Speaker:  Brian Hie (Stanford University)

Title:  Learning to read and write protein evolution

Abstract:  Evolution is the powerful force driving both the real-time emergence of pathogen resistance to drugs and immunity, as well as the diversity of natural forms and functions that have emerged over longer timescales. Modern evolutionary models, especially those that leverage advances in machine learning, can improve our ability to design new proteins in the laboratory. This talk will cover how models of protein sequences and structures can learn evolutionary rules that help guide artificial evolution, including the affinity maturation of antibodies against diverse viral antigens and the in-silico evolution of modular and programmable de novo proteins with structures not found in nature.

Bio: Brian Hie is an incoming Assistant Professor of Chemical Engineering and Data Science at Stanford University and an Innovation Investigator at Arc Institute, where he conducts research at the intersection of biology and machine learning. He was previously a Stanford Science Fellow in the Stanford University School of Medicine and a Visiting Researcher at Meta AI. He completed his Ph.D. at MIT CSAIL and was an undergraduate at Stanford University.

On Zoom at https://mit.zoom.us/j/93513735220