Human immunity is much more resilient than our antibody drugs. Learning how the immune system balances short-term protection and long-term adaptability is critical to designing robust therapeutics.
Evolution is a powerful force, as well as a powerful adversary. By learning evolutionary rules from data, machine learning provides a way to harness this force for our benefit.
Eric Nguyen, Michael Poli, Matthew G. Durrant, Brian Kang, Dhruva Katrekar, David B. Li, Liam J. Bartie, Armin W. Thomas, Samuel H. King, Garyk Brixi, Jeremy Sullivan, Madelena Y. Ng, Ashley Lewis, Aaron Lou, Stefano Ermon, Stephen A. Baccus, Tina Hernandez-Boussard, Christopher Ré, Patrick D. Hsu and Brian L. Hie.
Varun R. Shanker, Theodora U. J. Bruun, Brian L. Hie, Peter S. Kim
Brian L. Hie, Varun R. Shanker, Duo Xu, Theodora U. J. Bruun, Payton A. Weidenbacher, Shaogeng Tang, Wesley Wu, John E. Pak, Peter S. Kim
Brian Hie is an Assistant Professor of Chemical Engineering at Stanford University, the Dieter Schwarz Foundation Stanford Data Science Faculty Fellow, and an Innovation Investigator at Arc Institute. He leads the Laboratory of Evolutionary Design, which conducts research at the intersection of machine learning and biology. Previously, Brian was a Stanford Science Fellow in Biochemistry, a researcher at Meta AI, received his Ph.D. in Electrical Engineering and Computer Science from MIT, and his B.S. in Computer Science from Stanford.
Stanford Biophysics, Co-advised by Anne Brunet
Stanford Bioengineering
Stanford Genetics
UCSF Biomedical and Medical Informatics, Co-advised by Luke Gilbert
Stanford Bioengineering
Stanford Bioengineering
Stanford Bioengineering, Co-advised by Michael Fischbach and Karl Deisseroth
Stanford Bioengineering
Stanford Bioengineering, Co-advised by Stephen Baccus and Christopher Ré
Stanford Chemical Engineering
Stanford Biophysics
