About Me
I am a Principal Scientist and Group Lead at Prescient Design, Genentech, developing statistical frameworks for reliable decision making using large models, with applications to model‑guided scientific discovery. My research focuses on uncertainty quantification, Bayesian experimental design, and scalable inference, emphasizing methods that provide formal guarantees, are sample‑efficient, and improve with advances in foundation and generative models. I received my Ph.D. in Physics from Stanford University, where I worked on hierarchical Bayesian methods for cosmology. During my Ph.D., I interned at NASA Ames and the Center for Computational Astrophysics at the Flatiron Institute. I hold a B.S. in Mathematics and a B.S. in Physics from Duke University.
Recent events
- April 07, 2026: Stanford Society of Women Engineers, Lunch Speaker Series (Stanford, CA)
- February 20, 2026: Genesis Therapeutics, Seminar Series (Virtual)
- January 20, 2026: Biologic Summit 2026 (San Diego, CA)
- November 13, 2025: PEGS Europe 2025: Machine Learning for Protein Engineering (Lisbon, Portugal)
- November 12, 2025: PEGS Europe 2025: Machine Learning for Protein Engineering (Lisbon, Portugal)