Here's the homepage for the director of AI at Tesla
https://karpathy.ai/
Andrej Karpathy
2014-09-02
Tianlin Shi; Andrej Karpathy; Linxi Fan; Jonathan Hernandez; Percy Liang
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 2017 - 2022
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 I was the Sr. Director of AI at Tesla, where I led the computer vision team of <a href="https://www.tesla.com/autopilot">Tesla Autopilot</a>. This includes in-house data labeling, neural network training, the science of making it work, and deployment in production running on our custom inference chip. Today, the Autopilot increases the safety and convenience of driving, but the team's goal is to develop and deploy <a href="https://www.youtube.com/watch?v=tlThdr3O5Qo">Full Self-Driving</a> to our rapidly growing fleet of millions of cars. Our Aug 2021 <a href="https://youtu.be/j0z4FweCy4M?t=2900">Tesla AI Day</a> provides the most detailed and up-to-date overview of this effort.
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 2015 - 2017
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 I was a research scientist and a founding member at <a href="https://openai.com/">OpenAI</a>.
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 2011 - 2015
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 My PhD was focused on convolutional/recurrent neural networks and their applications in computer vision, natural language processing and their intersection. My adviser was <a href="http://vision.stanford.edu/">Fei-Fei Li</a> at the Stanford Vision Lab and I also had the pleasure to work with <a href="https://ai.stanford.edu/users/koller/">Daphne Koller</a>, <a href="http://www.robotics.stanford.edu/~ang/contact.html">Andrew Ng</a>, <a href="http://robots.stanford.edu/">Sebastian Thrun</a> and <a href="http://vladlen.info/">Vladlen Koltun</a> along the way during the first year rotation program.
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 I designed and was the primary instructor for the first deep learning class Stanford - <a href="http://cs231n.stanford.edu/">CS 231n: Convolutional Neural Networks for Visual Recognition</a>. The class became one of the largest at Stanford and has grown from 150 enrolled in 2015 to 330 students in 2016, and 750 students in 2017.
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 Along the way I squeezed in 3 internships at (a baby) Google Brain in 2011 working on learning-scale unsupervised learning from videos, then again in Google Research in 2013 working on large-scale supervised learning on YouTube videos, and finally at DeepMind in 2015 working on the deep reinforcement learning team.
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 2009 - 2011
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 MSc at the University of British Columbia where I worked with <a href="https://www.cs.ubc.ca/~van/">Michiel van de Panne</a> on learning controllers for physically-simulated figures, i.e., machine-learning for agile robotics but in a physical simulation.
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 2005 - 2009
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 BSc at the University of Toronto with a double major in computer science and physics and a minor in math. This is where I first got into deep learning, attending <a href="https://www.cs.toronto.edu/~hinton/">Geoff Hinton's</a> class and reading groups.
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 <p>teaching</p>
 
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 <p>pet projects</p>
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 <p><a href="https://github.com/karpathy/micrograd">micrograd</a> is a tiny scalar-valued autograd engine (with a bite! :)). It implements backpropagation (reverse-mode autodiff) over a dynamically built DAG and a small neural networks library on top of it with a PyTorch-like API.</p>
 
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 <p><a href="https://cs.stanford.edu/people/karpathy/convnetjs/">ConvNetJS</a> is a deep learning library written from scratch entirely in Javascript. This enables nice web-based demos that train convolutional neural networks (or ordinary ones) entirely in the browser. Many web demos included. I did an interview with Data Science Weekly about the library and some of its back story <a href="https://www.datascienceweekly.org/data-scientist-interviews/training-deep-learning-models-browser-andrej-karpathy-interview">here</a>. Also see my later followups such as <a href="https://github.com/karpathy/tsnejs">tSNEJS</a>, <a href="https://github.com/karpathy/reinforcejs">REINFORCEjs</a>, or <a href="https://github.com/karpathy/reinforcejs">recurrentjs</a>, <a href="https://cs.stanford.edu/people/karpathy/gan/">GANs in JS</a>.</p>
 
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 <p>How productive were you today? How much code have you written? Where did your time go? For a while I was really into tracking my productivity, and since I didn't like that RescueTime uploads your (very private) computer usage statistics to a cloud I wrote my own, privacy-first, tracker - <a href="https://github.com/karpathy/ulogme">ulogme</a>! That was fun.</p>
 
