Anish Athalye
Director, AI Research at Handshake
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolAnish Athalye is a San Francisco–based AI and systems engineer with 13 years of experience bridging research and product—co-founder and former CTO of Cleanlab and now Director of AI Research at Handshake. He holds advanced degrees from MIT (SB, MEng, PhD) and has interned at OpenAI, Dropbox, and Google, blending rigorous academic depth with practical engineering. Anish is a prolific open-source maintainer and contributor, from system-level work on xv6 and dotfiles to ML tooling and popular educational projects like The Missing Semester. His contributions span full-stack development, ML engineering, and devops, and include hands-on work improving model-centered labs and production-focused data-quality tooling. Notably, he built user-facing utilities (lumen auto-brightness, git-remote-dropbox) that combine careful systems optimization with polished UX—reflecting a rare mix of low-level systems skill and product sensibility.
13 years of coding experience
5 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Science (systems, security, verification, AI), Doctor of Philosophy - PhD, Computer Science (systems, security, verification, AI) at Massachusetts Institute of Technology
Github Skills (87)
opencv10
page-layout10
custom-theme10
beamer10
python10
image-processing10
testing10
command-line-interface10
c1110
c1710
vim10
website-design10
flask10
operating-system10
customizer10
Programming languages (28)
Github contributions (5)
anishathalye/neural-style
Nov 2015 - Dec 2022
Neural style in TensorFlow! 🎨
Role in this project:
ML Engineer
ML EngineerContributions:83 commits, 36 PRs, 119 pushes in 7 years 2 months
Contributions summary:Anish's contributions primarily focused on modifying and improving the neural style transfer implementation within the repository. They made several significant changes, including fixing style scaling issues, moving the Gram matrix computation, adding total variation regularization, and switching to initialization with noise. These modifications indicate a focus on improving the algorithm's functionality and performance. Further, the user added a command-line interface, showcasing an interest in making the tool more accessible.
backpropagationdeep-learningmachine-learningneural-styleneural-network
anishathalye/dotbot
Mar 2014 - Dec 2022
A tool that bootstraps your dotfiles ⚡️
Role in this project:Back-end Developer & DevOps Engineer
Back-end Developer & DevOps EngineerContributions:220 commits, 94 PRs, 270 pushes in 8 years 10 months
Contributions summary:Anish made several contributions to the `dotbot` project, including adding a cleaner executor to remove broken symbolic links and improving the linker executor's functionality, such as adding support for forced links and creating parent directories. They also addressed source compatibility issues with Python 3 and added YAML support for configuration files. Furthermore, the user implemented an extended configuration syntax for shell commands, enhancing their flexibility and control over input/output streams. Finally, the user contributed to the testing framework, adding tests to improve the robustness of the project.
dotfilesbashshellzsh
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