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Anima Anandkumar on Muck Rack

Anima Anandkumar

Ithaca
As seen in: Medium, Nature, arXiv, IEEE Spectrum, Caltech, Science Magazine, Physical Review B, Quanta Magazine, Pasadena Now, SIAM News and
AI+Science, Bren Professor @caltech, Time100, Fmr Sr Director of #AI research @nvidia Fmr Principal Scientist @awscloud
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Articles

Principled approaches for extending neural architectures to function spaces for operator learning

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By Julius Berner, Miguel Liu-Schiaffini, Jean Kossaifi, Valentin Duruisseaux, Boris Bonev, Kamyar Azizzadenesheli, Anima Anandkumar
| Nature Verified
Abstract Deep learning has achieved remarkable success in computer vision and natural language processing, where tasks are commonly formulated as mappings between finite-dimensional representations. Many scientific problems, however, including those governed by partial differential equations, are naturally posed on infinite-dimensional function spaces. This mismatch has limited conventional neural networks from achieving comparable success in scientific applications.
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Fourier neural operators for learning dynamics in quantum spin systems

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By Freya Shah, Taylor L. Patti, Julius Berner, Bahareh Tolooshams, Jean Kossaifi, Anima Anandkumar
| Nature Verified
Abstract Fourier Neural Operators (FNOs) excel on functional data tasks, such as those originating from partial differential equations. This renders them effective at simulating the time evolution of quantum wavefunctions, a computationally challenging, yet coveted task. In this manuscript, we use FNOs to model the evolution of random quantum spin systems, chosen due to their representative dynamics.
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Formalizing Neural Networks in Lean

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By Anima Anandkumar
| leandojo.org
Abstract Neural networks are increasingly deployed in safety- and mission-critical pipelines, yet many verification and analysis results are produced outside the programming environment that defines and runs the model. This separation creates a semantic gap between the executed network and the analyzed artifact, so guarantees can hinge on implicit conventions such as operator semantics, tensor layouts, preprocessing, and floating-point corner cases.
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