
 
 <div>
 <p><b>Unifying Human and Statistical Evaluation for Natural Language Generation</b>
 
 <a href="/assets/publications/nlg-paper.pdf">PDF</a>
 
 
 
 
 
 </p>
 <p>Tatsunori B Hashimoto<b>*</b>, Hugh Zhang<b>*</b>, Percy Liang</p>
 <p><i>Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2019)</i></p>
 
 </div>
 
 <div>
 <p><b>Generating Sentences by Editing Prototypes</b>
 
 <a href="https://arxiv.org/abs/1709.08878">PDF</a>
 
 
 
 
 
 </p>
 <p>Kelvin Guu<b>*</b>, Tatsunori B Hashimoto<b>*</b>, Yonatan Oren, Percy Liang</p>
 <p><i>Transactions of the Association of Computational Linguistics (TACL, presented at ACL 2018)</i></p>
 
 </div>
 
 <div>
 <p><b>Fairness Without Demographics in Repeated Loss Minimization</b>
 
 <a href="https://arxiv.org/abs/1806.08010">PDF</a>
 
 
 
 
 
 </p>
 <p>Tatsunori B Hashimoto, Megha Srivastava, Hongseok Namkoong, Percy Liang</p>
 <p><i>Proceedings of the 35th International Conference on Machine Learning (ICML 2018, Best paper runner up)</i></p>
 
 </div>
 
 <div>
 <p><b>Word embeddings as metric recovery in semantic spaces</b>
 
 <a href="https://transacl.org/ojs/index.php/tacl/article/view/809">PDF</a>
 
 
 
 
 
 </p>
 <p>Tatsunori B Hashimoto, David Alvarez-Melis, Tommi S Jaakkola</p>
 <p><i>Transactions of the Association for Computational Linguistics 4 (TACL, presented at ACL 2016)</i></p>
 
 </div>
 
 <div>
 <p><b>Metric recovery from directed unweighted graphs</b>
 
 <a href="http://proceedings.mlr.press/v38/hashimoto15.html">PDF</a>
 
 
 
 
 
 </p>
 <p>Tatsunori B Hashimoto, Yi Sun, Tommi S Jaakkola</p>
 <p><i>Artificial Intelligence and Statistics (AISTATS 2015), (best poster at NeurIPS 2014 workshop on networks)</i></p>
 
 </div>
 
 </div><div>
 
 
 <div>
 <p><b>Diffusion-LM Improves Controllable Text Generation</b>
 
 <a href="https://arxiv.org/pdf/2205.14217.pdf">PDF</a>
 
 
 
 
 
 </p>
 <p>Lisa Li, John Thickstun, Ishaan Gulrajani, Percy Liang, Tatsunori B Hashimoto</p>
 <p><i>ArXiv preprint</i></p>
 
 </div>
 
 <div>
 <p><b>Undersampling is a Minimax Optimal Robustness Intervention in Nonparametric Classification</b>
 
 <a href="https://arxiv.org/pdf/2205.13094.pdf">PDF</a>
 
 
 
 
 
 </p>
 <p>Niladri Chatterji, Saminul Haque, Tatsunori B Hashimoto</p>
 <p><i>ArXiv preprint</i></p>
 
 </div>
 
 <div>
 <p><b>TempLM: Distilling Language Models into Template-Based Generators</b>
 
 <a href="https://arxiv.org/pdf/2205.11055.pdf">PDF</a>
 
 
 
 
 
 </p>
 <p>Tianyi Zhang, Mina Lee, Lisa Li, Ende Shen, Tatsunori B Hashimoto</p>
 <p><i>ArXiv preprint</i></p>
 
 </div>
 
 <div>
 <p><b>Jury learning: Integrating dissenting voices into machine learning models</b>
 
 <a href="https://dl.acm.org/doi/pdf/10.1145/3491102.3502004">PDF</a>
 
 
 
 
 
 </p>
 <p>Mitchell Gordon, Michelle Lam, Joon Park, Kayur Patel, Jeff Hancock, Tatsunori B Hashimoto, Michael Bernstein</p>
 <p><i>Conference on Human Factors in Computing Systems (CHI 2022, Best paper)</i></p>
 
 </div>
 
 <div>
 <p><b>Spurious Correlations in Reference-Free Evaluation of Text Generation</b>
 
 <a href="https://arxiv.org/pdf/2204.09890.pdf">PDF</a>
 
 
 
 
 
 </p>
 <p>Esin Durmus, Faisal Ladhak, Tatsunori B Hashimoto</p>
 <p><i>Annual Meeting of the Association of Computational Linguistics (ACL 2022)</i></p>
 
 </div>
 
 <div>
 <p><b>Large Language Models Can Be Strong Differentially Private Learners</b>
 
 <a href="https://arxiv.org/pdf/2110.05679.pdf">PDF</a>
 
 
 
 
 
 </p>
 <p>Xuechen Li, Florian Tramer, Percy Liang, Tatsunori B Hashimoto</p>
 <p><i>International Conference on Learning Representations (ICLR 2022 Oral)</i></p>
 
 </div>
 
 <div>
 <p><b>Language Modeling via Stochastic Processes</b>
 
 <a href="https://openreview.net/forum?id=pMQwKL1yctf">PDF</a>
 
 
 
 
 
 </p>
 <p>Rose E Wang, Esin Durmus, Noah Goodman, Tatsunori B Hashimoto</p>
 <p><i>International Conference on Learning Representations (ICLR 2022 Oral)</i></p>
 
 </div>
 
 <div>
 <p><b>Is Importance Weighting Incompatible with Interpolating Classifiers?</b>
 
 <a href="https://arxiv.org/pdf/2112.12986.pdf">PDF</a>
 
 
 
 
 
 </p>
 <p>Ke A Wang, Niladri S Chatterji, Saminul Haque, Tatsunori B Hashimoto</p>
 <p><i>International Conference on Learning Representations (ICLR 2022)</i></p>
 
 </div>
 
 <div>
 <p><b>Distributionally Robust Models with Parametric Likelihood Ratios</b>
 
 <a href="https://openreview.net/pdf?id=a34GrNaYEcS">PDF</a>
 
 
 
 
 
 </p>
 <p>Paul Michel, Tatsunori B Hashimoto, Graham Neubig</p>
 <p><i>International Conference on Learning Representations (ICLR 2022)</i></p>
 
 </div>
 
 <div>
 <p><b>Extending the WILDS Benchmark for Unsupervised Adaptation</b>
 
 <a href="https://openreview.net/forum?id=z7p2V6KROVO">PDF</a>
 
 
 
 
 
 </p>
 <p>Shiori Sagawa, Pang Wei Koh [...] Tatsunori Hashimoto, Sergey Levine, Chelsea Finn, Percy Liang</p>
 <p><i>International Conference on Learning Representations (ICLR 2022 Oral)</i></p>
 
 </div>
 
 <div>
 <p><b>On the opportunities and risks of foundation models</b>
 
 <a href="https://arxiv.org/pdf/2108.07258.pdf">PDF</a>
 
 
 
 
 
 </p>
 <p>Rishi Bommasani [.. alphabetical authors ..] Tatsunori Hashimoto [...]</p>
 <p><i>ArXiv preprint</i></p>
 
 </div>
 
 <div>
 <p><b>Accelerating approximate aggregation queries with expensive predicates</b>
 
 <a href="https://arxiv.org/pdf/2108.06313.pdf">PDF</a>
 
 
 
 
 
 </p>
 <p>Daniel Kang, John Guibas, Peter Bailis, Tatsunori B Hashimoto, Yi Sun, Matei Zaharia</p>
 <p><i>ACM Conference on Management of Data (SIGMOD 2022)</i></p>
 
 </div>
 
 <div>
 <p><b>Model Performance Scaling with Multiple Data Sources</b>
 
 <a href="http://proceedings.mlr.press/v139/hashimoto21a.html">PDF</a>
 
 
 
 
 
 </p>
 <p>Tatsunori B Hashimoto</p>
 <p><i>International Conference on Machine Learning (ICML 2021)</i></p>
 
 </div>
 
 <div>
 <p><b>Measuring Conversational Uptake: A Case Study on Student-Teacher Interactions</b>
 
 <a href="https://arxiv.org/pdf/2106.03873.pdf">PDF</a>
 
 
 
 
 
 </p>
 <p>Dorottya Demszky, Jing Liu, Zid Mancenido, Julie Cohen, Heather Hill, Dan Jurafsky, Tatsunori B Hashimoto</p>
 <p><i>Annual Meeting of the Association of Computational Linguistics (ACL 2020)</i></p>
 
 </div>
 
 <div>
 <p><b>DReCa: A general task augmentation strategy for few-shot natural language inference</b>
 
 <a href="https://www.aclweb.org/anthology/2021.naacl-main.88.pdf">PDF</a>
 
 
 
 
 
 </p>
 <p>Shikhar Murty, Tatsunori B Hashimoto, Christopher D Manning</p>
 <p><i>Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2021)</i></p>
 
 </div>
 
 <div>
 <p><b>On the Inductive Bias of Masked Language Modeling: From Statistical to Syntactic Dependencies</b>
 
 <a href="https://arxiv.org/pdf/2104.05694.pdf">PDF</a>
 
 
 
 
 
 </p>
 <p>Tianyi Zhang, Tatsunori B Hashimoto</p>
 <p><i>Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2021)</i></p>
 
 </div>
 
 <div>
 <p><b>Modeling the Second Player in Distributionally Robust Optimization</b>
 
 <a href="https://openreview.net/pdf?id=ZDnzZrTqU9N">PDF</a>
 
 
 
 
 
 </p>
 <p>Paul Michel, Tatsunori B. Hashimoto, Graham Neubig</p>
 <p><i>International Conference on Learning Representations (ICLR 2021)</i></p>
 
 </div>
 
 <div>
 <p><b>Task-agnostic Indexes for Deep Learning-based Queries over Unstructured Data</b>
 
 <a href="https://arxiv.org/pdf/2009.04540.pdf">PDF</a>
 
 
 
 
 
 </p>
 <p>Daniel Kang, John Guibas, Peter Bailis, Tatsunori Hashimoto, Matei Zaharia</p>
 <p><i>Preprint</i></p>
 
 </div>
 
 <div>
 <p><b>The Disagreement Deconvolution: Bringing Machine Learning Performance Metrics In Line With Reality</b>
 
 <a href="http://www.kayur.org/papers/chi2021.pdf">PDF</a>
 
 
 
 
 
 </p>
 <p>Mitchell L Gordon, Kaitlyn Zhou, Kayur Patel, Tatsunori B Hashimoto, Michael S Bernstein</p>
 <p><i>Conference on Human Factors in Computing Systems (CHI 2021)</i></p>
 
 </div>
 
 <div>
 <p><b>Approximate Selection with Guarantees using Proxies</b>
 
 <a href="https://arxiv.org/abs/2004.00827">PDF</a>
 
 
 
 
 
 </p>
 <p>Daniel Kang<b>*</b>, Edward Gan<b>*</b>, Peter Bailis, Tatsunori B Hashimoto, Matei Zaharia</p>
 <p><i>International Conference on Very Large Data Bases (VLDB 2020)</i></p>
 
 </div>
 
 <div>
 <p><b>Improved Natural Language Generation via Loss Truncation</b>
 
 <a href="https://arxiv.org/abs/2004.14589">PDF</a>
 
 
 
 
 
 </p>
 <p>Daniel Kang, Tatsunori B Hashimoto</p>
 <p><i>Annual Meeting of the Association of Computational Linguistics (ACL 2020)</i></p>
 
 </div>
 
 <div>
 <p><b>Robustness to Spurious Correlations via Human Annotations</b>
 
 <a href="https://arxiv.org/abs/2007.06661">PDF</a>
 
 
 
 
 
 </p>
 <p>Megha Srivastava, Tatsunori B Hashimoto, Percy Liang</p>
 <p><i>International Conference on Machine Learning (ICML 2020)</i></p>
 
 </div>
 
 <div>
 <p><b>Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-case Generalization</b>
 
 <a href="https://arxiv.org/abs/1911.08731">PDF</a>
 
 
 
 
 
 </p>
 <p>Shiori Sagawa<b>*</b>, Pang Wei Koh<b>*</b>, Tatsunori B Hashimoto, Percy Liang</p>
 <p><i>International Conference on Learning Representations (ICLR 2020)</i></p>
 
 </div>
 
 <div>
 <p><b>Learning Autocomplete Systems as a Communication Game</b>
 
 <a href="https://arxiv.org/abs/1911.06964">PDF</a>
 
 
 
 
 
 </p>
 <p>Mina Lee, Tatsunori B Hashimoto, Percy Liang</p>
 <p><i>Emergent Communication Workshop at Neural Information Processing Systems (NeurIPS 2019)</i></p>
 
 </div>
 
 <div>
 <p><b>Distributionally Robust Language Modeling</b>
 
 <a href="https://arxiv.org/abs/1909.02060">PDF</a>
 
 
 
 
 
 </p>
 <p>Yonatan Oren<b>*</b>, Shiori Sagawa <b>*</b>, Tatsunori B Hashimoto <b>*</b>, Percy Liang</p>
 <p><i>Empirical Methods in Natural Language Processing (EMNLP 2019)</i></p>
 
 </div>
 
 <div>
 <p><b>Distributionally Robust Losses Against Mixture Covariate Shifts</b>
 
 <a href="/assets/publications/condrisk.pdf">PDF</a>
 
 
 
 
 
 </p>
 <p>John C Duchi, Tatsunori B Hashimoto, Hongseok Namkoong</p>
 <p><i>Preprint</i></p>
 
 </div>
 
 <div>
 <p><b>Inferring Multidimensional Rates of Aging from Cross-Sectional Data</b>
 
 <a href="https://arxiv.org/abs/1807.04709">PDF</a>
 
 
 
 
 
 </p>
 <p>Emma Pierson<b>*</b>, Pang Wei Koh <b>*</b>, Tatsunori B Hashimoto <b>*</b>, Daphne Koller, Jure Lesokevic, Nicholas Eriksson, Percy Liang</p>
 <p><i>22nd International Conference on Artificial Intelligence and Statistics (AISTATS 2019)</i></p>
 
 </div>
 
 <div>
 <p><b>Unifying Human and Statistical Evaluation for Natural Language Generation</b>
 
 <a href="/assets/publications/nlg-paper.pdf">PDF</a>
 
 
 
 
 
 </p>
 <p>Tatsunori B Hashimoto<b>*</b>, Hugh Zhang<b>*</b>, Percy Liang</p>
 <p><i>Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2019)</i></p>
 
 </div>
 
 <div>
 <p><b>A Retrieve-and-Edit Framework for Predicting Structured Outputs</b>
 
 <a href="http://arxiv.org/abs/1812.01194">PDF</a>
 
 
 
 
 
 </p>
 <p>Tatsunori B Hashimoto, Kelvin Guu, Yonatan Oren, Percy Liang</p>
 <p><i>Advances in Neural Information Processing Systems 31 (NeurIPS 2018, Oral)</i></p>
 
 </div>
 
 <div>
 <p><b>Generating Sentences by Editing Prototypes</b>
 
 <a href="https://arxiv.org/abs/1709.08878">PDF</a>
 
 
 
 
 
 </p>
 <p>Kelvin Guu<b>*</b>, Tatsunori B Hashimoto<b>*</b>, Yonatan Oren, Percy Liang</p>
 <p><i>Transactions of the Association of Computational Linguistics (TACL, presented at ACL 2018)</i></p>
 
 </div>
 
 <div>
 <p><b>Fairness Without Demographics in Repeated Loss Minimization</b>
 
 <a href="https://arxiv.org/abs/1806.08010">PDF</a>
 
 
 
 
 
 </p>
 <p>Tatsunori B Hashimoto, Megha Srivastava, Hongseok Namkoong, Percy Liang</p>
 <p><i>Proceedings of the 35th International Conference on Machine Learning (ICML 2018, Best paper runner up)</i></p>
 
 </div>
 
 <div>
 <p><b>Derivative free optimization via repeated classification</b>
 
 <a href="https://arxiv.org/abs/1804.03761">PDF</a>
 
 
 
 
 
 </p>
 <p>Tatsunori B Hashimoto, Steve Yadlowsky, John C Duchi</p>
 <p><i>21st International Conference on Artificial Intelligence and Statistics (AISTATS 2018)</i></p>
 
 </div>
 
 <div>
 <p><b>Unsupervised Transformation Learning via Convex Relaxations</b>
 
 <a href="http://papers.nips.cc/paper/7263-unsupervised-transformation-learning-via-convex-relaxations">PDF</a>
 
 
 
 
 
 </p>
 <p>Tatsunori B Hashimoto, Percy S Liang, John C Duchi</p>
 <p><i>Advances in Neural Information Processing Systems 30 (NeurIPS 2017)</i></p>
 
 </div>
 
 <div>
 <p><b>Continuous representations and models from random walk diffusion limits</b>
 
 <a href="https://dspace.mit.edu/handle/1721.1/105670">PDF</a>
 
 
 
 
 
 </p>
 <p>Tatsunori B Hashimoto</p>
 <p><i>PhD Thesis, MIT CSAIL, 2016</i></p>
 
 </div>
 
 <div>
 <p><b>Word embeddings as metric recovery in semantic spaces</b>
 
 <a href="https://transacl.org/ojs/index.php/tacl/article/view/809">PDF</a>
 
 
 
 
 
 </p>
 <p>Tatsunori B Hashimoto, David Alvarez-Melis, Tommi S Jaakkola</p>
 <p><i>Transactions of the Association for Computational Linguistics 4 (TACL, presented at ACL 2016)</i></p>
 
 </div>
 
 <div>
 <p><b>Learning Population-Level Diffusions with Generative RNNs</b>
 
 <a href="http://proceedings.mlr.press/v48/hashimoto16.pdf">PDF</a>
 
 
 
 
 
 </p>
 <p>Tatsunori B Hashimoto, David Gifford, Tommi S Jaakkola</p>
 <p><i>Proceedings of the 33rd International Conference on Machine Learning (ICML 2016)</i></p>
 
 </div>
 
 <div>
 <p><b>From random walks to distances on unweighted graphs</b>
 
 <a href="http://papers.nips.cc/paper/6009-from-random-walks-to-distances-on-unweighted-graph">PDF</a>
 
 
 
 
 
 </p>
 <p>Tatsunori B Hashimoto, Yi Sun, Tommi S Jaakkola</p>
 <p><i>Advances in Neural Information Processing Systems (NeurIPS 2015)</i></p>
 
 </div>
 
 <div>
 <p><b>Metric recovery from directed unweighted graphs</b>
 
 <a href="http://proceedings.mlr.press/v38/hashimoto15.html">PDF</a>
 
 
 
 
 
 </p>
 <p>Tatsunori B Hashimoto, Yi Sun, Tommi S Jaakkola</p>
 <p><i>Artificial Intelligence and Statistics (AISTATS 2015), (best poster at NeurIPS 2014 workshop on networks)</i></p>
 
 </div>
 
 <div>
 <p><b>A synergistic DNA logic predicts genome-wide chromatin accessibility</b>
 
 <a href="http://genome.cshlp.org/content/26/10/1430.short">PDF</a>
 
 
 
 
 
 </p>
 <p>Tatsunori B Hashimoto<b>*</b>, Richard I Sherwood<b>*</b>, Daniel D Kang<b>*</b>, Nisha Rajagopal, Amira A Barkal, Haoyang Zeng, Bart JM Emons, Sharanya Srinivasan, Tommi S Jaakkola, David K Gifford</p>
 <p><i>Genome research (2016)</i></p>
 
 </div>
 
 <div>
 <p><b>Cas9 Functionally Opens Chromatin</b>
 
 <a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0152683">PDF</a>
 
 
 
 
 
