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Spring 2022 Colloquia
Check back soon for more information on the computer science seminar series. If you would like to receive notices about upcoming seminars, you can subscribe to the announcement listserv. Unless otherwise noted, the seminars meet on Mondays at 4pm in Stanley Thomas 316. All colloquia this semester, unless otherwise noted, will be available for in-person attendance as well as remote attendance via Zoom. Current Tulane faculty, staff, and students are encouraged to attend in person. Due to the university's Covid-19 protocols, any guest lecturers, speakers, or non-affiliate meeting attendees will need to provide proof of full vaccination or negative COVID-19 test from no more than 72 hours before they are on campus. Zoom details will be provided via the announcement listserv, or you may email dramil1@tulane.edu to request the corresponding link.
AI-Driven Big Data Analytics
Justin Zhan | University of Arkansas
Abstract: Data has become the central driving force to new discoveries in science, informed governance, insight into society, and economic growth in the 21st century. Abundant data is a direct result of innovations including the Internet, faster computer processors, cheap storage, the proliferation of sensors, etc, and has the potential to increase business productivity and enable scientific discovery. However, while data is abundant and everywhere, people do not have a fundamental understanding of data. Traditional approaches to decision making under uncertainty are not adequate to deal with massive amounts of data, especially when such data is dynamically changing or becomes available over time. These challenges require novel techniques in AI-driven data analytics, In this seminar, a number of recent funded AI-driven big data analytics projects will be presented to address various data analytics, mining, modeling and optimization challenges.
About the Speaker: Dr. Justin Zhan is the Arkansas Research Alliance Scholar and Professor of Data Science at the Department of Computer Science and Computer Engineering, University of Arkansas. He is the Director of Data Science, Arkansas Integrative Metabolic Research Center. He is a joint professor at the Department of Biomedical Informatics, School of Medicine, University of Arkansas for Medical Sciences. He received his PhD degree from University of Ottawa, Master degree from Syracuse University, and Bachelor degree from Liaoning University of Engineering and Technology. His research interests include Data Science, Biomedical Informatics, Deep Learning & Big Data Analytics, Cyber Security & Blockchain, Network Science & Social Computing. He has served as a conference general chair, a program chair, a publicity chair, a workshop chair, or a program committee member for over one-hundred and fifty international conferences and an editor-in-chief, an editor, an associate editor, a guest editor, an editorial advisory board member, or an editorial board member for about thirty journals. He has published 246 articles in peer-reviewed journals and conferences and delivered 30 keynote speeches and invited talks. His research has been extensively funded by National Science Foundation, Department of Defense, and National Institute of Health.
Title: TBA
Maryam Majedi |University of Calgary
Abstract: TBA
Designing the Next Generation of User Interfaces for Children
Julia Woodward | University of Florida
Abstract: Children are increasingly interacting with technology, such as touchscreen devices, at home, in the classroom, and at museums. However, these devices are not designed to take into account that children interact with devices differently than adults. As children's everyday use of technology increases, these devices need to be tailored towards children. In this talk, I will present research exploring the differences between how children and adults interact and think about different technology. Our findings lead to a better understanding of how to design technology for children. I will also present some recent work examining how to design information in augmented reality (AR) headsets for both children's and adults' task performance. I will conclude with some takeaways and plans for future work in designing the next generation of user interfaces for children.
About the Speaker: Julia Woodward is a Doctoral Candidate studying Human-Centered Computing in the Department of Computer and Information Science and Engineering at the University of Florida, as well as a National Science Foundation Graduate Research Fellow. Her main research areas include examining how to design better user interfaces tailored towards children, and understanding how children think about and use technology. Through her research, she has identified specific differences between how adults and children interact with technology and has provided recommendations for designing technology for children. Her current dissertation work focuses on understanding how to design information in augmented reality (AR) headsets to aid in adults' and children's task performance and how it differs between the two populations. Julia is graduating this year and plans to continue researching and designing technology tailored for children.
Alireza Shirvani |Saint Louis University
This talk will be held online only on Friday, February 4th, at 4:00 p.m. CST. Please note the special weekday for this event. Zoom details will be provided via the announcement listserv, or you may email dramil1@tulane.edu to request the corresponding link.
Abstract: TBA.
