1. Developed robust methods for inferring fitness from abundance data; 2. Collaborated with Petrov and Sherlock labs to analyze fitness gains in glucose-limited yeast; 3. Created benchmarks for protein design while at Google.
Research Experience
1. Research Scientist at Google DeepMind, focusing on understanding optimization and generalization in machine learning models through the lens of dynamical systems; 2. As a PhD student at Stanford, worked on evolutionary models on random fitness landscapes and the intersection of ecology and evolution.
Education
1. PhD in Physics from Stanford University, advised by Daniel S. Fisher; 2. B.A. in Mathematics and Physics from Swarthmore College.
Background
Currently a research scientist at Google DeepMind, previously a physics PhD student at Stanford University with interests in theoretical biology, evolutionary dynamics, machine learning, and optimization.
Miscellany
Maintains an active interest in the intersection of machine learning and theoretical biology.