 </p>
 <p>Amira A Barkal, Sharanya Srinivasan, Tatsunori B Hashimoto, David K Gifford, Richard I Sherwood</p>
 <p><i>PloS One (2016)</i></p>
 
 </div>
 
 <div>
 <p><b>Cloning-free CRISPR</b>
 
 <a href="http://www.sciencedirect.com/science/article/pii/S2213671115002842">PDF</a>
 
 
 
 
 
 </p>
 <p>Mandana Arbab, Sharanya Srinivasan, Tatsunori B Hashimoto, Niels Geijsen, Richard I Sherwood</p>
 <p><i>Stem cell reports (2015)</i></p>
 
 </div>
 
 <div>
 <p><b>GERV: a statistical method for generative evaluation of regulatory variants for transcription factor binding</b>
 
 <a href="http://www.sciencedirect.com/science/article/pii/S2213671115002842">PDF</a>
 
 
 
 
 
 </p>
 <p>Haoyang Zeng, Tatsunori B Hashimoto, Daniel D Kang, David K Gifford</p>
 <p><i>Bioinformatics (2015)</i></p>
 
 </div>
 
 <div>
 <p><b>Long-term persistence and development of induced pancreatic beta cells generated by lineage conversion of acinar cells</b>
 
 <a href="https://www.nature.com/articles/nbt.3082">PDF</a>
 
 
 
 
 
 </p>
 <p>Weida Li, Claudia Cavelti-Weder, Yinying Zhang, Kendell Clement, Scott Donovan, Gabriel Gonzalez, Jiang Zhu, Marianne Stemann, Ke Xu, Tatsunori B Hashimoto, Takatsugu Yamada, Mio Nakanishi, Yuemei Zhang, Samuel Zeng, David Gifford, Alexander Meissner, Gordon Weir, Qiao Zhou</p>
 <p><i>Nature Biotechnology (2014)</i></p>
 
 </div>
 
 <div>
 <p><b>Universal count correction for high-throughput sequencing</b>
 
 <a href="http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003494">PDF</a>
 
 
 
 
 
 </p>
 <p>Tatsunori B Hashimoto, Matthew D Edwards, David K Gifford</p>
 <p><i>PLoS computational biology (2014)</i></p>
 
 </div>
 
 <div>
 <p><b>Discovery of directional and nondirectional pioneer transcription factors by modeling DNase profile magnitude and shape</b>
 
 <a href="https://www.nature.com/articles/nbt.2798">PDF</a>
 
 
 
 
 
 </p>
 <p>Richard I Sherwood <b>*</b>, Tatsunori B Hashimoto <b>*</b>, Charles W O'donnell <b>*</b>, Sophia Lewis, Amira A Barkal, John Peter Van Hoff, Vivek Karun, Tommi S Jaakkola, David K Gifford</p>
 <p><i>Nature Biotechnology (2014)</i></p>
 
 </div>
 
 <div>
 <p><b>Quantifying condition-dependent intracellular protein levels enables high-precision fitness estimates</b>
 
 <a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0075320">PDF</a>
 
 
 
 
 
 </p>
 <p>Kerry A Geiler-Samerotte, Tatsunori B Hashimoto, Michael F Dion, Bogdan A Budnik, Edoardo M Airoldi, D Allan Drummond</p>
 <p><i>PloS one (2013)</i></p>
 
 </div>
 
 <div>
 <p><b>Lineage-based identification of cellular states and expression programs</b>
 
 <a href="http://www.sciencedirect.com/science/article/pii/S2213671115002842">PDF</a>
 
 
 
 
 
 </p>
 <p>Tatsunori B Hashimoto, Tommi S Jaakkola, Richard Sherwood, Esteban O. Mazzoni, Hynek Wichterle, David Gifford</p>
 <p><i>Bioinformatics (2012)</i></p>
 
 </div>
 
 <div>
 <p><b>Finding drug discovery rules of thumb with bump hunting</b>
 
 <a href="http://www.acscinf.org/content/240-abstracts#S81">PDF</a>
 
 
 
 
 
 </p>
 <p>Tatsunori B Hashimoto, Matthew Segall</p>
 <p><i>Proceedings of the ACS (2010)</i></p>
 
 </div>
 
 <div>
 <p><b>BFL: a node and edge betweenness based fast layout algorithm for large scale networks.</b>
 
 <a href="https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/1471-2105-10-19">PDF</a>
 
 
 
 
 
 </p>
 <p>Tatsunori B Hashimoto, Masao Nagasaki, Kaname Kojima, Satoru Miyano</p>
 <p><i>BMC bioinformatics (2009)</i></p>
 
 </div>
 
 </div><div>
 
 
 <div>
 <p><b>Undersampling is a Minimax Optimal Robustness Intervention in Nonparametric Classification</b>
 
 <a href="https://arxiv.org/pdf/2205.13094.pdf">PDF</a>
 
 
 
 
 
 </p>
 <p>Niladri Chatterji, Saminul Haque, Tatsunori B Hashimoto</p>
 <p><i>ArXiv preprint</i></p>
 
 </div>
 
 <div>
 <p><b>Large Language Models Can Be Strong Differentially Private Learners</b>
 
 <a href="https://arxiv.org/pdf/2110.05679.pdf">PDF</a>
 
 
 
 
 
 </p>
 <p>Xuechen Li, Florian Tramer, Percy Liang, Tatsunori B Hashimoto</p>
 <p><i>International Conference on Learning Representations (ICLR 2022 Oral)</i></p>
 
 </div>
 
 <div>
 <p><b>Is Importance Weighting Incompatible with Interpolating Classifiers?</b>
 
 <a href="https://arxiv.org/pdf/2112.12986.pdf">PDF</a>
 
 
 
 
 
 </p>
 <p>Ke A Wang, Niladri S Chatterji, Saminul Haque, Tatsunori B Hashimoto</p>
 <p><i>International Conference on Learning Representations (ICLR 2022)</i></p>
 
 </div>
 
 <div>
 <p><b>Distributionally Robust Models with Parametric Likelihood Ratios</b>
 
 <a href="https://openreview.net/pdf?id=a34GrNaYEcS">PDF</a>
 
 
 
 
 
 </p>
 <p>Paul Michel, Tatsunori B Hashimoto, Graham Neubig</p>
 <p><i>International Conference on Learning Representations (ICLR 2022)</i></p>
 
 </div>
 
 <div>
 <p><b>Extending the WILDS Benchmark for Unsupervised Adaptation</b>
 
 <a href="https://openreview.net/forum?id=z7p2V6KROVO">PDF</a>
 
 
 
 
 
 </p>
 <p>Shiori Sagawa, Pang Wei Koh [...] Tatsunori Hashimoto, Sergey Levine, Chelsea Finn, Percy Liang</p>
 <p><i>International Conference on Learning Representations (ICLR 2022 Oral)</i></p>
 
 </div>
 
 <div>
 <p><b>Model Performance Scaling with Multiple Data Sources</b>
 
 <a href="http://proceedings.mlr.press/v139/hashimoto21a.html">PDF</a>
 
 
 
 
 
 </p>
 <p>Tatsunori B Hashimoto</p>
 <p><i>International Conference on Machine Learning (ICML 2021)</i></p>
 
 </div>
 
 <div>
 <p><b>Modeling the Second Player in Distributionally Robust Optimization</b>
 
 <a href="https://openreview.net/pdf?id=ZDnzZrTqU9N">PDF</a>
 
 
 
 
 
 </p>
 <p>Paul Michel, Tatsunori B. Hashimoto, Graham Neubig</p>
 <p><i>International Conference on Learning Representations (ICLR 2021)</i></p>
 
 </div>
 
 <div>
 <p><b>Task-agnostic Indexes for Deep Learning-based Queries over Unstructured Data</b>
 
 <a href="https://arxiv.org/pdf/2009.04540.pdf">PDF</a>
 
 
 
 
 
 </p>
 <p>Daniel Kang, John Guibas, Peter Bailis, Tatsunori Hashimoto, Matei Zaharia</p>
 <p><i>Preprint</i></p>
 
 </div>
 
 <div>
 <p><b>Approximate Selection with Guarantees using Proxies</b>
 
 <a href="https://arxiv.org/abs/2004.00827">PDF</a>
 
 
 
 
 
 </p>
 <p>Daniel Kang<b>*</b>, Edward Gan<b>*</b>, Peter Bailis, Tatsunori B Hashimoto, Matei Zaharia</p>
 <p><i>International Conference on Very Large Data Bases (VLDB 2020)</i></p>
 
 </div>
 
 <div>
 <p><b>Robustness to Spurious Correlations via Human Annotations</b>
 
 <a href="https://arxiv.org/abs/2007.06661">PDF</a>
 
 
 
 
 
 </p>
 <p>Megha Srivastava, Tatsunori B Hashimoto, Percy Liang</p>
 <p><i>International Conference on Machine Learning (ICML 2020)</i></p>
 
 </div>
 
 <div>
 <p><b>Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-case Generalization</b>
 
 <a href="https://arxiv.org/abs/1911.08731">PDF</a>
 
 
 
 
 
 </p>
 <p>Shiori Sagawa<b>*</b>, Pang Wei Koh<b>*</b>, Tatsunori B Hashimoto, Percy Liang</p>
 <p><i>International Conference on Learning Representations (ICLR 2020)</i></p>
 
 </div>
 
 <div>
 <p><b>Distributionally Robust Losses Against Mixture Covariate Shifts</b>
 
 <a href="/assets/publications/condrisk.pdf">PDF</a>
 
 
 
 
 
 </p>
 <p>John C Duchi, Tatsunori B Hashimoto, Hongseok Namkoong</p>
 <p><i>Preprint</i></p>
 
 </div>
 
 <div>
 <p><b>A Retrieve-and-Edit Framework for Predicting Structured Outputs</b>
 
 <a href="http://arxiv.org/abs/1812.01194">PDF</a>
 
 
 
 
 
 </p>
 <p>Tatsunori B Hashimoto, Kelvin Guu, Yonatan Oren, Percy Liang</p>
 <p><i>Advances in Neural Information Processing Systems 31 (NeurIPS 2018, Oral)</i></p>
 
 </div>
 
 <div>
 <p><b>Fairness Without Demographics in Repeated Loss Minimization</b>
 
 <a href="https://arxiv.org/abs/1806.08010">PDF</a>
 
 
 
 
 
 </p>
 <p>Tatsunori B Hashimoto, Megha Srivastava, Hongseok Namkoong, Percy Liang</p>
 <p><i>Proceedings of the 35th International Conference on Machine Learning (ICML 2018, Best paper runner up)</i></p>
 
 </div>
 
 <div>
 <p><b>Derivative free optimization via repeated classification</b>
 
 <a href="https://arxiv.org/abs/1804.03761">PDF</a>
 
 
 
 
 
 </p>
 <p>Tatsunori B Hashimoto, Steve Yadlowsky, John C Duchi</p>
 <p><i>21st International Conference on Artificial Intelligence and Statistics (AISTATS 2018)</i></p>
 
 </div>
 
 <div>
 <p><b>Unsupervised Transformation Learning via Convex Relaxations</b>
 
 <a href="http://papers.nips.cc/paper/7263-unsupervised-transformation-learning-via-convex-relaxations">PDF</a>
 
 
 
 
 
 </p>
 <p>Tatsunori B Hashimoto, Percy S Liang, John C Duchi</p>
 <p><i>Advances in Neural Information Processing Systems 30 (NeurIPS 2017)</i></p>
 
 </div>
 
 <div>
 <p><b>Continuous representations and models from random walk diffusion limits</b>
 
 <a href="https://dspace.mit.edu/handle/1721.1/105670">PDF</a>
 
 
 
 
 
 </p>
 <p>Tatsunori B Hashimoto</p>
 <p><i>PhD Thesis, MIT CSAIL, 2016</i></p>
 
 </div>
 
 <div>
 <p><b>Word embeddings as metric recovery in semantic spaces</b>
 
 <a href="https://transacl.org/ojs/index.php/tacl/article/view/809">PDF</a>
 
 
 
 
 
 </p>
 <p>Tatsunori B Hashimoto, David Alvarez-Melis, Tommi S Jaakkola</p>
 <p><i>Transactions of the Association for Computational Linguistics 4 (TACL, presented at ACL 2016)</i></p>
 
 </div>
 
 <div>
 <p><b>Learning Population-Level Diffusions with Generative RNNs</b>
 
 <a href="http://proceedings.mlr.press/v48/hashimoto16.pdf">PDF</a>
 
 
 
 
 
 </p>
 <p>Tatsunori B Hashimoto, David Gifford, Tommi S Jaakkola</p>
 <p><i>Proceedings of the 33rd International Conference on Machine Learning (ICML 2016)</i></p>
 
 </div>
 
 <div>
 <p><b>From random walks to distances on unweighted graphs</b>
 
 <a href="http://papers.nips.cc/paper/6009-from-random-walks-to-distances-on-unweighted-graph">PDF</a>
 
 
 
 
 
 </p>
 <p>Tatsunori B Hashimoto, Yi Sun, Tommi S Jaakkola</p>
 <p><i>Advances in Neural Information Processing Systems (NeurIPS 2015)</i></p>
 
 </div>
 
 <div>
 <p><b>Metric recovery from directed unweighted graphs</b>
 
 <a href="http://proceedings.mlr.press/v38/hashimoto15.html">PDF</a>
 
 
 
 
 
 </p>
 <p>Tatsunori B Hashimoto, Yi Sun, Tommi S Jaakkola</p>
 <p><i>Artificial Intelligence and Statistics (AISTATS 2015), (best poster at NeurIPS 2014 workshop on networks)</i></p>
 
 </div>
 
 </div><div>
 
 
 <div>
 <p><b>Diffusion-LM Improves Controllable Text Generation</b>
 
 <a href="https://arxiv.org/pdf/2205.14217.pdf">PDF</a>
 
 
 
 
 
 </p>
 <p>Lisa Li, John Thickstun, Ishaan Gulrajani, Percy Liang, Tatsunori B Hashimoto</p>
 <p><i>ArXiv preprint</i></p>
 </div>
 
 <div>
 <p><b>TempLM: Distilling Language Models into Template-Based Generators</b>
 
 <a href="https://arxiv.org/pdf/2205.11055.pdf">PDF</a>
 
 
 
 
 
 </p>
 <p>Tianyi Zhang, Mina Lee, Lisa Li, Ende Shen, Tatsunori B Hashimoto</p>
 <p><i>ArXiv preprint</i></p>
 </div>
 
 <div>
 <p><b>Jury learning: Integrating dissenting voices into machine learning models</b>
 
 <a href="https://dl.acm.org/doi/pdf/10.1145/3491102.3502004">PDF</a>
 
 
 
 
 
 </p>
 <p>Mitchell Gordon, Michelle Lam, Joon Park, Kayur Patel, Jeff Hancock, Tatsunori B Hashimoto, Michael Bernstein</p>
 <p><i>Conference on Human Factors in Computing Systems (CHI 2022, Best paper)</i></p>
 </div>
 
 <div>
 <p><b>Spurious Correlations in Reference-Free Evaluation of Text Generation</b>
 
 <a href="https://arxiv.org/pdf/2204.09890.pdf">PDF</a>
 
 
 
 
 
 </p>
 <p>Esin Durmus, Faisal Ladhak, Tatsunori B Hashimoto</p>
 <p><i>Annual Meeting of the Association of Computational Linguistics (ACL 2022)</i></p>
 </div>
 
 <div>
 <p><b>Large Language Models Can Be Strong Differentially Private Learners</b>
 
 <a href="https://arxiv.org/pdf/2110.05679.pdf">PDF</a>
 
 
 
 
 
 </p>
 <p>Xuechen Li, Florian Tramer, Percy Liang, Tatsunori B Hashimoto</p>
 <p><i>International Conference on Learning Representations (ICLR 2022 Oral)</i></p>
 </div>
 
 <div>
 <p><b>Language Modeling via Stochastic Processes</b>
 
 <a href="https://openreview.net/forum?id=pMQwKL1yctf">PDF</a>
 
 
 
 
 
 </p>
 <p>Rose E Wang, Esin Durmus, Noah Goodman, Tatsunori B Hashimoto</p>
 <p><i>International Conference on Learning Representations (ICLR 2022 Oral)</i></p>
 </div>
 
 <div>
 <p><b>On the opportunities and risks of foundation models</b>
 
 <a href="https://arxiv.org/pdf/2108.07258.pdf">PDF</a>
 
 
 
 
 
 </p>
 <p>Rishi Bommasani [.. alphabetical authors ..] Tatsunori Hashimoto [...]</p>
 <p><i>ArXiv preprint</i></p>
 </div>
 
 <div>
 <p><b>Measuring Conversational Uptake: A Case Study on Student-Teacher Interactions</b>
 
 <a href="https://arxiv.org/pdf/2106.03873.pdf">PDF</a>
 
 
 
 
 
 </p>
 <p>Dorottya Demszky, Jing Liu, Zid Mancenido, Julie Cohen, Heather Hill, Dan Jurafsky, Tatsunori B Hashimoto</p>
 <p><i>Annual Meeting of the Association of Computational Linguistics (ACL 2020)</i></p>
 </div>
 
 <div>
 <p><b>DReCa: A general task augmentation strategy for few-shot natural language inference</b>
 
 <a href="https://www.aclweb.org/anthology/2021.naacl-main.88.pdf">PDF</a>
 
 
 
 
 
 </p>
 <p>Shikhar Murty, Tatsunori B Hashimoto, Christopher D Manning</p>
 <p><i>Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2021)</i></p>
 </div>
 
 <div>
 <p><b>On the Inductive Bias of Masked Language Modeling: From Statistical to Syntactic Dependencies</b>
 
 <a href="https://arxiv.org/pdf/2104.05694.pdf">PDF</a>
 
 
 
 
 
 </p>
 <p>Tianyi Zhang, Tatsunori B Hashimoto</p>
 <p><i>Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2021)</i></p>
 </div>
 
 <div>
 <p><b>The Disagreement Deconvolution: Bringing Machine Learning Performance Metrics In Line With Reality</b>
 
 <a href="http://www.kayur.org/papers/chi2021.pdf">PDF</a>
 
 
 
 
 
 </p>
 <p>Mitchell L Gordon, Kaitlyn Zhou, Kayur Patel, Tatsunori B Hashimoto, Michael S Bernstein</p>
 <p><i>Conference on Human Factors in Computing Systems (CHI 2021)</i></p>
 </div>
 
 <div>
 <p><b>Improved Natural Language Generation via Loss Truncation</b>
 
 <a href="https://arxiv.org/abs/2004.14589">PDF</a>
 
 
 
 
 
 </p>
 <p>Daniel Kang, Tatsunori B Hashimoto</p>
 <p><i>Annual Meeting of the Association of Computational Linguistics (ACL 2020)</i></p>
 </div>
 
 <div>
 <p><b>Learning Autocomplete Systems as a Communication Game</b>
 
 <a href="https://arxiv.org/abs/1911.06964">PDF</a>
 
 
 
 
 
 </p>
 <p>Mina Lee, Tatsunori B Hashimoto, Percy Liang</p>
 <p><i>Emergent Communication Workshop at Neural Information Processing Systems (NeurIPS 2019)</i></p>
 </div>
 
 <div>
 <p><b>Distributionally Robust Language Modeling</b>
 
 <a href="https://arxiv.org/abs/1909.02060">PDF</a>
 
 
 
 
 
 </p>
 <p>Yonatan Oren<b>*</b>, Shiori Sagawa <b>*</b>, Tatsunori B Hashimoto <b>*</b>, Percy Liang</p>
 <p><i>Empirical Methods in Natural Language Processing (EMNLP 2019)</i></p>
 </div>
 
 <div>
 <p><b>Unifying Human and Statistical Evaluation for Natural Language Generation</b>
 
 <a href="/assets/publications/nlg-paper.pdf">PDF</a>
 
 
 