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 <div><p>misc: I built a lot of other random stuff over time. <a href="https://www.youtube.com/watch?v=VaW1dmqRE0o">Rubik's cube color extractor</a>, <a href="https://sites.google.com/site/scriptbotsevo/">predator prey neuroevolutionary multiagent simulations</a>, <a href="https://www.youtube.com/watch?v=2kupe2ZKK58">more of those</a>, <a href="https://www.youtube.com/watch?v=6LmQS4DJl6c">sketcher bots</a>, games for computer game competitions <a href="https://www.youtube.com/watch?v=EH-xVtuv8iI">#1</a>, <a href="https://www.youtube.com/watch?v=mcL1n7a90rQ">#2</a>, <a href="https://www.youtube.com/watch?v=LAtEVB3Mhyk">#3</a>, random <a href="https://www.youtube.com/watch?v=yqdfCQ5og3E">computer graphics things</a>, <a href="https://www.youtube.com/watch?v=mSaO0Ul_55c">Tetris AI</a>, <a href="https://code.google.com/archive/p/nplayertetris/">multiplayer coop tetris</a>, etc.</p></div>
 
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 <p>publications</p>
 
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 <p>ICLR 2017</p>
 <p>Tim Salimans, Andrej Karpathy, Xi Chen, Diederik P. Kingma, and Yaroslav Bulatov</p>
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 <p>CVPR 2016 (Oral)</p>
 <p>Justin Johnson*, Andrej Karpathy*, Li Fei-Fei</p>
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 <p>ICLR 2016 Workshop</p>
 <p>Andrej Karpathy*, Justin Johnson*, Li Fei-Fei</p>
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 <p>CVPR 2015 (Oral)</p>
 <p>Andrej Karpathy, Li Fei-Fei</p>
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 <p>IJCV 2015</p>
 <p>Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, Li Fei-Fei</p>
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 <p>NIPS 2014</p>
 <p>Andrej Karpathy, Armand Joulin, Li Fei-Fei</p>
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 <p>CVPR 2014 (Oral)</p>
 <p>Andrej Karpathy, George Toderici, Sanketh Shetty, Thomas Leung, Rahul Sukthankar, Li Fei-Fei</p>
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 <p>TACL 2013</p>
 <p>Richard Socher, Andrej Karpathy, Quoc V. Le, Christopher D. Manning, Andrew Y. Ng</p>
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 <p>ICRA 2013</p>
 <p>Andrej Karpathy, Stephen Miller, Li Fei-Fei</p>
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 <p>NIPS 2012</p>
 <p>Adam Coates, Andrej Karpathy, Andrew Ng</p>
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 <p>AI 2012</p>
 <p>Andrej Karpathy, Michiel van de Panne</p>
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 <p>SIGGRAPH 2011</p>
 <p>Stelian Coros, Andrej Karpathy, Benjamin Jones, Lionel Reveret, Michiel van de Panne</p>
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 </div>||||I|||| Andrej Karpathy

 I like to train deep neural nets on large datasets 🧠🤖💥

 2017 - 2022
 I was the Sr. Director of AI at Tesla, where I led the computer vision team of Tesla Autopilot. This includes in-house data labeling, neural network training, the science of making it work, and deployment in production running on our custom inference chip. Today, the Autopilot increases the safety and convenience of driving, but the team's goal is to develop and deploy Full Self-Driving to our rapidly growing fleet of millions of cars. Our Aug 2021 Tesla AI Day provides the most detailed and up-to-date overview of this effort.
 2015 - 2017
 I was a research scientist and a founding member at OpenAI.
 2011 - 2015
 My PhD was focused on convolutional/recurrent neural networks and their applications in computer vision, natural language processing and their intersection. My adviser was Fei-Fei Li at the Stanford Vision Lab and I also had the pleasure to work with Daphne Koller, Andrew Ng, Sebastian Thrun and Vladlen Koltun along the way during the first year rotation program.

 I designed and was the primary instructor for the first deep learning class Stanford - CS 231n: Convolutional Neural Networks for Visual Recognition. The class became one of the largest at Stanford and has grown from 150 enrolled in 2015 to 330 students in 2016, and 750 students in 2017.

