Advisor: Rick Stevens
I received my B.S. in Physics at Harvey Mudd College in 2016. Before starting my PhD, I worked as a software engineer at Lawrence Livermore National Lab on their high performance computing laser simulation code. My current research is at the intersection of AI and biology, with an emphasis on causality and interpretability.
Establishing causality in a cell’s response to stress is difficult: there are thousands of genes, proteins, and pathways interacting simultaneously — orchestrated to preserve the life of the cell, or to intentionally destroy it to minimize damage. The scale of the variables at play makes it challenging to tease out the key drivers of stress response; this is a problem that is compounded by limited samples. My research aims to build tools to solve this intractable problem using the framework of causal discovery by uncovering cause-and-effect relationships in data at scale. My recent work on causal graph partitioning is published in TMLR, and is a theoretically sound framework for scaling up causal discovery algorithms to the human genome scale.