 
 
 </p>
 <p>Tatsunori B Hashimoto<b>*</b>, Hugh Zhang<b>*</b>, Percy Liang</p>
 <p><i>Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2019)</i></p>
 </div>
 
 <div>
 <p><b>A Retrieve-and-Edit Framework for Predicting Structured Outputs</b>
 
 <a href="http://arxiv.org/abs/1812.01194">PDF</a>
 
 
 
 
 
 </p>
 <p>Tatsunori B Hashimoto, Kelvin Guu, Yonatan Oren, Percy Liang</p>
 <p><i>Advances in Neural Information Processing Systems 31 (NeurIPS 2018, Oral)</i></p>
 </div>
 
 <div>
 <p><b>Generating Sentences by Editing Prototypes</b>
 
 <a href="https://arxiv.org/abs/1709.08878">PDF</a>
 
 
 
 
 
 </p>
 <p>Kelvin Guu<b>*</b>, Tatsunori B Hashimoto<b>*</b>, Yonatan Oren, Percy Liang</p>
 <p><i>Transactions of the Association of Computational Linguistics (TACL, presented at ACL 2018)</i></p>
 </div>
 
 <div>
 <p><b>Continuous representations and models from random walk diffusion limits</b>
 
 <a href="https://dspace.mit.edu/handle/1721.1/105670">PDF</a>
 
 
 
 
 
 </p>
 <p>Tatsunori B Hashimoto</p>
 <p><i>PhD Thesis, MIT CSAIL, 2016</i></p>
 </div>
 
 <div>
 <p><b>Word embeddings as metric recovery in semantic spaces</b>
 
 <a href="https://transacl.org/ojs/index.php/tacl/article/view/809">PDF</a>
 
 
 
 
 
 </p>
 <p>Tatsunori B Hashimoto, David Alvarez-Melis, Tommi S Jaakkola</p>
 <p><i>Transactions of the Association for Computational Linguistics 4 (TACL, presented at ACL 2016)</i></p>
 </div>
 
 </div><div>
 
 
 <div>
 <p><b>Inferring Multidimensional Rates of Aging from Cross-Sectional Data</b>
 
 <a href="https://arxiv.org/abs/1807.04709">PDF</a>
 
 
 
 
 
 </p>
 <p>Emma Pierson<b>*</b>, Pang Wei Koh <b>*</b>, Tatsunori B Hashimoto <b>*</b>, Daphne Koller, Jure Lesokevic, Nicholas Eriksson, Percy Liang</p>
 <p><i>22nd International Conference on Artificial Intelligence and Statistics (AISTATS 2019)</i></p>
 </div>
 
 <div>
 <p><b>Continuous representations and models from random walk diffusion limits</b>
 
 <a href="https://dspace.mit.edu/handle/1721.1/105670">PDF</a>
 
 
 
 
 
 </p>
 <p>Tatsunori B Hashimoto</p>
 <p><i>PhD Thesis, MIT CSAIL, 2016</i></p>
 </div>
 
 <div>
 <p><b>Learning Population-Level Diffusions with Generative RNNs</b>
 
 <a href="http://proceedings.mlr.press/v48/hashimoto16.pdf">PDF</a>
 
 
 
 
 
 </p>
 <p>Tatsunori B Hashimoto, David Gifford, Tommi S Jaakkola</p>
 <p><i>Proceedings of the 33rd International Conference on Machine Learning (ICML 2016)</i></p>
 </div>
 
 <div>
 <p><b>A synergistic DNA logic predicts genome-wide chromatin accessibility</b>
 
 <a href="http://genome.cshlp.org/content/26/10/1430.short">PDF</a>
 
 
 
 
 
 </p>
 <p>Tatsunori B Hashimoto<b>*</b>, Richard I Sherwood<b>*</b>, Daniel D Kang<b>*</b>, Nisha Rajagopal, Amira A Barkal, Haoyang Zeng, Bart JM Emons, Sharanya Srinivasan, Tommi S Jaakkola, David K Gifford</p>
 <p><i>Genome research (2016)</i></p>
 </div>
 
 <div>
 <p><b>Cas9 Functionally Opens Chromatin</b>
 
 <a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0152683">PDF</a>
 
 
 
 
 
 </p>
 <p>Amira A Barkal, Sharanya Srinivasan, Tatsunori B Hashimoto, David K Gifford, Richard I Sherwood</p>
 <p><i>PloS One (2016)</i></p>
 </div>
 
 <div>
 <p><b>Cloning-free CRISPR</b>
 
 <a href="http://www.sciencedirect.com/science/article/pii/S2213671115002842">PDF</a>
 
 
 
 
 
 </p>
 <p>Mandana Arbab, Sharanya Srinivasan, Tatsunori B Hashimoto, Niels Geijsen, Richard I Sherwood</p>
 <p><i>Stem cell reports (2015)</i></p>
 </div>
 
 <div>
 <p><b>GERV: a statistical method for generative evaluation of regulatory variants for transcription factor binding</b>
 
 <a href="http://www.sciencedirect.com/science/article/pii/S2213671115002842">PDF</a>
 
 
 
 
 
 </p>
 <p>Haoyang Zeng, Tatsunori B Hashimoto, Daniel D Kang, David K Gifford</p>
 <p><i>Bioinformatics (2015)</i></p>
 </div>
 
 <div>
 <p><b>Long-term persistence and development of induced pancreatic beta cells generated by lineage conversion of acinar cells</b>
 
 <a href="https://www.nature.com/articles/nbt.3082">PDF</a>
 
 
 
 
 
 </p>
 <p>Weida Li, Claudia Cavelti-Weder, Yinying Zhang, Kendell Clement, Scott Donovan, Gabriel Gonzalez, Jiang Zhu, Marianne Stemann, Ke Xu, Tatsunori B Hashimoto, Takatsugu Yamada, Mio Nakanishi, Yuemei Zhang, Samuel Zeng, David Gifford, Alexander Meissner, Gordon Weir, Qiao Zhou</p>
 <p><i>Nature Biotechnology (2014)</i></p>
 </div>
 
 <div>
 <p><b>Universal count correction for high-throughput sequencing</b>
 
 <a href="http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003494">PDF</a>
 
 
 
 
 
 </p>
 <p>Tatsunori B Hashimoto, Matthew D Edwards, David K Gifford</p>
 <p><i>PLoS computational biology (2014)</i></p>
 </div>
 
 <div>
 <p><b>Discovery of directional and nondirectional pioneer transcription factors by modeling DNase profile magnitude and shape</b>
 
 <a href="https://www.nature.com/articles/nbt.2798">PDF</a>
 
 
 
 
 
 </p>
 <p>Richard I Sherwood <b>*</b>, Tatsunori B Hashimoto <b>*</b>, Charles W O'donnell <b>*</b>, Sophia Lewis, Amira A Barkal, John Peter Van Hoff, Vivek Karun, Tommi S Jaakkola, David K Gifford</p>
 <p><i>Nature Biotechnology (2014)</i></p>
 </div>
 
 <div>
 <p><b>Quantifying condition-dependent intracellular protein levels enables high-precision fitness estimates</b>
 
 <a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0075320">PDF</a>
 
 
 
 
 
 </p>
 <p>Kerry A Geiler-Samerotte, Tatsunori B Hashimoto, Michael F Dion, Bogdan A Budnik, Edoardo M Airoldi, D Allan Drummond</p>
 <p><i>PloS one (2013)</i></p>
 </div>
 
 <div>
 <p><b>Lineage-based identification of cellular states and expression programs</b>
 
 <a href="http://www.sciencedirect.com/science/article/pii/S2213671115002842">PDF</a>
 
 
 
 
 
 </p>
 <p>Tatsunori B Hashimoto, Tommi S Jaakkola, Richard Sherwood, Esteban O. Mazzoni, Hynek Wichterle, David Gifford</p>
 <p><i>Bioinformatics (2012)</i></p>
 </div>
 
 <div>
 <p><b>Finding drug discovery rules of thumb with bump hunting</b>
 
 <a href="http://www.acscinf.org/content/240-abstracts#S81">PDF</a>
 
 
 
 
 
 </p>
 <p>Tatsunori B Hashimoto, Matthew Segall</p>
 <p><i>Proceedings of the ACS (2010)</i></p>
 </div>
 
 <div>
 <p><b>BFL: a node and edge betweenness based fast layout algorithm for large scale networks.</b>
 
 <a href="https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/1471-2105-10-19">PDF</a>
 
 
 
 
 
 </p>
 <p>Tatsunori B Hashimoto, Masao Nagasaki, Kaname Kojima, Satoru Miyano</p>
 <p><i>BMC bioinformatics (2009)</i></p>
 </div>
 
 </div>||||I|||| Tatsunori Hashimoto

 Assistant Professor, Stanford

 thashim [AT] stanford.edu

 * Bio
 * Publications
 * Resume

 Bio

 I am currently an assistant professor at the computer science department in Stanford university.

 My research uses tools from statistics to make machine learning systems more robust and reliable — especially in challenging tasks involving natural language. The goal of my research is to use robustness and worst-case performance as a lens to understand and make progress on several fundamental challenges in machine learning and natural language processing. A few topics of recent interest are,

 Long-tail behavior How can we ensure that a machine learning system won't fail catastrophically in the wild under changing conditions? Understanding A system which understands how to answer questions or generate text should also do so robustly out-of-domain. Fairness Machine learning systems which rely on unreliable correlations can result in spurious and harmful predictions.

 Previously, I was a post-doc at Stanford working for John C. Duchi and Percy Liang on tradeoffs between the average and worst-case performance of machine learning models. Before my post-doc, I was a graduate student at MIT co-advised by Tommi Jaakkola and David Gifford and a undergraduate student at Harvard in statistics and math advised by Edoardo Airoldi.

 Advisees

 Esin Durmus (2021-) SAIL Postdoc (w/ Dan Jurafsky)

 Niladri Chatterji (2021-) SAIL Postdoc (w/ Percy Liang)

 Shibani Santurkar (2021-) Postdoc (w/ Percy Liang and Tengyu Ma)

 Daniel Kang (2021-) Graduate Student (w/ Matei Zaharia and Peter Bailis)

 Ishaan Gulrajani (2021-) Graduate Student

 Lisa Li (2021-) Graduate Student (w/ Percy Liang)

 Xuechen Li (2021-) Graduate Student

 Rohan Taori (2021-) Graduate Student

 Tianyi Zhang (2021-) Graduate Student

 Recent Research Areas

 Distributionally Robust Models
 Training methods to make models perform even in the worst-case on a population.
 Diverse Natural Language Generation
 Improving and quantifying the tendency for generation systems to memorize generic statements
 Representations from Random Walks
 Understanding when random-walk based word and graph representations works.

 Publications

 Most recent publications on Google Scholar.

 * Selected and Recent Papers
 * All
 * Stats+ML
 * NLP
 * Comp Bio

 Diffusion-LM Improves Controllable Text Generation PDF

 Lisa Li, John Thickstun, Ishaan Gulrajani, Percy Liang, Tatsunori B Hashimoto

 ArXiv preprint

 Undersampling is a Minimax Optimal Robustness Intervention in Nonparametric Classification PDF

 Niladri Chatterji, Saminul Haque, Tatsunori B Hashimoto

 ArXiv preprint

 TempLM: Distilling Language Models into Template-Based Generators PDF

 Tianyi Zhang, Mina Lee, Lisa Li, Ende Shen, Tatsunori B Hashimoto

 ArXiv preprint

 Jury learning: Integrating dissenting voices into machine learning models PDF

 Mitchell Gordon, Michelle Lam, Joon Park, Kayur Patel, Jeff Hancock, Tatsunori B Hashimoto, Michael Bernstein

 Conference on Human Factors in Computing Systems (CHI 2022, Best paper)

 Spurious Correlations in Reference-Free Evaluation of Text Generation PDF

 Esin Durmus, Faisal Ladhak, Tatsunori B Hashimoto

 Annual Meeting of the Association of Computational Linguistics (ACL 2022)

 Large Language Models Can Be Strong Differentially Private Learners PDF

 Xuechen Li, Florian Tramer, Percy Liang, Tatsunori B Hashimoto

 International Conference on Learning Representations (ICLR 2022 Oral)

 Language Modeling via Stochastic Processes PDF

 Rose E Wang, Esin Durmus, Noah Goodman, Tatsunori B Hashimoto

 International Conference on Learning Representations (ICLR 2022 Oral)

 Is Importance Weighting Incompatible with Interpolating Classifiers? PDF

 Ke A Wang, Niladri S Chatterji, Saminul Haque, Tatsunori B Hashimoto

 International Conference on Learning Representations (ICLR 2022)

 Distributionally Robust Models with Parametric Likelihood Ratios PDF

 Paul Michel, Tatsunori B Hashimoto, Graham Neubig

 International Conference on Learning Representations (ICLR 2022)

 Extending the WILDS Benchmark for Unsupervised Adaptation PDF

 Shiori Sagawa, Pang Wei Koh [...] Tatsunori Hashimoto, Sergey Levine, Chelsea Finn, Percy Liang

 International Conference on Learning Representations (ICLR 2022 Oral)

 Model Performance Scaling with Multiple Data Sources PDF

 Tatsunori B Hashimoto

 International Conference on Machine Learning (ICML 2021)

 DReCa: A general task augmentation strategy for few-shot natural language inference PDF

 Shikhar Murty, Tatsunori B Hashimoto, Christopher D Manning

 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2021)

 On the Inductive Bias of Masked Language Modeling: From Statistical to Syntactic Dependencies PDF

 Tianyi Zhang, Tatsunori B Hashimoto

 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2021)

 Modeling the Second Player in Distributionally Robust Optimization PDF

 Paul Michel, Tatsunori B. Hashimoto, Graham Neubig

 International Conference on Learning Representations (ICLR 2021)

 Approximate Selection with Guarantees using Proxies PDF

 Daniel Kang*, Edward Gan*, Peter Bailis, Tatsunori B Hashimoto, Matei Zaharia

 International Conference on Very Large Data Bases (VLDB 2020)

 Improved Natural Language Generation via Loss Truncation PDF

 Daniel Kang, Tatsunori B Hashimoto

 Annual Meeting of the Association of Computational Linguistics (ACL 2020)

 Robustness to Spurious Correlations via Human Annotations PDF

 Megha Srivastava, Tatsunori B Hashimoto, Percy Liang

 International Conference on Machine Learning (ICML 2020)

 Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-case Generalization PDF

 Shiori Sagawa*, Pang Wei Koh*, Tatsunori B Hashimoto, Percy Liang

 International Conference on Learning Representations (ICLR 2020)

 Distributionally Robust Language Modeling PDF

 Yonatan Oren*, Shiori Sagawa *, Tatsunori B Hashimoto *, Percy Liang

 Empirical Methods in Natural Language Processing (EMNLP 2019)

 Distributionally Robust Losses Against Mixture Covariate Shifts PDF

 John C Duchi, Tatsunori B Hashimoto, Hongseok Namkoong

 Preprint

 Unifying Human and Statistical Evaluation for Natural Language Generation PDF

 Tatsunori B Hashimoto*, Hugh Zhang*, Percy Liang

 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2019)

 Generating Sentences by Editing Prototypes PDF

 Kelvin Guu*, Tatsunori B Hashimoto*, Yonatan Oren, Percy Liang

 Transactions of the Association of Computational Linguistics (TACL, presented at ACL 2018)

 Fairness Without Demographics in Repeated Loss Minimization PDF

 Tatsunori B Hashimoto, Megha Srivastava, Hongseok Namkoong, Percy Liang

 Proceedings of the 35th International Conference on Machine Learning (ICML 2018, Best paper runner up)

 Word embeddings as metric recovery in semantic spaces PDF

 Tatsunori B Hashimoto, David Alvarez-Melis, Tommi S Jaakkola

 Transactions of the Association for Computational Linguistics 4 (TACL, presented at ACL 2016)

 Metric recovery from directed unweighted graphs PDF

 Tatsunori B Hashimoto, Yi Sun, Tommi S Jaakkola

 Artificial Intelligence and Statistics (AISTATS 2015), (best poster at NeurIPS 2014 workshop on networks)

 Diffusion-LM Improves Controllable Text Generation PDF

 Lisa Li, John Thickstun, Ishaan Gulrajani, Percy Liang, Tatsunori B Hashimoto

 ArXiv preprint

 Undersampling is a Minimax Optimal Robustness Intervention in Nonparametric Classification PDF

 Niladri Chatterji, Saminul Haque, Tatsunori B Hashimoto

 ArXiv preprint

 TempLM: Distilling Language Models into Template-Based Generators PDF

 Tianyi Zhang, Mina Lee, Lisa Li, Ende Shen, Tatsunori B Hashimoto

 ArXiv preprint

 Jury learning: Integrating dissenting voices into machine learning models PDF

 Mitchell Gordon, Michelle Lam, Joon Park, Kayur Patel, Jeff Hancock, Tatsunori B Hashimoto, Michael Bernstein

 Conference on Human Factors in Computing Systems (CHI 2022, Best paper)

 Spurious Correlations in Reference-Free Evaluation of Text Generation PDF

 Esin Durmus, Faisal Ladhak, Tatsunori B Hashimoto

 Annual Meeting of the Association of Computational Linguistics (ACL 2022)

 Large Language Models Can Be Strong Differentially Private Learners PDF

 Xuechen Li, Florian Tramer, Percy Liang, Tatsunori B Hashimoto

 International Conference on Learning Representations (ICLR 2022 Oral)

 Language Modeling via Stochastic Processes PDF

 Rose E Wang, Esin Durmus, Noah Goodman, Tatsunori B Hashimoto

 International Conference on Learning Representations (ICLR 2022 Oral)

 Is Importance Weighting Incompatible with Interpolating Classifiers? PDF

 Ke A Wang, Niladri S Chatterji, Saminul Haque, Tatsunori B Hashimoto

 International Conference on Learning Representations (ICLR 2022)

 Distributionally Robust Models with Parametric Likelihood Ratios PDF

 Paul Michel, Tatsunori B Hashimoto, Graham Neubig

 International Conference on Learning Representations (ICLR 2022)

 Extending the WILDS Benchmark for Unsupervised Adaptation PDF

 Shiori Sagawa, Pang Wei Koh [...] Tatsunori Hashimoto, Sergey Levine, Chelsea Finn, Percy Liang

 International Conference on Learning Representations (ICLR 2022 Oral)

 On the opportunities and risks of foundation models PDF

 Rishi Bommasani [.. alphabetical authors ..] Tatsunori Hashimoto [...]