 Along the way I squeezed in 3 internships at (a baby) Google Brain in 2011 working on learning-scale unsupervised learning from videos, then again in Google Research in 2013 working on large-scale supervised learning on YouTube videos, and finally at DeepMind in 2015 working on the deep reinforcement learning team.
 2009 - 2011
 MSc at the University of British Columbia where I worked with Michiel van de Panne on learning controllers for physically-simulated figures, i.e., machine-learning for agile robotics but in a physical simulation.
 2005 - 2009
 BSc at the University of Toronto with a double major in computer science and physics and a minor in math. This is where I first got into deep learning, attending Geoff Hinton's class and reading groups.

 featured talks
 Robot Brains podcast with Pieter Abbeel
 Tesla AI Day 2021
 AI for Full Self-Driving @ CVPR 2021
 AI for Full Self-Driving @ ScaledML 2020
 Tesla Autonomy Day 2019
 Multi-Task Learning in the Wilderness @ ICML 2019
 PyTorch at Tesla @ PyTorch DevCon 2019
 Building the Software 2.0 stack @ Spark-AI 2018
 2017 RE•WORK Summit with Nathan Benaich
 2017 "Heroes of Deep Learning" with Andrew Ng
 2017 Deep RL Bootcamp with Pieter Abbeel et al
 2016 Bay Area Deep Learning School: CNNs
 Deep Learning Workshop @ CVPR 2016
 RE•WORK Deep Learning Summit 2016
 NVIDIA GTC Keynote 2015 with Jensen Huang
 teaching
 In 2015 I designed and was the primary instructor for the first deep learning class Stanford - CS 231n: Convolutional Neural Networks for Visual Recognition ❤️. The class became one of the largest at Stanford and has grown from 150 enrolled in 2015 to 330 students in 2016, and 750 students in 2017.
 * my 2016 lecture videos
 * course notes
 * course syllabus
 * r/cs231n






 featured writing
 * Mar 2021 A from-scratch tour of Bitcoin in Python
 * Mar 2021 Short Story on AI: Forward Pass
 * Jun 2020 Biohacking Lite
 * Apr 2019 A Recipe for Training Neural Networks
 * Nov 2017 Software 2.0
 * Sep 2016 A Survival Guide to a PhD
 * Nov 2015 Short Story on AI: A Cognitive Discontinuity
 * May 2015 The Unreasonable Effectiveness of Recurrent Neural Networks
 * Sep 2014 What I learned from competing against a ConvNet on ImageNet
 * Oct 2012 The state of Computer Vision and AI: we are really, really far away
 pet projects
 micrograd is a tiny scalar-valued autograd engine (with a bite! :)). It implements backpropagation (reverse-mode autodiff) over a dynamically built DAG and a small neural networks library on top of it with a PyTorch-like API.
 char-rnn was a Torch character-level language model built out of LSTMs/GRUs/RNNs. Related to this also see the Unreasonable Effectiveness of Recurrent Neural Networks blog post, or the minimal RNN gist.
 arxiv-sanity tames the overwhelming flood of papers on Arxiv. It allows researchers to discover relevant papers, search/sort by similarity, see recent/popular papers, and get recommendations. Deployed live at arxiv-sanity.com. My obsession with meta research involved many more projects over the years, e.g. see pretty NIPS 2020 papers, research lei, scholaroctopus, and biomed-sanity. Update: my most revent arxiv-sanity-lite from-scratch rewrite is much better.
 neuraltalk2 was an early image captioning project in (lua)Torch. Also see our later extension with Justin Johnson to dense captioning.
 I am sometimes jokingly referred to as the reference human for ImageNet because I competed against an early ConvNet on categorizing images into 1,000 classes. This required a bunch of custom tooling and a lot of learning about dog breeds. See the blog post "What I learned from competing against a ConvNet on ImageNet". Also a Wired article.
 ConvNetJS is a deep learning library written from scratch entirely in Javascript. This enables nice web-based demos that train convolutional neural networks (or ordinary ones) entirely in the browser. Many web demos included. I did an interview with Data Science Weekly about the library and some of its back story here. Also see my later followups such as tSNEJS, REINFORCEjs, or recurrentjs, GANs in JS.
 How productive were you today? How much code have you written? Where did your time go? For a while I was really into tracking my productivity, and since I didn't like that RescueTime uploads your (very private) computer usage statistics to a cloud I wrote my own, privacy-first, tracker - ulogme! That was fun.
 misc: I built a lot of other random stuff over time. Rubik's cube color extractor, predator prey neuroevolutionary multiagent simulations, more of those, sketcher bots, games for computer game competitions #1, #2, #3, random computer graphics things, Tetris AI, multiplayer coop tetris, etc.
 publications
 World of Bits: An Open-Domain Platform for Web-Based Agents
 ICML 2017
 Tianlin (Tim) Shi, Andrej Karpathy, Linxi (Jim) Fan, Jonathan Hernandez, Percy Liang
 PixelCNN++: A PixelCNN Implementation with Discretized Logistic Mixture Likelihood and Other Modifications
 ICLR 2017
 Tim Salimans, Andrej Karpathy, Xi Chen, Diederik P. Kingma, and Yaroslav Bulatov
 Connecting Images and Natural Language (PhD thesis)
 2016
 Andrej Karpathy
 DenseCap: Fully Convolutional Localization Networks for Dense Captioning
 CVPR 2016 (Oral)
 Justin Johnson*, Andrej Karpathy*, Li Fei-Fei
 Visualizing and Understanding Recurrent Networks
 ICLR 2016 Workshop
 Andrej Karpathy*, Justin Johnson*, Li Fei-Fei
 Deep Visual-Semantic Alignments for Generating Image Descriptions
 CVPR 2015 (Oral)
 Andrej Karpathy, Li Fei-Fei
 ImageNet Large Scale Visual Recognition Challenge
 IJCV 2015
 Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, Li Fei-Fei
 Deep Fragment Embeddings for Bidirectional Image-Sentence Mapping
 NIPS 2014
 Andrej Karpathy, Armand Joulin, Li Fei-Fei
 Large-Scale Video Classification with Convolutional Neural Networks
 CVPR 2014 (Oral)
 Andrej Karpathy, George Toderici, Sanketh Shetty, Thomas Leung, Rahul Sukthankar, Li Fei-Fei
 Grounded Compositional Semantics for Finding and Describing Images with Sentences
 TACL 2013
 Richard Socher, Andrej Karpathy, Quoc V. Le, Christopher D. Manning, Andrew Y. Ng
 Object Discovery in 3D scenes via Shape Analysis
 ICRA 2013
 Andrej Karpathy, Stephen Miller, Li Fei-Fei
 Emergence of Object-Selective Features in Unsupervised Feature Learning
 NIPS 2012
 Adam Coates, Andrej Karpathy, Andrew Ng
 Curriculum Learning for Motor Skills
 AI 2012
 Andrej Karpathy, Michiel van de Panne
 Locomotion Skills for Simulated Quadrupeds
 SIGGRAPH 2011
 Stelian Coros, Andrej Karpathy, Benjamin Jones, Lionel Reveret, Michiel van de Panne