Junzhou Huang |The University of Texas at Arlington
Ivan Ruchkin |University of Pennsylvania
Xueyuan (Michael) Han |Harvard University
Thibaud Lutellier |University of Waterloo
Fall 2021 Colloquia
Check back soon for more information on the computer science seminar series. If you would like to receive notices about upcoming seminars, you can subscribe to the announcement listserv. Unless otherwise noted, the seminars meet on Mondays at 4pm in Stanley Thomas 302. All colloquia this semester will be available for in-person attendance as well as remote attendance via Zoom. Current Tulane faculty, staff, and students are encouraged to attend in person, but, due to the university's Covid-19 protocols, non-Tulane individuals must rely on the Zoom option for attendance. Zoom details will be provided via the announcement listserv, or you may email dramil1@tulane.edu to request the corresponding link.
Complex Proteoform Identification by Top-down Mass Spectrometry
Xiaowen Liu | Biomedical Informatics and Genomics Division, Tulane University
Abstract: Mass spectrometry-based proteomics has been rapidly developed in the past decade, but researchers are still in the early stage of exploring the world of complex proteoforms, which are protein products with various primary structure alterations resulting from gene mutations, alternative splicing, post-translational modifications, and other biological processes. Proteoform identification is essential to mapping proteoforms to their functions and discovering novel proteoforms and new protein functions. Top-down mass spectrometry is the method of choice for identifying complex proteoforms because it provides a "bird's eye view" of intact proteoforms. The combinatorial explosion of various alterations on a protein may result in billions of possible proteoforms, making proteoform identification a challenging computational problem. We propose to use mass graphs to efficiently represent proteoforms and design mass graph alignment algorithms for proteoform identification by top-down mass spectrometry. Experiments on top-down mass spectrometry data sets show that the proposed methods are capable of identifying complex proteoforms with various alterations.
About the Speaker: Dr. Xiaowen Liu is a professor of bioinformatics in the Division of Biomedical Informatics and Genomics, Tulane University School of Medicine. He received his Ph.D. degree in computer science from the City University of Hong Kong in 2008. After 4-year postdoc training at the University of Western Ontario, the University of Waterloo, and the University of California, San Diego, Dr. Liu took positions as an Assistant Professor and Associate Professor at the Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis from 2012 to 2021. Recently, he joined Tulane University School of Medicine. His research focuses on developing computational methods for analyzing mass spectrometry data, especially top-down mass spectrometry data. His lab developed TopPIC suite, a widely used software package for proteoform identification by top-down mass spectrometry.
Interdisciplinary Project Presentations
Tianyi Xu, Taotao Jing, and Haifeng Xia | Computer Science PhD Students, Tulane University
Tianyi Xu
Contextual Bandits With Probing
Abstract: In various practical applications from clinical trials to recommendation systems and anomaly detection, the problem of sequential decision-making is often encountered. Usually, each action (for example, the user's profile) has related information or context, and only the reward for the selected action is revealed (i.e., the bandit feedback). Since the reward is often a random variable, a statistical approach (contextual bandits) can be applied to solving these problems. Different from choosing one arm each time, we consider a novel extension of the bandit learning framework to incorporate joint probing and play. We assume that before the decision maker chooses an arm to play in each round, it can probe a subset of arms and observe their rewards (in that round). The decision maker then picks an arm to play according to the observations obtained in the probing stage and historical data. Our bandit learning model and its extensions can potentially be applied to a large body of sequential decision-making problems that involve joint probing and play under uncertainty. We will present an efficient algorithm for these problems and establish the regret bound using tools from online learning and statistics.