 ArXiv preprint

 Accelerating approximate aggregation queries with expensive predicates PDF

 Daniel Kang, John Guibas, Peter Bailis, Tatsunori B Hashimoto, Yi Sun, Matei Zaharia

 ACM Conference on Management of Data (SIGMOD 2022)

 Model Performance Scaling with Multiple Data Sources PDF

 Tatsunori B Hashimoto

 International Conference on Machine Learning (ICML 2021)

 Measuring Conversational Uptake: A Case Study on Student-Teacher Interactions PDF

 Dorottya Demszky, Jing Liu, Zid Mancenido, Julie Cohen, Heather Hill, Dan Jurafsky, Tatsunori B Hashimoto

 Annual Meeting of the Association of Computational Linguistics (ACL 2020)

 DReCa: A general task augmentation strategy for few-shot natural language inference PDF

 Shikhar Murty, Tatsunori B Hashimoto, Christopher D Manning

 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2021)

 On the Inductive Bias of Masked Language Modeling: From Statistical to Syntactic Dependencies PDF

 Tianyi Zhang, Tatsunori B Hashimoto

 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2021)

 Modeling the Second Player in Distributionally Robust Optimization PDF

 Paul Michel, Tatsunori B. Hashimoto, Graham Neubig

 International Conference on Learning Representations (ICLR 2021)

 Task-agnostic Indexes for Deep Learning-based Queries over Unstructured Data PDF

 Daniel Kang, John Guibas, Peter Bailis, Tatsunori Hashimoto, Matei Zaharia

 Preprint

 The Disagreement Deconvolution: Bringing Machine Learning Performance Metrics In Line With Reality PDF

 Mitchell L Gordon, Kaitlyn Zhou, Kayur Patel, Tatsunori B Hashimoto, Michael S Bernstein

 Conference on Human Factors in Computing Systems (CHI 2021)

 Approximate Selection with Guarantees using Proxies PDF

 Daniel Kang*, Edward Gan*, Peter Bailis, Tatsunori B Hashimoto, Matei Zaharia

 International Conference on Very Large Data Bases (VLDB 2020)

 Improved Natural Language Generation via Loss Truncation PDF

 Daniel Kang, Tatsunori B Hashimoto

 Annual Meeting of the Association of Computational Linguistics (ACL 2020)

 Robustness to Spurious Correlations via Human Annotations PDF

 Megha Srivastava, Tatsunori B Hashimoto, Percy Liang

 International Conference on Machine Learning (ICML 2020)

 Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-case Generalization PDF

 Shiori Sagawa*, Pang Wei Koh*, Tatsunori B Hashimoto, Percy Liang

 International Conference on Learning Representations (ICLR 2020)

 Learning Autocomplete Systems as a Communication Game PDF

 Mina Lee, Tatsunori B Hashimoto, Percy Liang

 Emergent Communication Workshop at Neural Information Processing Systems (NeurIPS 2019)

 Distributionally Robust Language Modeling PDF

 Yonatan Oren*, Shiori Sagawa *, Tatsunori B Hashimoto *, Percy Liang

 Empirical Methods in Natural Language Processing (EMNLP 2019)

 Distributionally Robust Losses Against Mixture Covariate Shifts PDF

 John C Duchi, Tatsunori B Hashimoto, Hongseok Namkoong

 Preprint

 Inferring Multidimensional Rates of Aging from Cross-Sectional Data PDF

 Emma Pierson*, Pang Wei Koh *, Tatsunori B Hashimoto *, Daphne Koller, Jure Lesokevic, Nicholas Eriksson, Percy Liang

 22nd International Conference on Artificial Intelligence and Statistics (AISTATS 2019)

 Unifying Human and Statistical Evaluation for Natural Language Generation PDF

 Tatsunori B Hashimoto*, Hugh Zhang*, Percy Liang

 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2019)

 A Retrieve-and-Edit Framework for Predicting Structured Outputs PDF

 Tatsunori B Hashimoto, Kelvin Guu, Yonatan Oren, Percy Liang

 Advances in Neural Information Processing Systems 31 (NeurIPS 2018, Oral)

 Generating Sentences by Editing Prototypes PDF

 Kelvin Guu*, Tatsunori B Hashimoto*, Yonatan Oren, Percy Liang

 Transactions of the Association of Computational Linguistics (TACL, presented at ACL 2018)

 Fairness Without Demographics in Repeated Loss Minimization PDF

 Tatsunori B Hashimoto, Megha Srivastava, Hongseok Namkoong, Percy Liang

 Proceedings of the 35th International Conference on Machine Learning (ICML 2018, Best paper runner up)

 Derivative free optimization via repeated classification PDF

 Tatsunori B Hashimoto, Steve Yadlowsky, John C Duchi

 21st International Conference on Artificial Intelligence and Statistics (AISTATS 2018)

 Unsupervised Transformation Learning via Convex Relaxations PDF

 Tatsunori B Hashimoto, Percy S Liang, John C Duchi

 Advances in Neural Information Processing Systems 30 (NeurIPS 2017)

 Continuous representations and models from random walk diffusion limits PDF

 Tatsunori B Hashimoto

 PhD Thesis, MIT CSAIL, 2016

 Word embeddings as metric recovery in semantic spaces PDF

 Tatsunori B Hashimoto, David Alvarez-Melis, Tommi S Jaakkola

 Transactions of the Association for Computational Linguistics 4 (TACL, presented at ACL 2016)

 Learning Population-Level Diffusions with Generative RNNs PDF

 Tatsunori B Hashimoto, David Gifford, Tommi S Jaakkola

 Proceedings of the 33rd International Conference on Machine Learning (ICML 2016)

 From random walks to distances on unweighted graphs PDF

 Tatsunori B Hashimoto, Yi Sun, Tommi S Jaakkola

 Advances in Neural Information Processing Systems (NeurIPS 2015)

 Metric recovery from directed unweighted graphs PDF

 Tatsunori B Hashimoto, Yi Sun, Tommi S Jaakkola

 Artificial Intelligence and Statistics (AISTATS 2015), (best poster at NeurIPS 2014 workshop on networks)

 A synergistic DNA logic predicts genome-wide chromatin accessibility PDF

 Tatsunori B Hashimoto*, Richard I Sherwood*, Daniel D Kang*, Nisha Rajagopal, Amira A Barkal, Haoyang Zeng, Bart JM Emons, Sharanya Srinivasan, Tommi S Jaakkola, David K Gifford

 Genome research (2016)

 Cas9 Functionally Opens Chromatin PDF

 Amira A Barkal, Sharanya Srinivasan, Tatsunori B Hashimoto, David K Gifford, Richard I Sherwood

 PloS One (2016)

 Cloning-free CRISPR PDF

 Mandana Arbab, Sharanya Srinivasan, Tatsunori B Hashimoto, Niels Geijsen, Richard I Sherwood

 Stem cell reports (2015)

 GERV: a statistical method for generative evaluation of regulatory variants for transcription factor binding PDF

 Haoyang Zeng, Tatsunori B Hashimoto, Daniel D Kang, David K Gifford

 Bioinformatics (2015)

 Long-term persistence and development of induced pancreatic beta cells generated by lineage conversion of acinar cells PDF

 Weida Li, Claudia Cavelti-Weder, Yinying Zhang, Kendell Clement, Scott Donovan, Gabriel Gonzalez, Jiang Zhu, Marianne Stemann, Ke Xu, Tatsunori B Hashimoto, Takatsugu Yamada, Mio Nakanishi, Yuemei Zhang, Samuel Zeng, David Gifford, Alexander Meissner, Gordon Weir, Qiao Zhou

 Nature Biotechnology (2014)

 Universal count correction for high-throughput sequencing PDF

 Tatsunori B Hashimoto, Matthew D Edwards, David K Gifford

 PLoS computational biology (2014)

 Discovery of directional and nondirectional pioneer transcription factors by modeling DNase profile magnitude and shape PDF

 Richard I Sherwood *, Tatsunori B Hashimoto *, Charles W O'donnell *, Sophia Lewis, Amira A Barkal, John Peter Van Hoff, Vivek Karun, Tommi S Jaakkola, David K Gifford

 Nature Biotechnology (2014)

 Quantifying condition-dependent intracellular protein levels enables high-precision fitness estimates PDF

 Kerry A Geiler-Samerotte, Tatsunori B Hashimoto, Michael F Dion, Bogdan A Budnik, Edoardo M Airoldi, D Allan Drummond

 PloS one (2013)

 Lineage-based identification of cellular states and expression programs PDF

 Tatsunori B Hashimoto, Tommi S Jaakkola, Richard Sherwood, Esteban O. Mazzoni, Hynek Wichterle, David Gifford

 Bioinformatics (2012)

 Finding drug discovery rules of thumb with bump hunting PDF

 Tatsunori B Hashimoto, Matthew Segall

 Proceedings of the ACS (2010)

 BFL: a node and edge betweenness based fast layout algorithm for large scale networks. PDF

 Tatsunori B Hashimoto, Masao Nagasaki, Kaname Kojima, Satoru Miyano

 BMC bioinformatics (2009)

 Undersampling is a Minimax Optimal Robustness Intervention in Nonparametric Classification PDF

 Niladri Chatterji, Saminul Haque, Tatsunori B Hashimoto

 ArXiv preprint

 Large Language Models Can Be Strong Differentially Private Learners PDF

 Xuechen Li, Florian Tramer, Percy Liang, Tatsunori B Hashimoto

 International Conference on Learning Representations (ICLR 2022 Oral)

 Is Importance Weighting Incompatible with Interpolating Classifiers? PDF

 Ke A Wang, Niladri S Chatterji, Saminul Haque, Tatsunori B Hashimoto

 International Conference on Learning Representations (ICLR 2022)

 Distributionally Robust Models with Parametric Likelihood Ratios PDF

 Paul Michel, Tatsunori B Hashimoto, Graham Neubig

 International Conference on Learning Representations (ICLR 2022)

 Extending the WILDS Benchmark for Unsupervised Adaptation PDF

 Shiori Sagawa, Pang Wei Koh [...] Tatsunori Hashimoto, Sergey Levine, Chelsea Finn, Percy Liang

 International Conference on Learning Representations (ICLR 2022 Oral)

 Model Performance Scaling with Multiple Data Sources PDF

 Tatsunori B Hashimoto

 International Conference on Machine Learning (ICML 2021)

 Modeling the Second Player in Distributionally Robust Optimization PDF

 Paul Michel, Tatsunori B. Hashimoto, Graham Neubig

 International Conference on Learning Representations (ICLR 2021)

 Task-agnostic Indexes for Deep Learning-based Queries over Unstructured Data PDF

 Daniel Kang, John Guibas, Peter Bailis, Tatsunori Hashimoto, Matei Zaharia

 Preprint

 Approximate Selection with Guarantees using Proxies PDF

 Daniel Kang*, Edward Gan*, Peter Bailis, Tatsunori B Hashimoto, Matei Zaharia

 International Conference on Very Large Data Bases (VLDB 2020)

 Robustness to Spurious Correlations via Human Annotations PDF

 Megha Srivastava, Tatsunori B Hashimoto, Percy Liang

 International Conference on Machine Learning (ICML 2020)

 Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-case Generalization PDF

 Shiori Sagawa*, Pang Wei Koh*, Tatsunori B Hashimoto, Percy Liang

 International Conference on Learning Representations (ICLR 2020)

 Distributionally Robust Losses Against Mixture Covariate Shifts PDF

 John C Duchi, Tatsunori B Hashimoto, Hongseok Namkoong

 Preprint

 A Retrieve-and-Edit Framework for Predicting Structured Outputs PDF

 Tatsunori B Hashimoto, Kelvin Guu, Yonatan Oren, Percy Liang

 Advances in Neural Information Processing Systems 31 (NeurIPS 2018, Oral)

 Fairness Without Demographics in Repeated Loss Minimization PDF

 Tatsunori B Hashimoto, Megha Srivastava, Hongseok Namkoong, Percy Liang

 Proceedings of the 35th International Conference on Machine Learning (ICML 2018, Best paper runner up)

 Derivative free optimization via repeated classification PDF

 Tatsunori B Hashimoto, Steve Yadlowsky, John C Duchi

 21st International Conference on Artificial Intelligence and Statistics (AISTATS 2018)

 Unsupervised Transformation Learning via Convex Relaxations PDF

 Tatsunori B Hashimoto, Percy S Liang, John C Duchi

 Advances in Neural Information Processing Systems 30 (NeurIPS 2017)

 Continuous representations and models from random walk diffusion limits PDF

 Tatsunori B Hashimoto

 PhD Thesis, MIT CSAIL, 2016

 Word embeddings as metric recovery in semantic spaces PDF

 Tatsunori B Hashimoto, David Alvarez-Melis, Tommi S Jaakkola

 Transactions of the Association for Computational Linguistics 4 (TACL, presented at ACL 2016)

 Learning Population-Level Diffusions with Generative RNNs PDF

 Tatsunori B Hashimoto, David Gifford, Tommi S Jaakkola

 Proceedings of the 33rd International Conference on Machine Learning (ICML 2016)

 From random walks to distances on unweighted graphs PDF

 Tatsunori B Hashimoto, Yi Sun, Tommi S Jaakkola

 Advances in Neural Information Processing Systems (NeurIPS 2015)

 Metric recovery from directed unweighted graphs PDF

 Tatsunori B Hashimoto, Yi Sun, Tommi S Jaakkola

 Artificial Intelligence and Statistics (AISTATS 2015), (best poster at NeurIPS 2014 workshop on networks)

 Diffusion-LM Improves Controllable Text Generation PDF

 Lisa Li, John Thickstun, Ishaan Gulrajani, Percy Liang, Tatsunori B Hashimoto

 ArXiv preprint

 TempLM: Distilling Language Models into Template-Based Generators PDF

 Tianyi Zhang, Mina Lee, Lisa Li, Ende Shen, Tatsunori B Hashimoto

 ArXiv preprint

 Jury learning: Integrating dissenting voices into machine learning models PDF

 Mitchell Gordon, Michelle Lam, Joon Park, Kayur Patel, Jeff Hancock, Tatsunori B Hashimoto, Michael Bernstein

 Conference on Human Factors in Computing Systems (CHI 2022, Best paper)

 Spurious Correlations in Reference-Free Evaluation of Text Generation PDF

 Esin Durmus, Faisal Ladhak, Tatsunori B Hashimoto

 Annual Meeting of the Association of Computational Linguistics (ACL 2022)

 Large Language Models Can Be Strong Differentially Private Learners PDF

 Xuechen Li, Florian Tramer, Percy Liang, Tatsunori B Hashimoto

 International Conference on Learning Representations (ICLR 2022 Oral)

 Language Modeling via Stochastic Processes PDF

 Rose E Wang, Esin Durmus, Noah Goodman, Tatsunori B Hashimoto

 International Conference on Learning Representations (ICLR 2022 Oral)

 On the opportunities and risks of foundation models PDF

 Rishi Bommasani [.. alphabetical authors ..] Tatsunori Hashimoto [...]

 ArXiv preprint

 Measuring Conversational Uptake: A Case Study on Student-Teacher Interactions PDF

 Dorottya Demszky, Jing Liu, Zid Mancenido, Julie Cohen, Heather Hill, Dan Jurafsky, Tatsunori B Hashimoto

 Annual Meeting of the Association of Computational Linguistics (ACL 2020)

 DReCa: A general task augmentation strategy for few-shot natural language inference PDF

 Shikhar Murty, Tatsunori B Hashimoto, Christopher D Manning

 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2021)

 On the Inductive Bias of Masked Language Modeling: From Statistical to Syntactic Dependencies PDF

 Tianyi Zhang, Tatsunori B Hashimoto

 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2021)

 The Disagreement Deconvolution: Bringing Machine Learning Performance Metrics In Line With Reality PDF

 Mitchell L Gordon, Kaitlyn Zhou, Kayur Patel, Tatsunori B Hashimoto, Michael S Bernstein

 Conference on Human Factors in Computing Systems (CHI 2021)

 Improved Natural Language Generation via Loss Truncation PDF

 Daniel Kang, Tatsunori B Hashimoto

 Annual Meeting of the Association of Computational Linguistics (ACL 2020)

 Learning Autocomplete Systems as a Communication Game PDF

 Mina Lee, Tatsunori B Hashimoto, Percy Liang

 Emergent Communication Workshop at Neural Information Processing Systems (NeurIPS 2019)

 Distributionally Robust Language Modeling PDF

 Yonatan Oren*, Shiori Sagawa *, Tatsunori B Hashimoto *, Percy Liang

 Empirical Methods in Natural Language Processing (EMNLP 2019)

 Unifying Human and Statistical Evaluation for Natural Language Generation PDF

 Tatsunori B Hashimoto*, Hugh Zhang*, Percy Liang

 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2019)

 A Retrieve-and-Edit Framework for Predicting Structured Outputs PDF

 Tatsunori B Hashimoto, Kelvin Guu, Yonatan Oren, Percy Liang

 Advances in Neural Information Processing Systems 31 (NeurIPS 2018, Oral)

 Generating Sentences by Editing Prototypes PDF

 Kelvin Guu*, Tatsunori B Hashimoto*, Yonatan Oren, Percy Liang

 Transactions of the Association of Computational Linguistics (TACL, presented at ACL 2018)

 Continuous representations and models from random walk diffusion limits PDF

 Tatsunori B Hashimoto

 PhD Thesis, MIT CSAIL, 2016

 Word embeddings as metric recovery in semantic spaces PDF

 Tatsunori B Hashimoto, David Alvarez-Melis, Tommi S Jaakkola

 Transactions of the Association for Computational Linguistics 4 (TACL, presented at ACL 2016)

 Inferring Multidimensional Rates of Aging from Cross-Sectional Data PDF

 Emma Pierson*, Pang Wei Koh *, Tatsunori B Hashimoto *, Daphne Koller, Jure Lesokevic, Nicholas Eriksson, Percy Liang

 22nd International Conference on Artificial Intelligence and Statistics (AISTATS 2019)

 Continuous representations and models from random walk diffusion limits PDF

 Tatsunori B Hashimoto

 PhD Thesis, MIT CSAIL, 2016

 Learning Population-Level Diffusions with Generative RNNs PDF

 Tatsunori B Hashimoto, David Gifford, Tommi S Jaakkola

 Proceedings of the 33rd International Conference on Machine Learning (ICML 2016)

 A synergistic DNA logic predicts genome-wide chromatin accessibility PDF

 Tatsunori B Hashimoto*, Richard I Sherwood*, Daniel D Kang*, Nisha Rajagopal, Amira A Barkal, Haoyang Zeng, Bart JM Emons, Sharanya Srinivasan, Tommi S Jaakkola, David K Gifford

 Genome research (2016)

 Cas9 Functionally Opens Chromatin PDF

 Amira A Barkal, Sharanya Srinivasan, Tatsunori B Hashimoto, David K Gifford, Richard I Sherwood

 PloS One (2016)

 Cloning-free CRISPR PDF

 Mandana Arbab, Sharanya Srinivasan, Tatsunori B Hashimoto, Niels Geijsen, Richard I Sherwood

 Stem cell reports (2015)

 GERV: a statistical method for generative evaluation of regulatory variants for transcription factor binding PDF

 Haoyang Zeng, Tatsunori B Hashimoto, Daniel D Kang, David K Gifford

 Bioinformatics (2015)

 Long-term persistence and development of induced pancreatic beta cells generated by lineage conversion of acinar cells PDF

 Weida Li, Claudia Cavelti-Weder, Yinying Zhang, Kendell Clement, Scott Donovan, Gabriel Gonzalez, Jiang Zhu, Marianne Stemann, Ke Xu, Tatsunori B Hashimoto, Takatsugu Yamada, Mio Nakanishi, Yuemei Zhang, Samuel Zeng, David Gifford, Alexander Meissner, Gordon Weir, Qiao Zhou

 Nature Biotechnology (2014)

 Universal count correction for high-throughput sequencing PDF

 Tatsunori B Hashimoto, Matthew D Edwards, David K Gifford

 PLoS computational biology (2014)

 Discovery of directional and nondirectional pioneer transcription factors by modeling DNase profile magnitude and shape PDF

 Richard I Sherwood *, Tatsunori B Hashimoto *, Charles W O'donnell *, Sophia Lewis, Amira A Barkal, John Peter Van Hoff, Vivek Karun, Tommi S Jaakkola, David K Gifford

 Nature Biotechnology (2014)

 Quantifying condition-dependent intracellular protein levels enables high-precision fitness estimates PDF

 Kerry A Geiler-Samerotte, Tatsunori B Hashimoto, Michael F Dion, Bogdan A Budnik, Edoardo M Airoldi, D Allan Drummond

 PloS one (2013)

 Lineage-based identification of cellular states and expression programs PDF

 Tatsunori B Hashimoto, Tommi S Jaakkola, Richard Sherwood, Esteban O. Mazzoni, Hynek Wichterle, David Gifford

 Bioinformatics (2012)

 Finding drug discovery rules of thumb with bump hunting PDF

 Tatsunori B Hashimoto, Matthew Segall

 Proceedings of the ACS (2010)

 BFL: a node and edge betweenness based fast layout algorithm for large scale networks. PDF

 Tatsunori B Hashimoto, Masao Nagasaki, Kaname Kojima, Satoru Miyano

 BMC bioinformatics (2009)

 Teaching

 CS324 (Winter 2022): Large Language Models

 CS329D (Fall 2021, Spring 2021): Machine Learning Under Distribution Shifts

 CS221 (Winter 2021): Artificial Intelligence: Principles and Techniques

 Talks and slides

 CMU ML Seminar / Princeton Theory Seminar (Fall 2022): Is Importance Weighting Incompatible with Interpolating Classifiers?