 Also on Google Scholar
 misc unsorted
 * Neural Networks: Zero To Hero lecture series
 * Mastodon
 * Some of my favorite tweets over time
 * My primary blog and my other blog
 * I like sci-fi. I enumerated and sorted sci-fi books I've read here
 * Justin Johnson and I held a reading group on Clubhouse. See YouTube or as podcast.
 * I joined the 2020 edition of MIT Technology Review's 35 Innovators Under 35
 * I like and use Goodreads for keeping track of reading, janky as it is.
 * I used to use Quora. I do not think they made the right choice with aggressive paywalls.
 * Loss function Tumblr :D! My collection of funny loss functions.
 * Some advice for undergrads and advice for those considering or pursuing a PhD
 * New York Times article covering my PhD image captioning work.
 * MIT Technology Review amusing article from a long time ago.
 * Long ago interview with data science weekly.
 * t-SNE visualization of CNN codes for ImageNet, pretty!
 * A long time ago I was really into Rubik's Cubes. I learned to solve them in about 17 seconds and then, frustrated by lack of learning resources, created YouTube videos explaining the Speedcubing methods. These went on to become relatively popular. There's also my long dead cubing page. Oh, and a video of me at a Rubik's cube competition :)
 * 0 frameworks were used to make this simple responsive website because I am becoming seriously allergic to 500-pound websites. This one is pure HTML and CSS in two static files and that's it.











2017 - 2022
 
 
 
 
  
 
 
 I was the Sr. Director of AI at Tesla, where I led the computer vision team of Tesla Autopilot. This includes in-house data labeling, neural network training, the science of making it work, and deployment in production running on our custom inference chip. Today, the Autopilot increases the safety and convenience of driving, but the team's goal is to develop and deploy Full Self-Driving to our rapidly growing fleet of millions of cars. Our Aug 2021 Tesla AI Day provides the most detailed and up-to-date overview of this effort.
 
