Thank you @Caltech for including me in the history of AI. It starts with Carver Mead, John Hopfield and Richard Feynman coming together to teach a course on physics of computation. Not many are aware that the main AI conference, NeurIPS, started at @Caltech
@animaanandkumar
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SignSGD and Signum: Sign-Based Gradient Optimization Methods
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When we derived SignSGD and Signum, it was surprising how just keeping the sign of stochastic gradients is sufficient for convergence and we could prove that theoretically. In practice, Signum and derivatives like Lion work well even at large scale. It is interesting to see Adam
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FunDPS: Guided Diffusion Sampling Framework for PDE Solutions
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Excited to introduce our latest work, Guided Diffusion Sampling on Function Spaces (FunDPS) (
https://
arxiv.org/abs/2505.17004) – a discretization-agnostic generative framework for solving PDE-based forward and inverse problems. Diffusion-based posterior sampling on function spaces: Our -
AI Understanding Physical World for Scientific Discovery
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It was an honor to be part of @Google IO Dialogues stage with James Manyika, Pushmeet Kohli and Joëlle Barral and talk about AI+Science. I talked about how AI needs to understand the physical world in order to make new scientific discoveries. While LLMs can come up with new
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Beyond LLMs: AI’s Role in Scientific Discovery
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In a recent interview I talk about what it takes for AI to make new scientific discoveries. tldr: it won’t be just LLMs.
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AI and Science Panel at Google IO 2026
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Great to be part of the panel on ai+science at @Google IO
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LeanDojo: Open-Source Framework for AI Theorem Proving
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LeanDojo was the first opensource LLM+Lean framework. We continue to build and have new tools like Lean Copilot, Lean Agent, and Lean Progress that significantly enhance theorem proving workflows for mathematicians. All of our code is here : http://
github.com/lean-dojo/ Lean Dojo -
LeanDojo: Open Source LLM Framework Enhances Theorem Proving
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LeanDojo was the first opensource LLM+Lean framework. We continue to build and have new tools like Lean Copilot, Lean Agent, and Lean Progress that significantly enhance theorem proving workflows for mathematicians. All of our code is here : http://
github.com/lean-dojo/ Lean Dojo -
Dictionary Learning Methods Show Promise for Data Representation
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Cool to see dictionary learning come back. We had a work from decade ago showing nice properties for local methods to recover good dictionaries
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Neural Operators for Learning PDEs Survey
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We proposed neural operators for learning PDEs. You can see a survey here https://
rdcu.be/dD6LO