 Evaluations and Assessments of Neural Conversation Systems (EANCS) (EMNLP 2021): Pitfalls in reference-free evaluations

 Translational Data Analytics Forum (OSU, Fall 2021): On the theory of foundation models and their application to privacy-preserving NLP

 Stanford CRFM workshop (Stanford 2021): On the Inductive Bias of Masked Language Modeling

 Socially responsible ML Workshop (ICML 2021): Not All Uncertainty is Noise: Machine Learning with Confounders and Disagreements

 Neyman Seminar (Berkeley Statistics, 8/28, 2020): Statistical Challenges in Understanding and Improving Natural Language Generation

 NeuralGen Workshop (NAACL 2019): Defining and Evaluating Diversity in Generation

 Resume

 * Stanford CS 2020 - now
 Assistant professor
 * Microsoft Semantic Machines 2019 - 2020
 Researcher
 * Stanford CS / Statistics 2016 - 2019
 Postdoc
 Working for Percy Liang + John Duchi
 * MIT CSAIL 2011 - 2016
 Ph.D.
 Advised by Tommi Jaakkola + David Gifford
 * RIKEN BSI 2007 - 2011
 Intern
 Laboratory for neural circuit theory
 * Optibrium LTD Summer 2010
 Summer Intern
 ML engineer
 * Harvard University 2007 - 2011
 Undergraduate
 Cum laude and departmental honors in Statistics and Math. Advised by Edoardo Airoldi.
 * U. Tokyo Institute of Medical Science 2006 - 2007
 Intern and Translator
 Miyano research lab

 Acknowledgement

 This website uses the website design and template by Martin Saveski

 Tatsunori Hashimoto
 thashim [AT] stanford.edu
Diffusion-LM Improves Controllable Text Generation
 
 PDF
 
 
 
 
 
 
 Lisa Li, John Thickstun, Ishaan Gulrajani, Percy Liang, Tatsunori B Hashimoto
  ArXiv preprint 
 
 
 
 
  Undersampling is a Minimax Optimal Robustness Intervention in Nonparametric Classification
 
 PDF
 
 
 
 
 
 
 Niladri Chatterji, Saminul Haque, Tatsunori B Hashimoto
  ArXiv preprint 
 
 
 
 
  TempLM: Distilling Language Models into Template-Based Generators
 
 PDF
 
 
 
 
 
 
 Tianyi Zhang, Mina Lee, Lisa Li, Ende Shen, Tatsunori B Hashimoto
  ArXiv preprint 
 
 
 
 
  Jury learning: Integrating dissenting voices into machine learning models
 
 PDF
 
 
 
 
 
 
 Mitchell Gordon, Michelle Lam, Joon Park, Kayur Patel, Jeff Hancock, Tatsunori B Hashimoto, Michael Bernstein
  Conference on Human Factors in Computing Systems (CHI 2022, Best paper) 
 
 
 
 
  Spurious Correlations in Reference-Free Evaluation of Text Generation
 
 PDF
 
 
 
 
 
 
 Esin Durmus, Faisal Ladhak, Tatsunori B Hashimoto
  Annual Meeting of the Association of Computational Linguistics (ACL 2022) 
 
 
 
 
  Large Language Models Can Be Strong Differentially Private Learners
 
 PDF
 
 
 
 
 
 
 Xuechen Li, Florian Tramer, Percy Liang, Tatsunori B Hashimoto
  International Conference on Learning Representations (ICLR 2022 Oral) 
 
 
 
 
  Language Modeling via Stochastic Processes
 
 PDF
 
 
 
 
 
 
 Rose E Wang, Esin Durmus, Noah Goodman, Tatsunori B Hashimoto
  International Conference on Learning Representations (ICLR 2022 Oral) 
 
 
 
 
  Is Importance Weighting Incompatible with Interpolating Classifiers?
 
 PDF
 
 
 
 
 
 
 Ke A Wang, Niladri S Chatterji, Saminul Haque, Tatsunori B Hashimoto
  International Conference on Learning Representations (ICLR 2022) 
 
 
 
 
  Distributionally Robust Models with Parametric Likelihood Ratios
 
 PDF
 
 
 
 
 
 
 Paul Michel, Tatsunori B Hashimoto, Graham Neubig
  International Conference on Learning Representations (ICLR 2022) 
 
 
 
 
  Extending the WILDS Benchmark for Unsupervised Adaptation
 
 PDF
 
 
 
 
 
 
 Shiori Sagawa, Pang Wei Koh [...] Tatsunori Hashimoto, Sergey Levine, Chelsea Finn, Percy Liang
  International Conference on Learning Representations (ICLR 2022 Oral) 
 
 
 
 
  Model Performance Scaling with Multiple Data Sources
 
 PDF
 
 
 
 
 
 
 Tatsunori B Hashimoto
  International Conference on Machine Learning (ICML 2021) 
 
 
 
 
  DReCa: A general task augmentation strategy for few-shot natural language inference
 
 PDF
 
 
 
 
 
 
 Shikhar Murty, Tatsunori B Hashimoto, Christopher D Manning
  Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2021) 
 
 
 
 
  On the Inductive Bias of Masked Language Modeling: From Statistical to Syntactic Dependencies
 
 PDF
 
 
 
 
 
 
 Tianyi Zhang, Tatsunori B Hashimoto
  Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2021) 
 
 
 
 
  Modeling the Second Player in Distributionally Robust Optimization
 
 PDF
 
 
 
 
 
 
 Paul Michel, Tatsunori B. Hashimoto, Graham Neubig
  International Conference on Learning Representations (ICLR 2021) 
 
 
 
 
  Approximate Selection with Guarantees using Proxies
 
 PDF
 
 
 
 
 
 
 Daniel Kang*, Edward Gan*, Peter Bailis, Tatsunori B Hashimoto, Matei Zaharia
  International Conference on Very Large Data Bases (VLDB 2020) 
 
 
 
 
  Improved Natural Language Generation via Loss Truncation
 
 PDF
 
 
 
 
 
 
 Daniel Kang, Tatsunori B Hashimoto
  Annual Meeting of the Association of Computational Linguistics (ACL 2020) 
 
 
 
 
  Robustness to Spurious Correlations via Human Annotations
 
 PDF
 
 
 
 
 
 
 Megha Srivastava, Tatsunori B Hashimoto, Percy Liang
  International Conference on Machine Learning (ICML 2020) 
 
 
 
 
  Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-case Generalization
 
 PDF
 
 
 
 
 
 
 Shiori Sagawa*, Pang Wei Koh*, Tatsunori B Hashimoto, Percy Liang
  International Conference on Learning Representations (ICLR 2020) 
 
 
 
 
  Distributionally Robust Language Modeling
 
 PDF
 
 
 
 
 
 
 Yonatan Oren*, Shiori Sagawa *, Tatsunori B Hashimoto *, Percy Liang
  Empirical Methods in Natural Language Processing (EMNLP 2019) 
 
 
 
 
  Distributionally Robust Losses Against Mixture Covariate Shifts
 
 PDF
 
 
 
 
 
 
 John C Duchi, Tatsunori B Hashimoto, Hongseok Namkoong
  Preprint 
 
 
 
 
  Unifying Human and Statistical Evaluation for Natural Language Generation
 
 PDF
 
 
 
 
 
 
 Tatsunori B Hashimoto*, Hugh Zhang*, Percy Liang
  Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2019) 
 
 
 
 
  Generating Sentences by Editing Prototypes
 
 PDF
 
 
 
 
 
 
 Kelvin Guu*, Tatsunori B Hashimoto*, Yonatan Oren, Percy Liang
  Transactions of the Association of Computational Linguistics (TACL, presented at ACL 2018) 
 
 
 
 
  Fairness Without Demographics in Repeated Loss Minimization
 
 PDF
 
 
 
 
 
 
 Tatsunori B Hashimoto, Megha Srivastava, Hongseok Namkoong, Percy Liang
  Proceedings of the 35th International Conference on Machine Learning (ICML 2018, Best paper runner up) 
 
 
 
 
  Word embeddings as metric recovery in semantic spaces
 
 PDF
 
 
 
 
 
 
 Tatsunori B Hashimoto, David Alvarez-Melis, Tommi S Jaakkola
  Transactions of the Association for Computational Linguistics 4 (TACL, presented at ACL 2016) 
 
 
 
 
  Metric recovery from directed unweighted graphs
 
 PDF
 
 
 
 
 
 
 Tatsunori B Hashimoto, Yi Sun, Tommi S Jaakkola
  Artificial Intelligence and Statistics (AISTATS 2015), (best poster at NeurIPS 2014 workshop on networks) 
 
 
 
  
 
 
 
  Diffusion-LM Improves Controllable Text Generation
 
 PDF
 
 
 
 
 
 
 Lisa Li, John Thickstun, Ishaan Gulrajani, Percy Liang, Tatsunori B Hashimoto
  ArXiv preprint 
 
 
 
 
  Undersampling is a Minimax Optimal Robustness Intervention in Nonparametric Classification
 
 PDF
 
 
 
 
 
 
 Niladri Chatterji, Saminul Haque, Tatsunori B Hashimoto
  ArXiv preprint 
 
 
 
 
  TempLM: Distilling Language Models into Template-Based Generators
 
 PDF
 
 
 
 
 
 
 Tianyi Zhang, Mina Lee, Lisa Li, Ende Shen, Tatsunori B Hashimoto
  ArXiv preprint 
 
 
 
 
  Jury learning: Integrating dissenting voices into machine learning models
 
 PDF
 
 
 
 
 
 
 Mitchell Gordon, Michelle Lam, Joon Park, Kayur Patel, Jeff Hancock, Tatsunori B Hashimoto, Michael Bernstein
  Conference on Human Factors in Computing Systems (CHI 2022, Best paper) 
 
 
 
 
  Spurious Correlations in Reference-Free Evaluation of Text Generation
 
 PDF
 
 
 
 
 
 
 Esin Durmus, Faisal Ladhak, Tatsunori B Hashimoto
  Annual Meeting of the Association of Computational Linguistics (ACL 2022) 
 
 
 
 
  Large Language Models Can Be Strong Differentially Private Learners
 
 PDF
 
 
 
 
 
 
 Xuechen Li, Florian Tramer, Percy Liang, Tatsunori B Hashimoto
  International Conference on Learning Representations (ICLR 2022 Oral) 
 
 
 
 
  Language Modeling via Stochastic Processes
 
 PDF
 
 
 
 
 
 
 Rose E Wang, Esin Durmus, Noah Goodman, Tatsunori B Hashimoto
  International Conference on Learning Representations (ICLR 2022 Oral) 
 
 
 
 
  Is Importance Weighting Incompatible with Interpolating Classifiers?
 
 PDF
 
 
 
 
 
 
 Ke A Wang, Niladri S Chatterji, Saminul Haque, Tatsunori B Hashimoto
  International Conference on Learning Representations (ICLR 2022) 
 
 
 
 
  Distributionally Robust Models with Parametric Likelihood Ratios
 
 PDF
 
 
 
 
 
 
 Paul Michel, Tatsunori B Hashimoto, Graham Neubig
  International Conference on Learning Representations (ICLR 2022) 
 
 
 
 
  Extending the WILDS Benchmark for Unsupervised Adaptation
 
 PDF
 
 
 
 
 
 
 Shiori Sagawa, Pang Wei Koh [...] Tatsunori Hashimoto, Sergey Levine, Chelsea Finn, Percy Liang
  International Conference on Learning Representations (ICLR 2022 Oral) 
 
 
 
 
  On the opportunities and risks of foundation models
 
 PDF
 
 
 
 
 
 
 Rishi Bommasani [.. alphabetical authors ..] Tatsunori Hashimoto [...]
  ArXiv preprint 
 
 
 
 
  Accelerating approximate aggregation queries with expensive predicates
 
 PDF
 
 
 
 
 
 
 Daniel Kang, John Guibas, Peter Bailis, Tatsunori B Hashimoto, Yi Sun, Matei Zaharia
  ACM Conference on Management of Data (SIGMOD 2022) 
 
 
 
 
  Model Performance Scaling with Multiple Data Sources
 
 PDF
 
 
 
 
 
 
 Tatsunori B Hashimoto
  International Conference on Machine Learning (ICML 2021) 
 
 
 
 
  Measuring Conversational Uptake: A Case Study on Student-Teacher Interactions
 
 PDF
 
 
 
 
 
 
 Dorottya Demszky, Jing Liu, Zid Mancenido, Julie Cohen, Heather Hill, Dan Jurafsky, Tatsunori B Hashimoto
  Annual Meeting of the Association of Computational Linguistics (ACL 2020) 
 
 
 
 
  DReCa: A general task augmentation strategy for few-shot natural language inference
 
 PDF
 
 
 
 
 
 
 Shikhar Murty, Tatsunori B Hashimoto, Christopher D Manning
  Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2021) 
 
 
 
 
  On the Inductive Bias of Masked Language Modeling: From Statistical to Syntactic Dependencies
 
 PDF
 
 
 
 
 
 
 Tianyi Zhang, Tatsunori B Hashimoto
  Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2021) 
 
 
 
 
  Modeling the Second Player in Distributionally Robust Optimization
 
 PDF
 
 
 
 
 
 
 Paul Michel, Tatsunori B. Hashimoto, Graham Neubig
  International Conference on Learning Representations (ICLR 2021) 
 
 
 
 
  Task-agnostic Indexes for Deep Learning-based Queries over Unstructured Data
 
 PDF
 
 
 
 
 
 
 Daniel Kang, John Guibas, Peter Bailis, Tatsunori Hashimoto, Matei Zaharia
  Preprint 
 
 
 
 
  The Disagreement Deconvolution: Bringing Machine Learning Performance Metrics In Line With Reality
 
 PDF
 
 
 
 
 
 
 Mitchell L Gordon, Kaitlyn Zhou, Kayur Patel, Tatsunori B Hashimoto, Michael S Bernstein
  Conference on Human Factors in Computing Systems (CHI 2021) 
 
 
 
 
  Approximate Selection with Guarantees using Proxies
 
 PDF
 
 
 
 
 
 
 Daniel Kang*, Edward Gan*, Peter Bailis, Tatsunori B Hashimoto, Matei Zaharia
  International Conference on Very Large Data Bases (VLDB 2020) 
 
 
 
 
  Improved Natural Language Generation via Loss Truncation
 
 PDF
 
 
 
 
 
 
 Daniel Kang, Tatsunori B Hashimoto
  Annual Meeting of the Association of Computational Linguistics (ACL 2020) 
 
 
 
 
  Robustness to Spurious Correlations via Human Annotations
 
 PDF
 
 
 
 
 
 
 Megha Srivastava, Tatsunori B Hashimoto, Percy Liang
  International Conference on Machine Learning (ICML 2020) 
 
 
 
 
  Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-case Generalization
 
 PDF
 
 
 
 
 
 
 Shiori Sagawa*, Pang Wei Koh*, Tatsunori B Hashimoto, Percy Liang
  International Conference on Learning Representations (ICLR 2020) 
 
 
 
 
  Learning Autocomplete Systems as a Communication Game
 
 PDF
 
 
 
 
 
 
 Mina Lee, Tatsunori B Hashimoto, Percy Liang
  Emergent Communication Workshop at Neural Information Processing Systems (NeurIPS 2019) 
 
 
 
 
  Distributionally Robust Language Modeling
 
 PDF
 
 
 
 
 
 
 Yonatan Oren*, Shiori Sagawa *, Tatsunori B Hashimoto *, Percy Liang
  Empirical Methods in Natural Language Processing (EMNLP 2019) 
 
 
 
 
  Distributionally Robust Losses Against Mixture Covariate Shifts
 
 PDF
 
 
 
 
 
 
 John C Duchi, Tatsunori B Hashimoto, Hongseok Namkoong
  Preprint 
 
 
 
 
  Inferring Multidimensional Rates of Aging from Cross-Sectional Data
 
 PDF
 
 
 
 
 
 
 Emma Pierson*, Pang Wei Koh *, Tatsunori B Hashimoto *, Daphne Koller, Jure Lesokevic, Nicholas Eriksson, Percy Liang
  22nd International Conference on Artificial Intelligence and Statistics (AISTATS 2019) 
 
 
 
 
  Unifying Human and Statistical Evaluation for Natural Language Generation
 
 PDF
 
 
 
 
 
 
 Tatsunori B Hashimoto*, Hugh Zhang*, Percy Liang
  Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2019) 
 
 
 
 
  A Retrieve-and-Edit Framework for Predicting Structured Outputs
 
 PDF
 
 
 
 
 
 
 Tatsunori B Hashimoto, Kelvin Guu, Yonatan Oren, Percy Liang
  Advances in Neural Information Processing Systems 31 (NeurIPS 2018, Oral) 
 
 
 
 
  Generating Sentences by Editing Prototypes
 
 PDF
 
 
 
 
 
 
 Kelvin Guu*, Tatsunori B Hashimoto*, Yonatan Oren, Percy Liang
  Transactions of the Association of Computational Linguistics (TACL, presented at ACL 2018) 
 
 
 
 
  Fairness Without Demographics in Repeated Loss Minimization
 
 PDF
 
 
 
 
 
 
 Tatsunori B Hashimoto, Megha Srivastava, Hongseok Namkoong, Percy Liang
  Proceedings of the 35th International Conference on Machine Learning (ICML 2018, Best paper runner up) 
 
 
 
 
  Derivative free optimization via repeated classification
 
 PDF
 
 
 
 
 
 
 Tatsunori B Hashimoto, Steve Yadlowsky, John C Duchi
  21st International Conference on Artificial Intelligence and Statistics (AISTATS 2018) 
 
 
 
 
  Unsupervised Transformation Learning via Convex Relaxations
 
 PDF
 
 
 
 
 
 
 Tatsunori B Hashimoto, Percy S Liang, John C Duchi
  Advances in Neural Information Processing Systems 30 (NeurIPS 2017) 
 
 
 
 
  Continuous representations and models from random walk diffusion limits
 
 PDF
 
 
 
 
 
 
 Tatsunori B Hashimoto
  PhD Thesis, MIT CSAIL, 2016 
 
 
 
 
  Word embeddings as metric recovery in semantic spaces
 
 PDF
 
 
 
 
 
 
 Tatsunori B Hashimoto, David Alvarez-Melis, Tommi S Jaakkola
  Transactions of the Association for Computational Linguistics 4 (TACL, presented at ACL 2016) 
 
 
 
 
  Learning Population-Level Diffusions with Generative RNNs
 
 PDF
 
 
 
 
 
 
 Tatsunori B Hashimoto, David Gifford, Tommi S Jaakkola
  Proceedings of the 33rd International Conference on Machine Learning (ICML 2016) 
 
 
 
 
  From random walks to distances on unweighted graphs
 
 PDF
 
 
 
 
 
 
 Tatsunori B Hashimoto, Yi Sun, Tommi S Jaakkola
  Advances in Neural Information Processing Systems (NeurIPS 2015) 
 
 
 
 
  Metric recovery from directed unweighted graphs
 
 PDF
 
 
 
 
 
 
 Tatsunori B Hashimoto, Yi Sun, Tommi S Jaakkola
  Artificial Intelligence and Statistics (AISTATS 2015), (best poster at NeurIPS 2014 workshop on networks) 
 