 
 
 
 
 
 2015 - 2017
 
 
 
 
  
 
 I was a research scientist and a founding member at OpenAI.
 
 
 
 
 2011 - 2015
 
 
 
 
  
  
 My PhD was focused on convolutional/recurrent neural networks and their applications in computer vision, natural language processing and their intersection. My adviser was Fei-Fei Li at the Stanford Vision Lab and I also had the pleasure to work with Daphne Koller, Andrew Ng, Sebastian Thrun and Vladlen Koltun along the way during the first year rotation program.
  
 I designed and was the primary instructor for the first deep learning class Stanford - CS 231n: Convolutional Neural Networks for Visual Recognition. The class became one of the largest at Stanford and has grown from 150 enrolled in 2015 to 330 students in 2016, and 750 students in 2017.
  
 Along the way I squeezed in 3 internships at (a baby) Google Brain in 2011 working on learning-scale unsupervised learning from videos, then again in Google Research in 2013 working on large-scale supervised learning on YouTube videos, and finally at DeepMind in 2015 working on the deep reinforcement learning team.
  
 
 
 
 2009 - 2011
 
 
 
 
  
 
 MSc at the University of British Columbia where I worked with Michiel van de Panne on learning controllers for physically-simulated figures, i.e., machine-learning for agile robotics but in a physical simulation.
 
 
 
 
 2005 - 2009
 
 
 
 
  
 
 BSc at the University of Toronto with a double major in computer science and physics and a minor in math. This is where I first got into deep learning, attending Geoff Hinton's class and reading groups.
 
 
  
 teaching
 
 
   
 pet projects
 
  
  micrograd is a tiny scalar-valued autograd engine (with a bite! :)). It implements backpropagation (reverse-mode autodiff) over a dynamically built DAG and a small neural networks library on top of it with a PyTorch-like API.
 
 
 
  
 
 
 
 
 
 
 
  
  ConvNetJS is a deep learning library written from scratch entirely in Javascript. This enables nice web-based demos that train convolutional neural networks (or ordinary ones) entirely in the browser. Many web demos included. I did an interview with Data Science Weekly about the library and some of its back story here. Also see my later followups such as tSNEJS, REINFORCEjs, or recurrentjs, GANs in JS.
 
 
 
  
 How productive were you today? How much code have you written? Where did your time go? For a while I was really into tracking my productivity, and since I didn't like that RescueTime uploads your (very private) computer usage statistics to a cloud I wrote my own, privacy-first, tracker - ulogme! That was fun.
 
 
 
  
  misc: I built a lot of other random stuff over time. Rubik's cube color extractor, predator prey neuroevolutionary multiagent simulations, more of those, sketcher bots, games for computer game competitions #1, #2, #3, random computer graphics things, Tetris AI, multiplayer coop tetris, etc. 
 
 
  
 publications
 
 
 
 ICLR 2017
 Tim Salimans, Andrej Karpathy, Xi Chen, Diederik P. Kingma, and Yaroslav Bulatov
 
 
 
 
 CVPR 2016 (Oral)
 Justin Johnson*, Andrej Karpathy*, Li Fei-Fei
 
 
 
 ICLR 2016 Workshop
 Andrej Karpathy*, Justin Johnson*, Li Fei-Fei
 
 
 
 CVPR 2015 (Oral)
 Andrej Karpathy, Li Fei-Fei
 
 
 
 IJCV 2015
 Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, Li Fei-Fei
 
 
 
 NIPS 2014
 Andrej Karpathy, Armand Joulin, Li Fei-Fei
 
 
 
 CVPR 2014 (Oral)
 Andrej Karpathy, George Toderici, Sanketh Shetty, Thomas Leung, Rahul Sukthankar, Li Fei-Fei
 
 
 
 TACL 2013
 Richard Socher, Andrej Karpathy, Quoc V. Le, Christopher D. Manning, Andrew Y. Ng
 
 
 
 ICRA 2013
 Andrej Karpathy, Stephen Miller, Li Fei-Fei
 
 
 
 NIPS 2012
 Adam Coates, Andrej Karpathy, Andrew Ng
 
 
 
 AI 2012
 Andrej Karpathy, Michiel van de Panne
 
 
 
 SIGGRAPH 2011
 Stelian Coros, Andrej Karpathy, Benjamin Jones, Lionel Reveret, Michiel van de Panne
Andrej Karpathy
https://karpathy.ai/