Taotao Jing
Augmented Multi-Modality Fusion for Generalized Zero-Shot Sketch-based Visual Retrieval
Abstract: Augmented Multi-Modality Fusion for Generalized Zero-Shot Sketch-based Visual Retrieval Abstract: Zero-shot sketch-based image retrieval (ZS-SBIR) has attracted great attention recently, due to the potential application of sketch-based retrieval under the zero-shot scenario, where the categories of query sketches and gallery photo pool are not observed in the training stage. However, it is still under insufficient exploration for the general and practical scenario when the query sketches and gallery photos contain both seen and unseen categories. Such problem is defined as generalized zero-shot sketch-based image retrieval (GZS-SBIR), which is also the focus of this work. To this end, we propose a novel Augmented Multi-modality Fusion (AMF) framework to generalize seen concepts to unobserved one efficiently. Specifically, a novel knowledge discovery module named cross-domain augmentation is designed in both visual and semantic space to mimic novel knowledge unseen from the training stage, which is the key to handle GZS-SBIR challenge. Moreover, a triplet domain alignment module is proposed to couple the cross-domain distribution across photo and sketch in visual space. To enhance the robustness of our model, we explore embedding propagation to refine both visual and semantic features by removing undesired noise. Eventually, visual-semantic fusion representations are concatenated for further domain discrimination and task-specific recognition, which tends to trigger the cross-domain alignment in both visual and semantic feature space.In addition to popular ZS-SBIR benchmarks, a new evaluation protocol specifically designed for GZS-SBIR problem is constructed from DomainNet dataset with more diverse sub-domains, and the promising results demonstrate the superiority of the proposed solution over other baselines.
Haifeng Xia
Privacy Protected Multi-Domain Collaborative Learning
Abstract: Unsupervised domain adaptation (UDA) aims to transfer knowledge from one or more well-labeled source domains to improve model performance on the different-yet-related target domain without any annotations. However, existing UDA algorithms fail to bring any benefits to source domains and neglect privacy protection during data sharing. With these considerations, we define Privacy Protected Multi-Domain Collaborative Learning (P2MDCL) and propose a novel Mask-Driven Federated Network (MDFNet) to reach a "win-win" deal for multiple domains with data protected. First, each domain is armed with individual local model via a mask disentangled mechanism to learn domain-invariant semantics. Second, the centralized server refines the global invariant model by integrating and exchanging local knowledge across all domains. Moreover, adaptive self-supervised optimization is deployed to learn discriminative features for unlabeled domains. Finally, theoretical studies and experimental results illustrate rationality and effectiveness of our method on solving P2MDCL.
Hard Instances From Generalized Error Correcting Codes
Victor Bankston | Department of Computer Science, Tulane University
Abstract: Understanding the tractability of certain instances of NP-hard problems is an important issue in modern complexity theory. For example, when restricted to perfect graphs, Independent Set, $\alpha(G)$ can be solved in polynomial time by computing a semidefinite relaxation, Lovasz's theta function $\vartheta(G)$. To better understand the intractability of Independent Set, we seek families of instances of graphs for which $\vartheta(G)$ and $\alpha(G)$ differ to the largest possible extent. We propose a method for constructing such instances based on the fact that $\vartheta(G) > \alpha(G)$ may be viewed as a Bell Inequality. We present a toy model where measurements may be viewed as codes in $\mathbb{F}_5^n$. We then study the Bell Inequalities using the techniques for analyzing error correcting codes. We then study the more natural Pauli measurements and observe that these also have the structure of a generalized error correcting code, an association scheme. Thus, we argue that we can construct hard instances of Independent Set by constructing 2-designs of Pauli measurements.
Automated Deep Learning for Open & Inclusive AI
Jun (Luke) Huan | StylingAI Inc.
Abstract: Big Data, AI, and cloud computing are transforming our society. In areas such as game playing, image classification, and speech recognition, AI algorithms may have already surpassed human experts'capability. We are observing transformations AI and data science produce to industry sectors such as e-commerce, social-networking, finance, health, and transportation among others. My talk covers two parts: (1) a brief introduction to the Baidu AutoDL project where we use deep learning to design deep learning networks and (2) the applications of automated deep learning in different vertical areas including content generation and digital human.
About the Speaker: Dr. Jun (Luke) Huan is the founder, president, and chief scientist of StylingAI Inc., a start-up aiming to developing and applying AI capabilities for automated content generation. Before that he served as a distinguished scientist and the head of Baidu Big Data Laboratory at Baidu Research. Before joining industry, he was the Charles E. and Mary Jane Spahr Professor in the EECS Department at the University of Kansas. From 2015-2018, Dr. Huan worked as a program director at the US NSF in charge of its big data program.
Dr. Huan works on Data Science, AI, Machine Learning and Data Mining. Dr. Huan's research is recognized internationally. He has published more than 150 peer-reviewed papers in leading conferences and journals and has graduated eleven Ph.D. students. He was a recipient of the NSF Faculty Early Career Development Award in 2009. His group won several best paper awards from leading international conferences. Dr. Huan service record includes Program Co-Chair of IEEE BIBM in 2015 and IEEE Big Data 2019.
