Accelerating Molecular Discovery with Machine Learning: A Geometric, Sampling and Optimization Perspective

Applied and Computational Mathematics Seminar
Monday, April 1, 2024 - 2:00pm for 1 hour (actually 50 minutes)
Skiles 005 and
Yuanqi Du – Cornell University –
Molei Tao

Please Note: Speaker will present in person. Bio: Yuanqi Du is a PhD student at the Department of Computer Science, Cornell University studying AI and its intersection with Scientific Discovery advised by Prof. Carla P. Gomes. His research interests include Geometric Deep Learning, Probabilistic Machine Learning, Sampling, Optimization, and AI for Science (with a focus on molecular discovery). Aside from his research, he is passionate about education and community building. He leads the organization of a series of events such as the Learning on Graphs conference and AI for Science, Probabilistic Machine Learning workshops at ML conferences and an educational initiative (AI for Science101) to bridge the AI and Science community.

Recent advancements in machine learning have paved the way for groundbreaking opportunities in the realm of molecular discovery. At the forefront of this evolution are improved computational tools with proper inductive biases and efficient optimization. In this talk, I will delve into our efforts around these themes from a geometry, sampling and optimization perspective. I will first introduce how to encode symmetries in the design of neural networks and the balance of expressiveness and computational efficiency. Next, I will discuss how generative models enable a wide range of design and optimization tasks in molecular discovery. In the third part, I will talk about how the advancements in stochastic optimal control, sampling and optimal transport can be applied to find transition states in chemical reactions.