 
 
 
  A synergistic DNA logic predicts genome-wide chromatin accessibility
 
 PDF
 
 
 
 
 
 
 Tatsunori B Hashimoto*, Richard I Sherwood*, Daniel D Kang*, Nisha Rajagopal, Amira A Barkal, Haoyang Zeng, Bart JM Emons, Sharanya Srinivasan, Tommi S Jaakkola, David K Gifford
  Genome research (2016) 
 
 
 
 
  Cas9 Functionally Opens Chromatin
 
 PDF
 
 
 
 
 
 
 Amira A Barkal, Sharanya Srinivasan, Tatsunori B Hashimoto, David K Gifford, Richard I Sherwood
  PloS One (2016) 
 
 
 
 
  Cloning-free CRISPR
 
 PDF
 
 
 
 
 
 
 Mandana Arbab, Sharanya Srinivasan, Tatsunori B Hashimoto, Niels Geijsen, Richard I Sherwood
  Stem cell reports (2015) 
 
 
 
 
  GERV: a statistical method for generative evaluation of regulatory variants for transcription factor binding
 
 PDF
 
 
 
 
 
 
 Haoyang Zeng, Tatsunori B Hashimoto, Daniel D Kang, David K Gifford
  Bioinformatics (2015) 
 
 
 
 
  Long-term persistence and development of induced pancreatic beta cells generated by lineage conversion of acinar cells
 
 PDF
 
 
 
 
 
 
 Weida Li, Claudia Cavelti-Weder, Yinying Zhang, Kendell Clement, Scott Donovan, Gabriel Gonzalez, Jiang Zhu, Marianne Stemann, Ke Xu, Tatsunori B Hashimoto, Takatsugu Yamada, Mio Nakanishi, Yuemei Zhang, Samuel Zeng, David Gifford, Alexander Meissner, Gordon Weir, Qiao Zhou
  Nature Biotechnology (2014) 
 
 
 
 
  Universal count correction for high-throughput sequencing
 
 PDF
 
 
 
 
 
 
 Tatsunori B Hashimoto, Matthew D Edwards, David K Gifford
  PLoS computational biology (2014) 
 
 
 
 
  Discovery of directional and nondirectional pioneer transcription factors by modeling DNase profile magnitude and shape
 
 PDF
 
 
 
 
 
 
 Richard I Sherwood *, Tatsunori B Hashimoto *, Charles W O'donnell *, Sophia Lewis, Amira A Barkal, John Peter Van Hoff, Vivek Karun, Tommi S Jaakkola, David K Gifford
  Nature Biotechnology (2014) 
 
 
 
 
  Quantifying condition-dependent intracellular protein levels enables high-precision fitness estimates
 
 PDF
 
 
 
 
 
 
 Kerry A Geiler-Samerotte, Tatsunori B Hashimoto, Michael F Dion, Bogdan A Budnik, Edoardo M Airoldi, D Allan Drummond
  PloS one (2013) 
 
 
 
 
  Lineage-based identification of cellular states and expression programs
 
 PDF
 
 
 
 
 
 
 Tatsunori B Hashimoto, Tommi S Jaakkola, Richard Sherwood, Esteban O. Mazzoni, Hynek Wichterle, David Gifford
  Bioinformatics (2012) 
 
 
 
 
  Finding drug discovery rules of thumb with bump hunting
 
 PDF
 
 
 
 
 
 
 Tatsunori B Hashimoto, Matthew Segall
  Proceedings of the ACS (2010) 
 
 
 
 
  BFL: a node and edge betweenness based fast layout algorithm for large scale networks.
 
 PDF
 
 
 
 
 
 
 Tatsunori B Hashimoto, Masao Nagasaki, Kaname Kojima, Satoru Miyano
  BMC bioinformatics (2009) 
 
 
 
  
 
 
 
  Undersampling is a Minimax Optimal Robustness Intervention in Nonparametric Classification
 
 PDF
 
 
 
 
 
 
 Niladri Chatterji, Saminul Haque, Tatsunori B Hashimoto
  ArXiv preprint 
 
 
 
 
  Large Language Models Can Be Strong Differentially Private Learners
 
 PDF
 
 
 
 
 
 
 Xuechen Li, Florian Tramer, Percy Liang, Tatsunori B Hashimoto
  International Conference on Learning Representations (ICLR 2022 Oral) 
 
 
 
 
  Is Importance Weighting Incompatible with Interpolating Classifiers?
 
 PDF
 
 
 
 
 
 
 Ke A Wang, Niladri S Chatterji, Saminul Haque, Tatsunori B Hashimoto
  International Conference on Learning Representations (ICLR 2022) 
 
 
 
 
  Distributionally Robust Models with Parametric Likelihood Ratios
 
 PDF
 
 
 
 
 
 
 Paul Michel, Tatsunori B Hashimoto, Graham Neubig
  International Conference on Learning Representations (ICLR 2022) 
 
 
 
 
  Extending the WILDS Benchmark for Unsupervised Adaptation
 
 PDF
 
 
 
 
 
 
 Shiori Sagawa, Pang Wei Koh [...] Tatsunori Hashimoto, Sergey Levine, Chelsea Finn, Percy Liang
  International Conference on Learning Representations (ICLR 2022 Oral) 
 
 
 
 
  Model Performance Scaling with Multiple Data Sources
 
 PDF
 
 
 
 
 
 
 Tatsunori B Hashimoto
  International Conference on Machine Learning (ICML 2021) 
 
 
 
 
  Modeling the Second Player in Distributionally Robust Optimization
 
 PDF
 
 
 
 
 
 
 Paul Michel, Tatsunori B. Hashimoto, Graham Neubig
  International Conference on Learning Representations (ICLR 2021) 
 
 
 
 
  Task-agnostic Indexes for Deep Learning-based Queries over Unstructured Data
 
 PDF
 
 
 
 
 
 
 Daniel Kang, John Guibas, Peter Bailis, Tatsunori Hashimoto, Matei Zaharia
  Preprint 
 
 
 
 
  Approximate Selection with Guarantees using Proxies
 
 PDF
 
 
 
 
 
 
 Daniel Kang*, Edward Gan*, Peter Bailis, Tatsunori B Hashimoto, Matei Zaharia
  International Conference on Very Large Data Bases (VLDB 2020) 
 
 
 
 
  Robustness to Spurious Correlations via Human Annotations
 
 PDF
 
 
 
 
 
 
 Megha Srivastava, Tatsunori B Hashimoto, Percy Liang
  International Conference on Machine Learning (ICML 2020) 
 
 
 
 
  Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-case Generalization
 
 PDF
 
 
 
 
 
 
 Shiori Sagawa*, Pang Wei Koh*, Tatsunori B Hashimoto, Percy Liang
  International Conference on Learning Representations (ICLR 2020) 
 
 
 
 
  Distributionally Robust Losses Against Mixture Covariate Shifts
 
 PDF
 
 
 
 
 
 
 John C Duchi, Tatsunori B Hashimoto, Hongseok Namkoong
  Preprint 
 
 
 
 
  A Retrieve-and-Edit Framework for Predicting Structured Outputs
 
 PDF
 
 
 
 
 
 
 Tatsunori B Hashimoto, Kelvin Guu, Yonatan Oren, Percy Liang
  Advances in Neural Information Processing Systems 31 (NeurIPS 2018, Oral) 
 
 
 
 
  Fairness Without Demographics in Repeated Loss Minimization
 
 PDF
 
 
 
 
 
 
 Tatsunori B Hashimoto, Megha Srivastava, Hongseok Namkoong, Percy Liang
  Proceedings of the 35th International Conference on Machine Learning (ICML 2018, Best paper runner up) 
 
 
 
 
  Derivative free optimization via repeated classification
 
 PDF
 
 
 
 
 
 
 Tatsunori B Hashimoto, Steve Yadlowsky, John C Duchi
  21st International Conference on Artificial Intelligence and Statistics (AISTATS 2018) 
 
 
 
 
  Unsupervised Transformation Learning via Convex Relaxations
 
 PDF
 
 
 
 
 
 
 Tatsunori B Hashimoto, Percy S Liang, John C Duchi
  Advances in Neural Information Processing Systems 30 (NeurIPS 2017) 
 
 
 
 
  Continuous representations and models from random walk diffusion limits
 
 PDF
 
 
 
 
 
 
 Tatsunori B Hashimoto
  PhD Thesis, MIT CSAIL, 2016 
 
 
 
 
  Word embeddings as metric recovery in semantic spaces
 
 PDF
 
 
 
 
 
 
 Tatsunori B Hashimoto, David Alvarez-Melis, Tommi S Jaakkola
  Transactions of the Association for Computational Linguistics 4 (TACL, presented at ACL 2016) 
 
 
 
 
  Learning Population-Level Diffusions with Generative RNNs
 
 PDF
 
 
 
 
 
 
 Tatsunori B Hashimoto, David Gifford, Tommi S Jaakkola
  Proceedings of the 33rd International Conference on Machine Learning (ICML 2016) 
 
 
 
 
  From random walks to distances on unweighted graphs
 
 PDF
 
 
 
 
 
 
 Tatsunori B Hashimoto, Yi Sun, Tommi S Jaakkola
  Advances in Neural Information Processing Systems (NeurIPS 2015) 
 
 
 
 
  Metric recovery from directed unweighted graphs
 
 PDF
 
 
 
 
 
 
 Tatsunori B Hashimoto, Yi Sun, Tommi S Jaakkola
  Artificial Intelligence and Statistics (AISTATS 2015), (best poster at NeurIPS 2014 workshop on networks) 
 
 
 
  
 
 
 
  Diffusion-LM Improves Controllable Text Generation
 
 PDF
 
 
 
 
 
 
 Lisa Li, John Thickstun, Ishaan Gulrajani, Percy Liang, Tatsunori B Hashimoto
  ArXiv preprint 
 
 
 
  TempLM: Distilling Language Models into Template-Based Generators
 
 PDF
 
 
 
 
 
 
 Tianyi Zhang, Mina Lee, Lisa Li, Ende Shen, Tatsunori B Hashimoto
  ArXiv preprint 
 
 
 
  Jury learning: Integrating dissenting voices into machine learning models
 
 PDF
 
 
 
 
 
 
 Mitchell Gordon, Michelle Lam, Joon Park, Kayur Patel, Jeff Hancock, Tatsunori B Hashimoto, Michael Bernstein
  Conference on Human Factors in Computing Systems (CHI 2022, Best paper) 
 
 
 
  Spurious Correlations in Reference-Free Evaluation of Text Generation
 
 PDF
 
 
 
 
 
 
 Esin Durmus, Faisal Ladhak, Tatsunori B Hashimoto
  Annual Meeting of the Association of Computational Linguistics (ACL 2022) 
 
 
 
  Large Language Models Can Be Strong Differentially Private Learners
 
 PDF
 
 
 
 
 
 
 Xuechen Li, Florian Tramer, Percy Liang, Tatsunori B Hashimoto
  International Conference on Learning Representations (ICLR 2022 Oral) 
 
 
 
  Language Modeling via Stochastic Processes
 
 PDF
 
 
 
 
 
 
 Rose E Wang, Esin Durmus, Noah Goodman, Tatsunori B Hashimoto
  International Conference on Learning Representations (ICLR 2022 Oral) 
 
 
 
  On the opportunities and risks of foundation models
 
 PDF
 
 
 
 
 
 
 Rishi Bommasani [.. alphabetical authors ..] Tatsunori Hashimoto [...]
  ArXiv preprint 
 
 
 
  Measuring Conversational Uptake: A Case Study on Student-Teacher Interactions
 
 PDF
 
 
 
 
 
 
 Dorottya Demszky, Jing Liu, Zid Mancenido, Julie Cohen, Heather Hill, Dan Jurafsky, Tatsunori B Hashimoto
  Annual Meeting of the Association of Computational Linguistics (ACL 2020) 
 
 
 
  DReCa: A general task augmentation strategy for few-shot natural language inference
 
 PDF
 
 
 
 
 
 
 Shikhar Murty, Tatsunori B Hashimoto, Christopher D Manning
  Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2021) 
 
 
 
  On the Inductive Bias of Masked Language Modeling: From Statistical to Syntactic Dependencies
 
 PDF
 
 
 
 
 
 
 Tianyi Zhang, Tatsunori B Hashimoto
  Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2021) 
 
 
 
  The Disagreement Deconvolution: Bringing Machine Learning Performance Metrics In Line With Reality
 
 PDF
 
 
 
 
 
 
 Mitchell L Gordon, Kaitlyn Zhou, Kayur Patel, Tatsunori B Hashimoto, Michael S Bernstein
  Conference on Human Factors in Computing Systems (CHI 2021) 
 
 
 
  Improved Natural Language Generation via Loss Truncation
 
 PDF
 
 
 
 
 
 
 Daniel Kang, Tatsunori B Hashimoto
  Annual Meeting of the Association of Computational Linguistics (ACL 2020) 
 
 
 
  Learning Autocomplete Systems as a Communication Game
 
 PDF
 
 
 
 
 
 
 Mina Lee, Tatsunori B Hashimoto, Percy Liang
  Emergent Communication Workshop at Neural Information Processing Systems (NeurIPS 2019) 
 
 
 
  Distributionally Robust Language Modeling
 
 PDF
 
 
 
 
 
 
 Yonatan Oren*, Shiori Sagawa *, Tatsunori B Hashimoto *, Percy Liang
  Empirical Methods in Natural Language Processing (EMNLP 2019) 
 
 
 
  Unifying Human and Statistical Evaluation for Natural Language Generation
 
 PDF
 
 
 
 
 
 
 Tatsunori B Hashimoto*, Hugh Zhang*, Percy Liang
  Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2019) 
 
 
 
  A Retrieve-and-Edit Framework for Predicting Structured Outputs
 
 PDF
 
 
 
 
 
 
 Tatsunori B Hashimoto, Kelvin Guu, Yonatan Oren, Percy Liang
  Advances in Neural Information Processing Systems 31 (NeurIPS 2018, Oral) 
 
 
 
  Generating Sentences by Editing Prototypes
 
 PDF
 
 
 
 
 
 
 Kelvin Guu*, Tatsunori B Hashimoto*, Yonatan Oren, Percy Liang
  Transactions of the Association of Computational Linguistics (TACL, presented at ACL 2018) 
 
 
 
  Continuous representations and models from random walk diffusion limits
 
 PDF
 
 
 
 
 
 
 Tatsunori B Hashimoto
  PhD Thesis, MIT CSAIL, 2016 
 
 
 
  Word embeddings as metric recovery in semantic spaces
 
 PDF
 
 
 
 
 
 
 Tatsunori B Hashimoto, David Alvarez-Melis, Tommi S Jaakkola
  Transactions of the Association for Computational Linguistics 4 (TACL, presented at ACL 2016) 
 
 
  
 
 
 
  Inferring Multidimensional Rates of Aging from Cross-Sectional Data
 
 PDF
 
 
 
 
 
 
 Emma Pierson*, Pang Wei Koh *, Tatsunori B Hashimoto *, Daphne Koller, Jure Lesokevic, Nicholas Eriksson, Percy Liang
  22nd International Conference on Artificial Intelligence and Statistics (AISTATS 2019) 
 
 
 
  Continuous representations and models from random walk diffusion limits
 
 PDF
 
 
 
 
 
 
 Tatsunori B Hashimoto
  PhD Thesis, MIT CSAIL, 2016 
 
 
 
  Learning Population-Level Diffusions with Generative RNNs
 
 PDF
 
 
 
 
 
 
 Tatsunori B Hashimoto, David Gifford, Tommi S Jaakkola
  Proceedings of the 33rd International Conference on Machine Learning (ICML 2016) 
 
 
 
  A synergistic DNA logic predicts genome-wide chromatin accessibility
 
 PDF
 
 
 
 
 
 
 Tatsunori B Hashimoto*, Richard I Sherwood*, Daniel D Kang*, Nisha Rajagopal, Amira A Barkal, Haoyang Zeng, Bart JM Emons, Sharanya Srinivasan, Tommi S Jaakkola, David K Gifford
  Genome research (2016) 
 
 
 
  Cas9 Functionally Opens Chromatin
 
 PDF
 
 
 
 
 
 
 Amira A Barkal, Sharanya Srinivasan, Tatsunori B Hashimoto, David K Gifford, Richard I Sherwood
  PloS One (2016) 
 
 
 
  Cloning-free CRISPR
 
 PDF
 
 
 
 
 
 
 Mandana Arbab, Sharanya Srinivasan, Tatsunori B Hashimoto, Niels Geijsen, Richard I Sherwood
  Stem cell reports (2015) 
 
 
 
  GERV: a statistical method for generative evaluation of regulatory variants for transcription factor binding
 
 PDF
 
 
 
 
 
 
 Haoyang Zeng, Tatsunori B Hashimoto, Daniel D Kang, David K Gifford
  Bioinformatics (2015) 
 
 
 
  Long-term persistence and development of induced pancreatic beta cells generated by lineage conversion of acinar cells
 
 PDF
 
 
 
 
 
 
 Weida Li, Claudia Cavelti-Weder, Yinying Zhang, Kendell Clement, Scott Donovan, Gabriel Gonzalez, Jiang Zhu, Marianne Stemann, Ke Xu, Tatsunori B Hashimoto, Takatsugu Yamada, Mio Nakanishi, Yuemei Zhang, Samuel Zeng, David Gifford, Alexander Meissner, Gordon Weir, Qiao Zhou
  Nature Biotechnology (2014) 
 
 
 
  Universal count correction for high-throughput sequencing
 
 PDF
 
 
 
 
 
 
 Tatsunori B Hashimoto, Matthew D Edwards, David K Gifford
  PLoS computational biology (2014) 
 
 
 
  Discovery of directional and nondirectional pioneer transcription factors by modeling DNase profile magnitude and shape
 
 PDF
 
 
 
 
 
 
 Richard I Sherwood *, Tatsunori B Hashimoto *, Charles W O'donnell *, Sophia Lewis, Amira A Barkal, John Peter Van Hoff, Vivek Karun, Tommi S Jaakkola, David K Gifford
  Nature Biotechnology (2014) 
 
 
 
  Quantifying condition-dependent intracellular protein levels enables high-precision fitness estimates
 
 PDF
 
 
 
 
 
 
 Kerry A Geiler-Samerotte, Tatsunori B Hashimoto, Michael F Dion, Bogdan A Budnik, Edoardo M Airoldi, D Allan Drummond
  PloS one (2013) 
 
 
 
  Lineage-based identification of cellular states and expression programs
 
 PDF
 
 
 
 
 
 
 Tatsunori B Hashimoto, Tommi S Jaakkola, Richard Sherwood, Esteban O. Mazzoni, Hynek Wichterle, David Gifford
  Bioinformatics (2012) 
 
 
 
  Finding drug discovery rules of thumb with bump hunting
 
 PDF
 
 
 
 
 
 
 Tatsunori B Hashimoto, Matthew Segall
  Proceedings of the ACS (2010) 
 
 
 
  BFL: a node and edge betweenness based fast layout algorithm for large scale networks.
 