Special Joint Talk in Both the Algebraic Geometry and Geometric Topology Seminar of the Department of Mathematics and the Department of Computer Science Colloquium
Erin Wolf Chambers| Saint Louis University
This talk will be held on Wednesday, December 8th, at 3:00 p.m. in Gibson 310. Please note the special weekday, time, and venue for this event.
Reeb Graph Metrics From the Ground Up
Abstract: The Reeb graph has been utilized in various applications including the analysis of scalar fields. Recently, research has been focused on using topological signatures such as the Reeb graph to compare multiple scalar fields by defining distance metrics on the topological signatures themselves. In this talk, we will introduce and study five existing metrics that have been defined on Reeb graphs: the bottleneck distance, the interleaving distance, functional distortion distance, the Reeb graph edit distance, and the universal edit distance. This talk covers material from a recent survey paper, which has multiple contributions: (1) provide definitions and concrete examples of these distances in order to develop the intuition of the reader, (2) visit previously proven results of stability, universality, and discriminativity, (3) identify and complete any remaining properties which have only been proven (or disproven) for a subset of these metrics, (4) expand the taxonomy of the bottleneck distance to better distinguish between variations which have been commonly miscited, and (5) reconcile the various definitions and requirements on the underlying spaces for these metrics to be defined and properties to be proven.
About the Speaker: Dr. Erin Wolf Chambers is a Professor at Saint Louis University in the Department of Computer Science, with a secondary appointment in the Department of Mathematics. Her research focus is on computational topology and geometry, with a more general interest in combinatorics and algorithms. Complementing this work, she is also active in projects to support and improve the culture and climate in computer science and mathematics at all levels. She currently serves as editor for several journals, on the board of trustees for the Society for Computational Geometry, and on the SafeToC organizing committee and as an advocate. She received her PhD in Computer Science from the University of Illinois at Urbana-Champaign in 2008, and was a Visiting Research Professor at Saarland University in Summer 2011.
Architectural Support for Interdisciplinary Data Science Research
Lu Peng | Louisiana State University
Abstract: The ever-increasing amount of global data introduces big challenges to computer systems in the aspect of performance, power consumption, reliability, and security. Deep learning and other advanced algorithms have been proposed to handle the problems in the layers of software, however, they require significant computing resources and memory bandwidth. In consequence, traditional CPU-based platforms are no longer the best choices for deploying these algorithms because they do not provide sufficient parallelism. Graphics Processing Units (GPUs) can provide improved performance but at the cost of higher power consumption. FPGAs and ASICs have garnered attention due to their application-specific nature, ability to achieve high degrees of parallelism, and high energy efficiency. In this talk, I will introduce our recent work in the computer system and architectural support for interdisciplinary data science research including hardware accelerator for deep neural networks, accelerator design for smart contracts processing, and an application of blockchain in contact tracing against COVID-19. Other recent work including adapting the B+ tree on Graphics Processing Units (GPUs) and improving resilience for Big Data kernels will be briefly introduced.
About the Speaker: Lu Peng is the Gerard L. "Jerry" Rispone professor with the Division of Electrical and Computer Engineering, Louisiana State University, Baton Rouge, Louisiana. His research interests include computer systems and architecture focusing on many design issues on CPUs and GPUs, hardware accelerators, and applications for deep learning neural networks and blockchains. As PI or Co-PI, he has led or co-led several interdisciplinary research projects with collaborating researchers from different fields: Computer Science, Electrical Engineering, Statistics, Chemistry, Pathobiological Sciences, and Meteorology. His work has been supported by multiple federal and state agencies including NSF, NIH, NRL, DOE/LLNL, ORAU, NASA/LaSpace, BoR, and LSU RoC, as well as industrial companies including Chevron and Xilinx. He was a recipient of the ORAU Ralph E. Power junior faculty enhancement awards in 2007 and the Best Paper Award from IEEE IGSC in 2019 and IEEE ICCD in 2001.
Summer 2021 Colloquia
Check back soon for more information on the computer science seminar series. If you would like to receive notices about upcoming seminars, you can subscribe to the announcement listserv. Unless otherwise noted, the seminars meet on Mondays at 4pm in Stanley Thomas 302.
