The Next Great Scientific Theory is Hiding Inside a Neural Network
- Speaker
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Miles CranmerAssistant Professor, University of Cambridge
Presidential Lectures are free public colloquia centered on four main themes: Biology, Physics, Mathematics and Computer Science, and Neuroscience and Autism Science. These curated, high-level scientific talks feature leading scientists and mathematicians and are intended to foster discourse and drive discovery among the broader NYC-area research community. We invite those interested in the topic to join us for this weekly lecture series.
Machine learning methods such as neural networks are quickly finding uses in everything from text generation to construction cranes. Excitingly, those same tools also promise a new paradigm for scientific discovery.
In this Presidential Lecture, Miles Cranmer will outline an innovative approach that leverages neural networks in the scientific process. Rather than directly modeling data, the approach interprets neural networks trained using the data. Through training, the neural networks can capture the physics underlying the system being studied. By extracting what the neural networks have learned, scientists can improve their theories. He will also discuss the Polymathic AI initiative, a collaboration between researchers at the Flatiron Institute and scientists around the world. Polymathic AI is designed to spur scientific discovery using similar technology to that powering ChatGPT. Using Polymathic AI, scientists will be able to model a broad range of physical systems across different scales.
About the Speaker
Cranmer is an assistant professor in data intensive science at the University of Cambridge with joint appointments in the Department of Applied Mathematics and Theoretical Physics and the Institute of Astronomy. He completed his Ph.D. at Princeton University. His research focuses on accelerating scientific discovery by developing and applying novel methods at the intersection of machine learning and physics. Cranmer has created a suite of standard software libraries for ‘symbolic regression’ that have been utilized in numerous scientific discoveries. His work covers various areas of deep learning, including physics-motivated architectures such as Lagrangian neural networks.