Anima Anandkumar
Bren Professor of Computing, Caltech | Former Senior Director of AI Research, NVIDIA
Bren Professor of Computing and Mathematical Sciences at Caltech and former Senior Director of AI Research at NVIDIA. ACM Fellow and IEEE Fellow. World-renowned authority on tensor algebra, non-convex optimization, and AI for science. Pioneer of Neural Operators—specifically Fourier Neural Operators (FNO)—which solve complex partial differential equations (PDEs) for weather prediction, aerodynamics, and carbon sequestration up to 100,000x faster than traditional numerical solvers.
Areas of focus
Professional niches
Proof of Work
Eureka: Human-Level Reward Design via Coding Large Language Models
Created Eureka, an autonomous algorithm leveraging frontier coding LLMs to synthesize, evaluate, and iteratively refine complex reward algorithms for reinforcement learning, outperforming human expert-designed rewards across 29 dexterous robot manipulation tasks.
Requires fast GPU simulation environments to provide high-throughput performance feedback to the outer-loop LLM.
Context: GPT-4 reward coding loops interacting with NVIDIA Isaac Gym GPU physics simulations.
View missionFourier Neural Operator for Parametric Partial Differential Equations (FNO)
Pioneered the Fourier Neural Operator (FNO), formulating a deep learning architecture that directly learns mappings between infinite-dimensional function spaces via Fast Fourier Transforms, achieving mesh-independent zero-shot super-resolution on Navier-Stokes fluid turbulence 1,000x faster than standard numerical solvers.
Periodic boundary condition assumptions require specialized padding/coordinate embeddings for complex arbitrary 3D boundaries.
Context: Fourier spectral convolution layers evaluated on 2D/3D Navier-Stokes, Darcy Flow, and global atmospheric weather forecasting (FourCastNet).
View mission