Research
Computation, evolution, and immunity
I am currently a James S. McDonnell Foundation Postdoctoral Fellow hosted in the Department of Electrical Engineering & Computer Sciences at the University of California, Berkeley, advised by Yun Song. My PhD training was in the Department of Genome Sciences at the University of Washington, Seattle, where I was jointly advised by Erick Matsen and Kelley Harris, and held an F31 Predoctoral Fellowship from the NIH. I previously earned an MS in Physics at the University of Vermont, and have held several industry research positions; before my PhD, I was a Senior Computational Biologist at a biotechnology start-up, and, during my MS, I was a Computational Physicist at an electronics technology company.
I am committed to advancing science with quantitative models and computational tools. Broadly, I’m interested in applied probabilistic and dynamical modeling, statistical inference, optimization, and machine learning, all grounded by questions about evolving biological systems. In addition to innovating theoretical and computational methods, I work closely with domain experts and experimentalists to design and analyze data and discover new biology. My nontraditional path has positioned me at the interface between quantitative sciences and biology, and I enjoy interdisciplinary collaborations and inclusive scientific discourse.
Quantitative immunology
A key direction is to synthesize dynamical evolutionary modeling with deep representation learning to predict and control the adaptive immune system. I aim to clarify how immune memories are encoded and how systemic responses emerge, both in terms of rapid evolution in immune receptor repertoires (our antibodies and T cell receptors), and co-evolution with rapidly evolving pathogens (i.e., viruses). Adaptive immunity is an ideal setting to think about the function of evolving systems. There is considerable scope for sequence data-driven studies of evolutionary mechanisms in this setting.
Evolutionary dynamics
I am also interested in models and inference for evolutionary dynamics, including mean-field approaches to interacting tree processes, ill-posed inverse problems, sparse optimization, and graph-based machine-learning architectures, which have the potential to accommodate complex biological processes. Many problems in evolutionary inference—on time scales ranging from phylogenetic to population genetic to somatic—are amenable to these computational techniques when framed appropriately. I aim to infer evolutionary histories and dynamical parameters in complex evolutionary settings—with interactions and multi-scale adaptation—that are out of reach for standard models.
