BioML Seminar 3.5 - Recent progress in generative methods for protein structure modeling and design

​​​​​[IN PERSON EVENT IN BERKELEY]

​​​​​​​Join us for a new seminar from the BioML group in Machine Learning at Berkeley, sponsored by Amplify Partners. This week we're hosting Stanford professor Brian Trippe.

​Talk Summary: Generative machine learning methods have driven much progress in protein structure modeling and design.  I will describe progress in recent years made with these approaches, some of their limitations, and recent work from my group intended to address these limitations.

​In particular, I will cover work from two papers:

• ​Predicting mutational effects on protein binding from folding energy: arxiv.org/abs/2507.05502

• ​Calibrating generative models to meet distributional constraints: arxiv.org/abs/2510.10020

​Speaker Bio:
Brian Trippe is an Assistant Professor in the Department of Statistics at Stanford, which he joined in 2024.  Previously he was a visiting researcher at the Institute for Protein Design at the University of Washington where he worked with David Baker, and a postdoctoral fellow at Columbia University. He completed his PhD in Computational and Systems Biology at MIT in 2022. His recent research has developed statistical machine learning methods to address challenges in biotechnology and medicine, with a focus on generative modeling and inference algorithms for protein engineering.