Dissertation Defense Talk
Majid Mirzanezhad | Computer Science PhD Student, Tulane University
Please join us for Majid Mirzanezhad's PhD dissertation defense as described below. This is our first in-person colloquium in a long time! While there is an option to join remotely, we sincerely hope you will be able to join in person. There will be a reception with snacks afterwards.
This talk will be held on Thursday, July 29th, at 2:00 p.m. CST in Stanley Thomas, Room # 302. Please note the special weekday and time for this event. This presentation will also be delivered online at https://tulane.zoom.us/j/99919478181?pwd=d2wvR0ltbjhWSUF6bVZ6VHIrSHVVZz09.
Abstract: The rapid growth of the need for using Geographic Information Systems (GIS), for a better understanding of the environment, has led many researchers and practitioners of various disciplines to design efficient algorithmic methods for confronting the real-world problems arising in the realm of intelligent transportation systems, urban planning, mobility, surveillance systems, and other disciplines over the past few decades. In this dissertation, we consider several topics in computational geometry that involve applications in maps and networks in GIS. We first propose several algorithms that capture the similarity between linear features, notably curves, whose edges are relatively long. One of the popular metrics to capture the similarity between curves is the Fréchet distance. We give a linear-time greedy algorithm deciding and approximating the Fréchet distance and a near linear-time algorithm computing the exact Fréchet distance between two curves in any constant dimension. We also propose several efficient data structures for the approximate nearest-neighbor problem and distance oracle queries among curves under the Fréchet distance.
We exploit the metric studied above for simplification purposes. We specifically consider the problem of simplifying a feature, e.g., graph/tree/curve with an alternative feature of minimum-complexity such that the distance between the input and simplified features remains at most some threshold. We propose several algorithmic and NP-hardness results based on the distance measure we use and the vertex placement of the simplified feature that can be selected from the input's vertices, or its edges, or any points in the ambient space.
About the Speaker: Majid Mirzanezhad is a Ph.D. candidate in the Department of Computer Science at Tulane University. His research area is on computational geometry with applications in GIS and primarily focused on approximation algorithms and data structures for curves and graphs. Prior to pursuing his Ph.D., Majid received his MSc and BSc in computer science from Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran.
Check back soon for more information on the computer science seminar series. If you would like to receive notices about upcoming seminars, you can subscribe to the announcement listserv. Unless otherwise noted, the seminars meet on Mondays at 4pm in Stanley Thomas 302. However, due to the current pandemic, all colloquia are being conducted virtually.
Karthik Shivaram and Xintian Li | Computer Science PhD Students, Tulane University
These presentations will be delivered online. You may access the presentations on Monday, April 26th, at 4:00 pm CST via the following link: https://tulane.zoom.us/j/94777604484?pwd=MGNHUGNWb09va05NQ0taNDI1NDduUT09 . Meeting ID: 947 7760 4484. Passcode: 511369. Please be sure to mute your microphone when you log on.
Karthik Shivaram
Combating Partisan Homogenization in Content-Based News Recommendation Systems
Abstract: Content-based news recommendation systems build user profiles to identify important terms and phrases that correlate with the user's engagement to make accurate recommendations. Prior work by Ping et al. [1] suggests that these recommendation systems tend to have a homogenization effect when a user's political views are diverse over a set of topics. In this work we propose a novel attention-based neural network architecture in a multitask learning setting to overcome this problem of partisan homogenization.
Xintian Li
Evacuation Diffusion Modeling from Twitter
Abstract: Evacuations have a significant impact on saving human lives during hurricanes. However, as a complex dynamic process, it is typically difficult to know the individual evacuation decisions in real time. Since a large amount of information is continuously posted through social media platforms from all populations, we can use them to predict individual evacuation behavior. In this project, we collect tweets during Hurricane Irma 2017, and train a text classifier in an active-learning way to identify tweets indicating positive evacuation decisions from both negative and irrelevant ones. We predict the demographic information for each identified evacuee, based on which we use time series modeling to predict evacuation rate changes over time. We also use the demographic information to help predict possible evacuees in different time ranges. The results can be used to help inform planning strategies of emergency response agencies. .