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The University of Chicago and the California Institute of Technology are centers of gravity for the study, application, and use of AI and Machine Learning. By combining efforts and with a gift from the Margot and Tom Pritzker Foundation, the two institutions aim to accelerate the pace of discovery across the physical and biological sciences by advancing the integration of AI into scientific research and development and training new generations of interdisciplinary scientists. 

The new grant from the Margot and Tom Pritzker Foundation includes funds for the annual The University of Chicago and Caltech Conference on AI+Science, which brings together an elite and diverse cohort of leading researchers in core AI and domain sciences to lead conversations and drive partnerships that will shape future inquiry, industry investment, and entrepreneurial opportunities. The generous gift also includes three prestigious new awards for research excellence in AI+Science, which will be announced at the conference. Finally, the funding supports four postdoctoral researchers, six graduate students, and two sabbatical visitors yearly, with appointments evenly split between DSI and Caltech.

2025 Conference Information

2023 Conference Information

 

 

Postdoctoral Researchers

Talk Title: Covariant Neural Networks for Physics Applications

Watch Alexander’s Research Lightning Talk

Talk Abstract: Most traditional neural network architectures do not respect any intrinsic structure of the input data, and instead are expect to “learn” it. CNNs are the first widespread example of a symmetry, in this case the translational symmetry of images, being used to advise much more efficient and transparent network architectures. More recently, CNNs were generalized to other non-commutative symmetry groups such as SO(3). However, in physics application one is more likely to encounter input data that belong to linear representations of Lie Groups, as opposed to being functions (or “images”) on a symmetric space of the group.

To deal with such problems, I will present a general feed-forward architecture that takes vectors as inputs, works entirely in the Fourier space of the symmetry group, and is fully covariant. This approach allows one to achieve equal performance with drastically fewer learnable parameters, Moreover, the models become much more physically meaningful and more likely to be interpretable. My application of choice is in particle physics, where the main symmetry is the 6-dimensional Lorentz group. I will demonstrate the success of covariant architectures compared to more conventional approaches.

Bio: I am a PhD student at the University of Chicago working on theoretical hydrodynamics problems in relation to the quantum Hall effect. In addition, I am working on developing new group-covariant machine learning tools for physics applications, such as Lorentz-covariant neural networks for particle physics. My background is in mathematical physics, in which I hold a master’s degree from the Saint-Petersburg University in Russia. My interests lie on the intersection of theoretical and mathematical physics and new inter-disciplinary applications of such ideas.

Jake’s research interests include high-dimensional statistics, empirical Bayes, multiple testing, and distribution-free inference. Jake is also interested in modeling incentives in data analysis. He has worked on real data problems in precision agriculture, personalization, online market behavior, microscopy, and astronomy. He is co-advised by Prof. Rina Foygel Barber and Prof. Rebecca Willett.

Matthew is interested in developing biologically inspired artificial neural networks to gain both a greater understanding of them and their biological analogues. Before joining the Lab, Matthew completed his PhD investigating decays of B mesons at CERN’s LHCb experiment.He is advised by Prof. David Freedman. 

PhD Students

Roxie is a Computer Science PhD student at the University of Chicago advised by Professor Rebecca Willett. Previously, she received her master’s degree in Operations Research at Columbia University and bachelor’s degree at Xi’an JiaoTong University.

Roxie’s research interests lie in machine learning for dynamical systems and its applications in scientific computing. In particular, she works on learning structural hidden representations of the high-dimensional data. Her work has been applied to addressing inverse problems with uncertainty quantification and designing practical algorithms to achieve data efficiency in decision-making problems (i.e., bandits). Currently, she is interested in predicting high-dimensional chaotic systems with deep learning.

Owen is a PhD student in computer science at the University of Chicago, advised by Rebecca Willett. For part of his PhD, he was supported by the NSF Research Traineeship in AI-enabled Molecular Engineering of Materials and Systems for Sustainablity. Before his PhD, he was a data scientist working with Haky Im.

Elena is a Compuer science PhD student at the University of Chicago, working with Professor Rebecca Willett. Her research focuses on machine learning and its scientific applications, with particular emphasis on generative models for quantum simulations, emulators for chaotic systems, and advanced techniques in weather forecasting. Elena has experience with tensor networks for neural network compression and generative adversarial networks (GANs).

Before her doctoral studies, she earned a Bachelor’s degree in Mathematics, and a Master’s degree in Mathematics and Computer Science.

Matt is a Computational Neuroscience student in Prof. Dave Freedman’s lab, where he works on the neurophysiology of visual cognition and working memory. Before this, he was an undergrad at Princeton, where he studied computer science.

Olivia is a PhD student in computer science at the University of Chicago, advised by Prof. Rebecca Willett. She is interested in integrating physical knowledge into machine learning methods, particularly for solving nonlinear inverse problems. Currently, she is working on an inverse scattering problem that involves computationally reconstructing images of objects from wave scattering data. As a part of this work, she is exploring the use of a numerical partial differential equation (PDE) solver to improve the performance of neural network-based approaches. She previously received a Bachelor’s degree and Master’s degree in Computer Science from the University of Chicago.

Ruize (Richard) Xu is a Computer Science PhD student at the University of Chicago, advised by Professor Risi Kondor on artificial intelligence for science and working with Professors David McAllester and Professor Zhiyuan Li on artificial intelligence for mathematics. His research interests lie at the intersection of machine learning for science and mathematics, focusing on Graph Neural Networks based approaches for chemical property prediction and leveraging large language models for automated theorem proving. Before his doctoral studies, Ruize earned Bachelor’s degrees in Computer Science, Mathematics, and Economics from the University of Chicago.

Jacob is working towards a Ph.D. in Computer Science with a focus on machine learning and A.I. in science. 
He is particularly interested in questions regarding representations of molecules for machine learning tasks and their relation to notions of model equivariance and conformational variability. He is also studying novel methods for combining different datasets, especially those that were experimentally generated with those that were generated through simulation. He is advised by Prof. Rebecca Willet.

He graduated from Harvey Mudd College in 2020 with a B.A. in Computer Science and Mathematics and an M.S. in Computer Science at University of Chicago in 2023.

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