About me

I am a Founding Scientist at Liquid AI. I am also finishing my PhD in computational math (ICME) at Stanford University, advised by Scott Linderman. My research interests are broadly related to dynamical systems and efficient sequence modeling. Before Stanford, I obtained a master’s degree at MIT in the LGO program and attended undergrad at Georgia Tech. I have previously interned as a PhD machine learning research scientist at NVIDIA in the Learning and Perception team and also worked in machine learning research roles at Dell and Goodyear.

Publications

Convolutional State Space Models for Long-Range Spatiotemporal Modeling
Jimmy T.H. Smith, Shalini De Mello, Jan Kautz, Scott W. Linderman, Wonmin Byeon
Advances in Neural Information Processing Systems (NeurIPS) 2023.

Simplified State Space Layers for Sequence Modeling
Jimmy T.H. Smith, Andrew Warrington, Scott W. Linderman
International Conference on Learning Representations (ICLR) 2023. Selected for Oral Presentation (top 5% of accepted papers, top 1.5% of all submissions)

Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systems
Jimmy T.H. Smith, Scott W. Linderman, David Sussillo
Advances in Neural Information Processing Systems (NeurIPS) 2021.

Bayesian Inference in Augmented Bow Tie Networks
Jimmy T.H. Smith, Dieterich Lawson, Scott W. Linderman
Bayesian Deep Learning Workshop, NeurIPS 2021.