 PDF
 
 
 
 
 
 
 Tatsunori B Hashimoto, Masao Nagasaki, Kaname Kojima, Satoru Miyano
  BMC bioinformatics (2009)
Tatsunori Hashimoto
https://thashim.github.io/
https://web.stanford.edu/~jurafsky/
Dan Jurafsky
2022-01-01
None
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 Image: Do Pham, Stanford
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 <b>BOOKS</b>
 <br />
 <br />
<b>2022 ARTICLES </b>
  
  <a href="pubs.html"><b>[ALL PUBS]</b></a>
  <a href="https://scholar.google.com/citations?user=uZg9l58AAAAJ&amp;hl=en"><b>[GOOGLE SCHOLAR]</b></a>
 <p>
Peter Henderson, Mark S. Krass, Lucia Zheng, Neel Guha, Christopher D. Manning, Dan Jurafsky, and Daniel E. Ho. 2022. <a href="https://arxiv.org/pdf/2207.00220">Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset. Proceedings of Neurips 2022.
[</a><a href="https://arxiv.org/pdf/2207.00220">pdf</a>]
</p><p>
Rishi Bommasani, Kathleen A. Creel, Ananya Kumar, Dan Jurafsky, Percy Liang. 2022. Picking on the Same Person: Does Algorithmic Monoculture lead to Outcome Homogenization? Proceedings of the 36th Conference on Neural Information Processing Systems (NeurIPS), 2022. [<a href="https://arxiv.org/abs/2211.13972">pdf</a>]
</p><p>
Eric Mitchell*, Peter Henderson*, Christopher Manning, Dan Jurafsky, Chelsea Finn. 2022.
<a href="https://openreview.net/pdf?id=fYPC0TnrItR">Self-Destructing Models: Increasing the Costs of Harmful Dual Uses in Foundation Models.</a>
New Frontiers in Adversarial Machine Learning Workshop at ICML and Pre-training: Perspectives, Pitfalls, and Paths Forward at ICML, 2022.
[<a href="https://openreview.net/pdf?id=fYPC0TnrItR">pdf</a>]
</p><p>
Mirac Suzgun, Luke Melas-Kyriazi and Dan Jurafsky. 2022.
<a href="https://preview.aclanthology.org/emnlp-22-ingestion/2022.emnlp-main.141.pdf">Prompt-and-Rerank: A Method for Zero-Shot and Few-Shot Arbitrary Textual Style Transfer with Small Language Models</a>. Proceedings of EMNLP 2022.
[<a href="https://preview.aclanthology.org/emnlp-22-ingestion/2022.emnlp-main.141.pdf">pdf</a>]
[<a href="https://preview.aclanthology.org/emnlp-22-ingestion/2022.emnlp-main.141.pdf">bib</a>]
</p><p>
Kawin Ethayarajh and Dan Jurafsky. 2022.
<a href="https://preview.aclanthology.org/emnlp-22-ingestion/2022.emnlp-main.406.pdf">The Authenticity Gap in Human Evaluation.</a> EMNLP 2022.
[<a href="https://preview.aclanthology.org/emnlp-22-ingestion/2022.emnlp-main.406.pdf">pdf</a>]
[<a href="https://preview.aclanthology.org/emnlp-22-ingestion/2022.emnlp-main.406.bib">bib</a>]
</p><p>
 Dallas Card, Serina Chang, Chris Becker, Julia Mendelsohn, Rob Voigt, Leah Boustan, Ran Abramitzky, and Dan Jurafsky. 2022. <a href="https://www.pnas.org/doi/10.1073/pnas.2120510119"> Computational analysis of 140 years of US political speeches reveals more positive but increasingly polarized framing of immigration.</a> Proceedings of the National Academy of Sciences 119 (31) e2120510119. [<a href="https://www.pnas.org/doi/10.1073/pnas.2120510119">pdf</a>]
 </p><p>
Sterling Alic, Dorottya Demszky, Zid Mancenido, Jing Liu, Heather Hill &amp; Dan Jurafsky. 2022.
<a href="https://aclanthology.org/2022.bea-1.27.pdf">
Computationally Identifying Funneling and Focusing Questions in Classroom Discourse
</a>.
17th Workshop on Innovative Use of NLP for Building Educational Applications (BEA)
[<a href="https://aclanthology.org/2022.bea-1.27.pdf">pdf</a>]
[<a href="https://aclanthology.org/2022.bea-1.27.bib">bib</a>]
 </p><p>
Kaitlyn Zhou, Kawin Ethayarajh, Dallas Card, Dan Jurafsky. 2022. <a href="https://arxiv.org/pdf/2205.05092.pdf"> Problems with Cosine as a Measure of Embedding Similarity for High Frequency Words</a>. ACL 2022. <a href="https://arxiv.org/pdf/2205.05092.pdf">[pdf]</a> <a href="https://aclanthology.org/2022.acl-short.45.bib">[bib]</a>.
</p><p>
Kaitlyn Zhou, Kawin Ethayarajh, Dan Jurafsky. 2022.
<a href="https://arxiv.org/pdf/2205.05093.pdf">Richer Countries and Richer Representations</a>.
ACL 2022 - Findings. <a href="https://arxiv.org/pdf/2205.05093.pdf">[pdf]</a>
<a href="https://aclanthology.org/2022.findings-acl.164.bib">[bib]</a>.
</p><p>
San, N., Bartelds, M., ÃgÃºnrÃ¨mÃ­, T., Mount, A., Thompson, R., Higgins, M., Barker, R., Simpson, J., &amp; Jurafsky, D. 2022.
<a href="https://arxiv.org/pdf/2204.07272.pdf">
 Automated speech tools for helping communities process restricted-access corpora for language revival efforts.</a>
In <i>Proceedings of the 5th workshop on the use of Computational Methods in the Study of Endangered Languages (ComputEL-5)</i>. <a href="https://arxiv.org/pdf/2204.07272.pdf">[pdf]</a>
</p><p>
Junshen Chen, Dallas Card, and Dan Jurafsky. 2022.
<a href="https://aclanthology.org/2022.findings-acl.288.pdf">Modular Domain Adaptation</a>.
ACL 2022 - Findings.
<a href="https://aclanthology.org/2022.findings-acl.288.pdf">[pdf]</a>
<a href="https://aclanthology.org/2022.findings-acl.288.bib">[bib]</a>.
</p><p>
Stephan Risi, Crystal Lee, Mathias W. Nielsen, Emma Kerr,
Emer Brady, Lanu Kim, Daniel A. McFarland, Dan Jurafsky, James Zou, and Londa Schiebinger. 2022.
<a href="https://doi.org/10.1371/journal.pone.0262027">Diversifying History: A Large-Scale Analysis of Changes in Researcher Demographics and Scholarly Agendas</a>.
PlosOne. 17(1). <a href="https://doi.org/10.1371/journal.pone.0262027">[pdf]</a>.
 </p><p>
 Bradley P. Turnwald, Margaret A. Perry, David Jurgens, Vinodkumar Prabhakaran, Dan Jurafsky, Hazel R. Markus, Alia J. Crum. 2022. Language in popular American culture constructs the meaning of healthy and unhealthy eating: Narratives of craveability, excitement, and social connection in movies, television, social media, recipes, and food reviews,
Appetite, Volume 172. <a href="https://doi.org/10.1016/j.appet.2022.105949">[pdf]</a>
</p></td></tr></tbody></div>||||I|||| DAN JURAFSKY   BOOKS      
 Jackson Eli Reynolds Professor   3rd edn. draft chapters! James Beard Award      
 in Humanities,   Speech and Language Processing Finalist!      
 Professor of Linguistics   Dan Jurafsky and James H. Martin      
Image: Do Pham, Stanford Professor of Computer Science         
 Stanford University         
 I study natural language processing and its application to the social and cognitive sciences. I am a past MacArthur Fellow and also work on the language of food . 2022 ARTICLES [ALL PUBS] [GOOGLE SCHOLAR]      
         
    Peter Henderson, Mark S. Krass, Lucia Zheng, Neel Guha, Christopher D. Manning, Dan Jurafsky, and Daniel E. Ho. 2022. Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset. Proceedings of Neurips 2022. [pdf]  
         
jurafsky@stanford.edu   Rishi Bommasani, Kathleen A. Creel, Ananya Kumar, Dan Jurafsky, Percy Liang. 2022. Picking on the Same Person: Does Algorithmic Monoculture lead to Outcome Homogenization? Proceedings of the 36th Conference on Neural Information Processing Systems (NeurIPS), 2022. [pdf]  
Margaret Jacks 117 TEACHING         
Stanford CA 94305-2150   Eric Mitchell*, Peter Henderson*, Christopher Manning, Dan Jurafsky, Chelsea Finn. 2022. Self-Destructing Models: Increasing the Costs of Harmful Dual Uses in Foundation Models. New Frontiers in Adversarial Machine Learning Workshop at ICML and Pre-training: Perspectives, Pitfalls, and Paths Forward at ICML, 2022. [pdf] 
 WINTER 2023         
BIO cs124/ling180:   Mirac Suzgun, Luke Melas-Kyriazi and Dan Jurafsky. 2022. Prompt-and-Rerank: A Method for Zero-Shot and Few-Shot Arbitrary Textual Style Transfer with Small Language Models. Proceedings of EMNLP 2022. [pdf] [bib]   
 From Languages to Information         
TWITTER: @jurafsky Tu/Thu 3:00 PM - 4:20 PM,   Kawin Ethayarajh and Dan Jurafsky. 2022. The Authenticity Gap in Human Evaluation. EMNLP 2022. [pdf] [bib]     
         
CV SPRING 2023   Dallas Card, Serina Chang, Chris Becker, Julia Mendelsohn, Rob Voigt, Leah Boustan, Ran Abramitzky, and Dan Jurafsky. 2022. Computational analysis of 140 years of US political speeches reveals more positive but increasingly polarized framing of immigration. Proceedings of the National Academy of Sciences 119 (31) e2120510119. [pdf] 
 CS 384: Seminar on Social and Ethical Issues in Natural Language Processing        
PEOPLE Thu 3:00-5:50 PM   Sterling Alic, Dorottya Demszky, Zid Mancenido, Jing Liu, Heather Hill & Dan Jurafsky. 2022. Computationally Identifying Funneling and Focusing Questions in Classroom Discourse . 17th Workshop on Innovative Use of NLP for Building Educational Applications (BEA) [pdf] [bib]  
         
NLP group CS 329R: Race and Natural Language Processing (co-taught with Jennifer Eberhardt)  Kaitlyn Zhou, Kawin Ethayarajh, Dallas Card, Dan Jurafsky. 2022. Problems with Cosine as a Measure of Embedding Similarity for High Frequency Words. ACL 2022. [pdf] [bib].    
 Tue 1:30-4:00 PM         
WHERE'S DAN?   Kaitlyn Zhou, Kawin Ethayarajh, Dan Jurafsky. 2022. Richer Countries and Richer Representations. ACL 2022 - Findings. [pdf] [bib].    
 Earlier Courses         
LANGUAGE OF FOOD   San, N., Bartelds, M., ÃgÃºnrÃ¨mÃ­, T., Mount, A., Thompson, R., Higgins, M., Barker, R., Simpson, J., & Jurafsky, D. 2022. Automated speech tools for helping communities process restricted-access corpora for language revival efforts. In Proceedings of the 5th workshop on the use of Computational Methods in the Study of Endangered Languages (ComputEL-5). [pdf] 
blog LECTURE VIDEOS         
class CS124: YouTube lecture videos   Junshen Chen, Dallas Card, and Dan Jurafsky. 2022. Modular Domain Adaptation. ACL 2022 - Findings. [pdf] [bib].     
articles 2012 NLP Online w/Chris Manning:         
 - Youtube channel lecture videos   Stephan Risi, Crystal Lee, Mathias W. Nielsen, Emma Kerr, Emer Brady, Lanu Kim, Daniel A. McFarland, Dan Jurafsky, James Zou, and Londa Schiebinger. 2022. Diversifying History: A Large-Scale Analysis of Changes in Researcher Demographics and Scholarly Agendas. PlosOne. 17(1). [pdf].  
 - Slides         
    Bradley P. Turnwald, Margaret A. Perry, David Jurgens, Vinodkumar Prabhakaran, Dan Jurafsky, Hazel R. Markus, Alia J. Crum. 2022. Language in popular American culture constructs the meaning of healthy and unhealthy eating: Narratives of craveability, excitement, and social connection in movies, television, social media, recipes, and food reviews, Appetite, Volume 172. [pdf]
         
         
         
         
         
         
         
         
         
         
         
         
         
         
         
         
         
         
         
         
         
         
         
         
         
         
         
         

Image: Do Pham, Stanford
  
 
  
 BOOKS
 
 
2022 ARTICLES 
  
   [ALL PUBS] 
   [GOOGLE SCHOLAR] 
 
Peter Henderson, Mark S. Krass, Lucia Zheng, Neel Guha, Christopher D. Manning, Dan Jurafsky, and Daniel E. Ho. 2022. Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset. Proceedings of Neurips 2022.
[ pdf]
 
Rishi Bommasani, Kathleen A. Creel, Ananya Kumar, Dan Jurafsky, Percy Liang. 2022. Picking on the Same Person: Does Algorithmic Monoculture lead to Outcome Homogenization? Proceedings of the 36th Conference on Neural Information Processing Systems (NeurIPS), 2022. [pdf]
 
Eric Mitchell*, Peter Henderson*, Christopher Manning, Dan Jurafsky, Chelsea Finn. 2022.
Self-Destructing Models: Increasing the Costs of Harmful Dual Uses in Foundation Models.
New Frontiers in Adversarial Machine Learning Workshop at ICML and Pre-training: Perspectives, Pitfalls, and Paths Forward at ICML, 2022.
[pdf]
 
Mirac Suzgun, Luke Melas-Kyriazi and Dan Jurafsky. 2022.
Prompt-and-Rerank: A Method for Zero-Shot and Few-Shot Arbitrary Textual Style Transfer with Small Language Models. Proceedings of EMNLP 2022.
[pdf]
[bib]
 
Kawin Ethayarajh and Dan Jurafsky. 2022.
The Authenticity Gap in Human Evaluation. EMNLP 2022.
[pdf]
[bib]
 
 Dallas Card, Serina Chang, Chris Becker, Julia Mendelsohn, Rob Voigt, Leah Boustan, Ran Abramitzky, and Dan Jurafsky. 2022.  Computational analysis of 140 years of US political speeches reveals more positive but increasingly polarized framing of immigration. Proceedings of the National Academy of Sciences 119 (31) e2120510119. [pdf]
  
Sterling Alic, Dorottya Demszky, Zid Mancenido, Jing Liu, Heather Hill &amp; Dan Jurafsky. 2022.
Computationally Identifying Funneling and Focusing Questions in Classroom Discourse
.
17th Workshop on Innovative Use of NLP for Building Educational Applications (BEA)
[pdf]
[bib]
  
Kaitlyn Zhou, Kawin Ethayarajh, Dallas Card, Dan Jurafsky. 2022.  Problems with Cosine as a Measure of Embedding Similarity for High Frequency Words. ACL 2022. [pdf] [bib].
 
Kaitlyn Zhou, Kawin Ethayarajh, Dan Jurafsky. 2022.
Richer Countries and Richer Representations.
ACL 2022 - Findings. [pdf]
[bib].
 
San, N., Bartelds, M., ÃgÃºnrÃ¨mÃ­, T., Mount, A., Thompson, R., Higgins, M., Barker, R., Simpson, J., &amp; Jurafsky, D. 2022.
 Automated speech tools for helping communities process restricted-access corpora for language revival efforts.
In Proceedings of the 5th workshop on the use of Computational Methods in the Study of Endangered Languages (ComputEL-5). [pdf]
 
Junshen Chen, Dallas Card, and Dan Jurafsky. 2022.
Modular Domain Adaptation.
ACL 2022 - Findings.
[pdf]
[bib].
 
Stephan Risi, Crystal Lee, Mathias W. Nielsen, Emma Kerr,
Emer Brady, Lanu Kim, Daniel A. McFarland, Dan Jurafsky, James Zou, and Londa Schiebinger. 2022.
Diversifying History: A Large-Scale Analysis of Changes in Researcher Demographics and Scholarly Agendas.
PlosOne. 17(1). [pdf].
  
 Bradley P. Turnwald, Margaret A. Perry, David Jurgens, Vinodkumar Prabhakaran, Dan Jurafsky, Hazel R. Markus, Alia J. Crum. 2022. Language in popular American culture constructs the meaning of healthy and unhealthy eating: Narratives of craveability, excitement, and social connection in movies, television, social media, recipes, and food reviews,
Appetite, Volume 172. [pdf]
Dan Jurafsky
http://web.stanford.edu/~jurafsky/
https://physics.mit.edu/faculty/john-belcher/
John Winston Belcher » MIT Physics
2022-10-13
None
<div><div>
	
<h2>Research Interests</h2>
<p>Professor Belcher’s research interests are within the areas of space plasma physics, in particular the interaction of the heliosphere with the local interstellar medium. He was the principal investigator on the Voyager Plasma Science Experiment during the Voyager Neptune Encounter—the end of the Grand Tour. He is now a co-investigator on the <a href="http://web.mit.edu/space/www/voyager/voyager.html">Plasma Science Experiment</a> on board the <a href="http://voyager.jpl.nasa.gov/">Voyager Interstellar Mission</a>. He has also published scholarly studies of the results of introducing interactive engagement techniques into introductory physics courses at MIT, as in <a href="http://icampus.mit.edu/projects/project/?pname=TEAL">TEAL</a>.</p>
<figure><p>
</p><figcaption>Courtesy of xTalks Office of Digital Learning | YouTube</figcaption></figure>
<h2>Biographic Sketch</h2>
<p>Professor Belcher was born in Louisiana in 1943, and graduated from Odessa High School in West Texas in 1961. He attended Rice University in Houston, graduating with a double major in math and physics in 1965, summa cum laude. He then went to Caltech for graduate school where he earned his Ph.D. in Physics in 1971.</p>
<p>Professor Belcher came to MIT in 1971, to work with Professors <a href="http://web.mit.edu/space/www/Bridge.html">Herbert Bridge</a> and Alan Lazarus, who had the plasma probe on board Mariner 5. Just after he arrived, the Space Plasma Group wrote a proposal for the Voyager mission to Jupiter and Saturn. After reaching these two planets, as well as Uranus and Neptune, Voyager is still going strong. In its most recent incarnation, it is referred to as the <a href="http://voyager.jpl.nasa.gov/">Voyager Interstellar Mission</a>. Within the next ten years, it is probable that the MIT plasma instrument on Voyager 2 will make measurements in the interstellar medium.</p>
<p>Professor Belcher has twice received the NASA Exceptional Scientific Achievement Medal, once for contributions to the understanding of the plasma dynamics of the Jovian magnetosphere, in 1980, and once for his role as principal investigator on the Plasma Science Experiment on Voyager during the Neptune encounter, in 1990. In 2004, the Institute awarded Belcher with the Class of ’22 professorship, designed to honor “a tenured faculty member with a record of excellence in education, with respect to both curriculum development and classroom teaching.” He is the Associate Chair of the MIT Faculty in AY 2013-2015. He is a MacVicar Faculty Fellow. He has been recently awarded a 2015 MIT Excellence awards and named an 2016 Oersted Medal Recipient.</p>
<p>More info:</p>
<ul><li><a href="http://mit.edu/jbelcher/www/">Personal webpage</a></li></ul>
<figure><p>
</p><figcaption>Asking for help – Physics Professor John Belcher gives a very candid description of his own experience with depression.<br />Courtesy of MIT MindHandHeart</figcaption></figure>
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 John Winston Belcher

 Class of 1922 Professor of Physics, Emeritus
 Research interests are within the areas of space plasma physics, in particular the interaction of the heliosphere with the local interstellar medium.
 Research Areas
 Astrophysics Observation, Instrumentation, and Experiment
 (617) 253-4285
 jbelcher@mit.edu
 Office: 37-695
 Affiliated Center(s): MIT Kavli Institute for Astrophysics & Space Research , Plasma Science and Fusion Center (PSFC)
 Assistant: Thea Paneth

 Research Interests

 Professor Belcher’s research interests are within the areas of space plasma physics, in particular the interaction of the heliosphere with the local interstellar medium. He was the principal investigator on the Voyager Plasma Science Experiment during the Voyager Neptune Encounter—the end of the Grand Tour. He is now a co-investigator on the Plasma Science Experiment on board the Voyager Interstellar Mission. He has also published scholarly studies of the results of introducing interactive engagement techniques into introductory physics courses at MIT, as in TEAL.

 Courtesy of xTalks Office of Digital Learning | YouTube

 Biographic Sketch

 Professor Belcher was born in Louisiana in 1943, and graduated from Odessa High School in West Texas in 1961. He attended Rice University in Houston, graduating with a double major in math and physics in 1965, summa cum laude. He then went to Caltech for graduate school where he earned his Ph.D. in Physics in 1971.

 Professor Belcher came to MIT in 1971, to work with Professors Herbert Bridge and Alan Lazarus, who had the plasma probe on board Mariner 5. Just after he arrived, the Space Plasma Group wrote a proposal for the Voyager mission to Jupiter and Saturn. After reaching these two planets, as well as Uranus and Neptune, Voyager is still going strong. In its most recent incarnation, it is referred to as the Voyager Interstellar Mission. Within the next ten years, it is probable that the MIT plasma instrument on Voyager 2 will make measurements in the interstellar medium.

 Professor Belcher has twice received the NASA Exceptional Scientific Achievement Medal, once for contributions to the understanding of the plasma dynamics of the Jovian magnetosphere, in 1980, and once for his role as principal investigator on the Plasma Science Experiment on Voyager during the Neptune encounter, in 1990. In 2004, the Institute awarded Belcher with the Class of ’22 professorship, designed to honor “a tenured faculty member with a record of excellence in education, with respect to both curriculum development and classroom teaching.” He is the Associate Chair of the MIT Faculty in AY 2013-2015. He is a MacVicar Faculty Fellow. He has been recently awarded a 2015 MIT Excellence awards and named an 2016 Oersted Medal Recipient.

 More info:

 * Personal webpage

 Asking for help – Physics Professor John Belcher gives a very candid description of his own experience with depression.
 Courtesy of MIT MindHandHeart

 In the news
 In the news

 Seven MIT educators honored for digital learning innovation

 Educators recognized for improving classroom instruction and student engagement through innovative uses of digital technology.

 Awards & Honors

 * 2016 // Oersted Medal (AAPT) "The Challenges of Pedagogical Change at a Research I University"
 * 2008 // American Physical Society Fellow (Nominated by FED) "For developing 3D electromagnetic field visualization tools and for the creation and large-scale implementation of a studio-based, active learning version of introductory physics, TEAL."
 * 2004 // Class of 1922 Professor for recognition as a “tenured faculty member with a record of excellence in education with respect to both curriculum development and classroom teaching.”
 * 2004 // Dean’s Educational and Student Advising Award of the School of Science
 * 2000-10 // Margaret MacVicar Faculty Fellow, MIT (Honors MIT's Best Teachers)
 * 1990 // NASA Exceptional Scientific Achievement Medal
 * 1980 // NASA Exceptional Scientific Achievement Medal

 Key Publications

 * Dori Y. J. and J.W. Belcher; How Does Technology,Enabled Active Learning Affect Undergraduate Students’ Understanding of Electromagnetism Concepts?; The Journal of the Learning Sciences; 14(2); 243,279; 2005.

 * Belcher; John W.; Alan J. Lazarus; Ralph L. McNutt; Jr. and George S. Gordon; Jr.; Solar Wind Conditions in the Outer Heliosphere and the Distance to the Termination Shock; J. Geophys. Res. 15;177,15;183; 1993.

 * Belcher; J. W.; H. S. Bridge; F. Bagenal; B. Coppi; O. Divers; A. Eviatar; G. S. Gordon; Jr.; A. J. Lazarus; R. L. McNutt; Jr.; K. W. Ogilvie; J. D. Richardson; G. L. Siscoe; E. C. Sittler; Jr.; J. T. Steinberg; J. D. Sullivan; A. Szabo; L. Villanueva; V. M. Vasyliunas; and M. Zhang; Plasma Observations Near Neptune: Initial Results from Voyager 2; Science; 246; 1478,1483; 1989.

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 For Emergencies Accessibility
Research Interests
Professor Belcher’s research interests are within the areas of space plasma physics, in particular the interaction of the heliosphere with the local interstellar medium. He was the principal investigator on the Voyager Plasma Science Experiment during the Voyager Neptune Encounter—the end of the Grand Tour. He is now a co-investigator on the Plasma Science Experiment on board the Voyager Interstellar Mission. He has also published scholarly studies of the results of introducing interactive engagement techniques into introductory physics courses at MIT, as in TEAL.
 
 Courtesy of xTalks Office of Digital Learning | YouTube 
Biographic Sketch
Professor Belcher was born in Louisiana in 1943, and graduated from Odessa High School in West Texas in 1961. He attended Rice University in Houston, graduating with a double major in math and physics in 1965, summa cum laude. He then went to Caltech for graduate school where he earned his Ph.D. in Physics in 1971.
Professor Belcher came to MIT in 1971, to work with Professors Herbert Bridge and Alan Lazarus, who had the plasma probe on board Mariner 5. Just after he arrived, the Space Plasma Group wrote a proposal for the Voyager mission to Jupiter and Saturn. After reaching these two planets, as well as Uranus and Neptune, Voyager is still going strong. In its most recent incarnation, it is referred to as the Voyager Interstellar Mission. Within the next ten years, it is probable that the MIT plasma instrument on Voyager 2 will make measurements in the interstellar medium.
Professor Belcher has twice received the NASA Exceptional Scientific Achievement Medal, once for contributions to the understanding of the plasma dynamics of the Jovian magnetosphere, in 1980, and once for his role as principal investigator on the Plasma Science Experiment on Voyager during the Neptune encounter, in 1990. In 2004, the Institute awarded Belcher with the Class of ’22 professorship, designed to honor “a tenured faculty member with a record of excellence in education, with respect to both curriculum development and classroom teaching.” He is the Associate Chair of the MIT Faculty in AY 2013-2015. He is a MacVicar Faculty Fellow. He has been recently awarded a 2015 MIT Excellence awards and named an 2016 Oersted Medal Recipient.
More info:
  Personal webpage  
 
 Asking for help – Physics Professor John Belcher gives a very candid description of his own experience with depression.Courtesy of MIT MindHandHeart
John Winston Belcher » MIT Physics
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Sara Seager
2014-10-27
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	<div><p></p><h2>Discover more about Professor Sara Seager’s work</h2>
<p></p></div><div><div><p>0</p><p>Planets in the habitable zone</p></div><div><p>0</p><p>Identified habitable planets</p></div></div><div><h2>Book Sara as a Guest Speaker</h2>
<p>The field of exoplanets is maturing but still with huge unrealized potential. Professor Sara Seager’s fascinating and thought-provoking talks explain, in layman’s terms, the exciting field of exoplanet research and the holy grail to find and identify an Earth-type planet.</p>
</div><div><div><p>Having authored three of her own books (’The Smallest Lights in the Universe: a Memoir’, ‘Exoplanets and the Search For Habitable Worlds’, and ‘Exoplanet Atmospheres: Physical Processes’) and edited a fourth, Sara has experience consolidating years of research into an authoritative resource and is credited with producing a book that “will be a bible for students and professionals interested in exoplanet atmospheres.” <a href="https://www.saraseager.com/books/">Find out more about the books here</a></p></div><div><p>Sara has been credited on a great number of papers, either as the lead or in an instrumental role, regarding the different aspects of exoplanet research, including Exoplanet Atmospheres; Observational Detections; Exoplanet Interiors; Exoplanet Biosignatures; Exoplanet Characterization; A New Approach to Exoplanet Data Interpretation; and ExoplanetSat NanoSatellite. <a href="https://www.saraseager.com/papers/">Click here for more detail.</a></p></div></div><div><div><div><p></p><h2>Join the journey to discover earths twins</h2>
<p></p></div><div><p>To be kept up to date with the latest breakthroughs and discoveries, register your email address to receive news updates on the search for Earth’s twin.</p>
 
 
 
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 Having authored three of her own books (’The Smallest Lights in the Universe: a Memoir’, ‘Exoplanets and the Search For Habitable Worlds’, and ‘Exoplanet Atmospheres: Physical Processes’) and edited a fourth, Sara has experience consolidating years of research into an authoritative resource and is credited with producing a book that “will be a bible for students and professionals interested in exoplanet atmospheres.” Find out more about the books here

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Discover more about Professor Sara Seager’s work
     0 Planets in the habitable zone   0 Identified habitable planets    Book Sara as a Guest Speaker
The field of exoplanets is maturing but still with huge unrealized potential. Professor Sara Seager’s fascinating and thought-provoking talks explain, in layman’s terms, the exciting field of exoplanet research and the holy grail to find and identify an Earth-type planet.
   Having authored three of her own books (’The Smallest Lights in the Universe: a Memoir’, ‘Exoplanets and the Search For Habitable Worlds’, and ‘Exoplanet Atmospheres: Physical Processes’) and edited a fourth, Sara has experience consolidating years of research into an authoritative resource and is credited with producing a book that “will be a bible for students and professionals interested in exoplanet atmospheres.” Find out more about the books here    Sara has been credited on a great number of papers, either as the lead or in an instrumental role, regarding the different aspects of exoplanet research, including Exoplanet Atmospheres; Observational Detections; Exoplanet Interiors; Exoplanet Biosignatures; Exoplanet Characterization; A New Approach to Exoplanet Data Interpretation; and ExoplanetSat NanoSatellite. Click here for more detail.         Join the journey to discover earths twins
    To be kept up to date with the latest breakthroughs and discoveries, register your email address to receive news updates on the search for Earth’s twin.
Sara Seager
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https://physics.mit.edu/faculty/raymond-ashoori/
Raymond Ashoori » MIT Physics
2022-10-19
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<h2>Research Interests</h2>
<p>The Ashoori group studies interacting electronic systems in low dimensional (0,1 or 2 dimensions) electronic structures, such as semiconductor samples, graphene, and transition-metal dichalcogenide layers. The Ashoori group has developed a novel pulsed method that allows contactless measurement of electron tunneling spectra in isolated conducting and even insulating samples.  This method, for instance, permitted the discovery of a sharp feature in tunneling spectra originating from the vibrations of an electronic crystal. The Ashoori group also uses very sensitive capacitance measurements for study of novel electronic systems. In collaboration with the Jarillo-Herrero group, these measurements detected spectral features from the Hofstadter Butterfly  in graphene and correlated insulating states of twisted bilayer graphene.The Ashoori group’s measurement methods have sufficient sensitivity to determine spectra for single electron additions to small isolated structures such as quantum dots. While simple quantum mechanical calculations determine the motion of a single electron in such a confining structures, it is far from simple to understand the behavior of many trapped electrons. Not only do the electrons repel one another, they are indistinguishable. This fact, along with the principle that only one electron can exist in any quantum mechanical orbit, produces unusual and sometimes counterintuitive correlations in the motions of electrons.</p>
<h2>Biographical Sketch</h2>
<p>A graduate of the University of California at San Diego (B.A. 1984) and Cornell University (Ph.D. 1991), Professor Ashoori joined the MIT faculty in 1993, after serving as a postdoctoral member of the technical staff at AT&amp;T Bell Laboratories. He was promoted to Associate Professor in 1998 and full Professor in July 2004. His awards include the David and Lucille Packard Fellowship (1993), NSF Young Investigator Award (1993), Sloan Fellow (1993), McMillan Award (1994), APS Fellow (2009), Moore Foundation Grant for Ultrahigh Resolution Spectroscopy of Electronic Systems (2011). </p>
	</div>
	<div>
	<p>
	In the news
	</p>
	</div>
	<div>
	<div>
	<p>
	</p><h2>Awards &amp; Honors</h2>
	<p></p>
	</div>
	<div>
	<ul>
	<li>
	<strong>2012</strong> // Gordon and Betty Moore Foundation Grant
	</li>
	<li>
	<strong>2009</strong> // American Physical Society (APS) "For the development of imaging techniques that reveal the physical properties of reduced-dimensional electronic systems."
	</li>
	<li>
	<strong>1994</strong> // Sloan Research Fellowship
	</li>
	<li>
	<strong>1993</strong> // Packard Fellowship for Science and Engineering
	</li>
	</ul>
	</div>
	</div>
	<div>
	<div>
	<p>
	</p><h2>Key Publications</h2>
	<p></p>
	</div>
	<div>
	<ul>
	<li>
	<p>Lu Li, C. Richter, S. Paetel, T. Kopp, J. Mannhart, R. C. Ashoori. <a href="http://www.sciencemag.org/content/332/6031/825.full.html">“Very Large Capacitance Enhancement in a Two-Dimensional Electron System”</a>, Science, 332, 825-828 (2011)</p>
 	</li>
	<li>
	<p>Lu Li, C. Richter, J. Mannhart R. C. Ashoori, <a href="http://www.nature.com/nphys/journal/v7/n10/full/nphys2080.html">“Coexistence of magnetic order and two-dimensional superconductivity at LaAlO3/SrTiO3 interfaces”</a>, Nature Physics, 7, 762-766 (2011)</p>
 	</li>
	<li>
	<p>O.E. Dial, R.C. Ashoori, L.N. Pfeiffer, K.W. West. <a href="http://www.nature.com/nature/journal/v464/n7288/full/nature08941.html">“Anomalous structure in the single particle spectrum of the fractional quantum Hall effect”</a>, Nature, 464, 566-570 (2010)</p>
 	</li>
	</ul>
	</div>
	</div>
	</article>
	</div>
	
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 Raymond Ashoori

 Professor of Physics
 Division Head, Atomic, Biophysical, and Condensed Matter Physics
 Focus of research lies in the study of interacting electronic systems in low dimensional electronic structures.
 Research Areas
 Condensed Matter Experiment
 (617) 253-5585
 ashoori@mit.edu
 Office: 13-2053
 Lab(s): Ashoori Group
 Affiliated Center(s): Center for Integrated Quantum Materials , MIT Center for Quantum Engineering
 Assistant: Gerry Miller

 Research Interests

 The Ashoori group studies interacting electronic systems in low dimensional (0,1 or 2 dimensions) electronic structures, such as semiconductor samples, graphene, and transition-metal dichalcogenide layers. The Ashoori group has developed a novel pulsed method that allows contactless measurement of electron tunneling spectra in isolated conducting and even insulating samples. This method, for instance, permitted the discovery of a sharp feature in tunneling spectra originating from the vibrations of an electronic crystal. The Ashoori group also uses very sensitive capacitance measurements for study of novel electronic systems. In collaboration with the Jarillo-Herrero group, these measurements detected spectral features from the Hofstadter Butterfly in graphene and correlated insulating states of twisted bilayer graphene.The Ashoori group’s measurement methods have sufficient sensitivity to determine spectra for single electron additions to small isolated structures such as quantum dots. While simple quantum mechanical calculations determine the motion of a single electron in such a confining structures, it is far from simple to understand the behavior of many trapped electrons. Not only do the electrons repel one another, they are indistinguishable. This fact, along with the principle that only one electron can exist in any quantum mechanical orbit, produces unusual and sometimes counterintuitive correlations in the motions of electrons.

 Biographical Sketch

 A graduate of the University of California at San Diego (B.A. 1984) and Cornell University (Ph.D. 1991), Professor Ashoori joined the MIT faculty in 1993, after serving as a postdoctoral member of the technical staff at AT&T Bell Laboratories. He was promoted to Associate Professor in 1998 and full Professor in July 2004. His awards include the David and Lucille Packard Fellowship (1993), NSF Young Investigator Award (1993), Sloan Fellow (1993), McMillan Award (1994), APS Fellow (2009), Moore Foundation Grant for Ultrahigh Resolution Spectroscopy of Electronic Systems (2011).

 In the news
 In the news

 Faculty highlight: Raymond Ashoori

 Novel experimental technique reveals exotic behaviors in graphene systems.

 Awards & Honors

 * 2012 // Gordon and Betty Moore Foundation Grant
 * 2009 // American Physical Society (APS) "For the development of imaging techniques that reveal the physical properties of reduced-dimensional electronic systems."
 * 1994 // Sloan Research Fellowship
 * 1993 // Packard Fellowship for Science and Engineering

 Key Publications

 * Lu Li, C. Richter, S. Paetel, T. Kopp, J. Mannhart, R. C. Ashoori. “Very Large Capacitance Enhancement in a Two-Dimensional Electron System”, Science, 332, 825-828 (2011)

 * Lu Li, C. Richter, J. Mannhart R. C. Ashoori, “Coexistence of magnetic order and two-dimensional superconductivity at LaAlO3/SrTiO3 interfaces”, Nature Physics, 7, 762-766 (2011)

 * O.E. Dial, R.C. Ashoori, L.N. Pfeiffer, K.W. West. “Anomalous structure in the single particle spectrum of the fractional quantum Hall effect”, Nature, 464, 566-570 (2010)

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Research Interests
The Ashoori group studies interacting electronic systems in low dimensional (0,1 or 2 dimensions) electronic structures, such as semiconductor samples, graphene, and transition-metal dichalcogenide layers. The Ashoori group has developed a novel pulsed method that allows contactless measurement of electron tunneling spectra in isolated conducting and even insulating samples.  This method, for instance, permitted the discovery of a sharp feature in tunneling spectra originating from the vibrations of an electronic crystal. The Ashoori group also uses very sensitive capacitance measurements for study of novel electronic systems. In collaboration with the Jarillo-Herrero group, these measurements detected spectral features from the Hofstadter Butterfly  in graphene and correlated insulating states of twisted bilayer graphene.The Ashoori group’s measurement methods have sufficient sensitivity to determine spectra for single electron additions to small isolated structures such as quantum dots. While simple quantum mechanical calculations determine the motion of a single electron in such a confining structures, it is far from simple to understand the behavior of many trapped electrons. Not only do the electrons repel one another, they are indistinguishable. This fact, along with the principle that only one electron can exist in any quantum mechanical orbit, produces unusual and sometimes counterintuitive correlations in the motions of electrons.
Biographical Sketch
A graduate of the University of California at San Diego (B.A. 1984) and Cornell University (Ph.D. 1991), Professor Ashoori joined the MIT faculty in 1993, after serving as a postdoctoral member of the technical staff at AT&amp;T Bell Laboratories. He was promoted to Associate Professor in 1998 and full Professor in July 2004. His awards include the David and Lucille Packard Fellowship (1993), NSF Young Investigator Award (1993), Sloan Fellow (1993), McMillan Award (1994), APS Fellow (2009), Moore Foundation Grant for Ultrahigh Resolution Spectroscopy of Electronic Systems (2011). 
	
	
	
	In the news
	
	
	
	
	
	 Awards &amp; Honors
	 
	
	
	
	
	2012 // Gordon and Betty Moore Foundation Grant
	
	
	2009 // American Physical Society (APS) "For the development of imaging techniques that reveal the physical properties of reduced-dimensional electronic systems."
	
	
	1994 // Sloan Research Fellowship
	
	
	1993 // Packard Fellowship for Science and Engineering
	
	
	
	
	
	
	
	 Key Publications
	 
	
	
	
	
	Lu Li, C. Richter, S. Paetel, T. Kopp, J. Mannhart, R. C. Ashoori. “Very Large Capacitance Enhancement in a Two-Dimensional Electron System”, Science, 332, 825-828 (2011)
 	
	
	Lu Li, C. Richter, J. Mannhart R. C. Ashoori, “Coexistence of magnetic order and two-dimensional superconductivity at LaAlO3/SrTiO3 interfaces”, Nature Physics, 7, 762-766 (2011)
 	
	
	O.E. Dial, R.C. Ashoori, L.N. Pfeiffer, K.W. West. “Anomalous structure in the single particle spectrum of the fractional quantum Hall effect”, Nature, 464, 566-570 (2010)
Raymond Ashoori » MIT Physics
https://physics.mit.edu/faculty/raymond-ashoori/