About Me

I am a Principal Machine Learning Scientist at Prescient Design, Genentech. My research focuses on high-dimensional inference and sampling, with a particular emphasis on developing probabilistic algorithms for active, machine-guided molecular design. 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.

Current research focus

• LLM alignment & robustness: Applying probabilistic ML and Bayesian inference techniques to ensure LLMs behave safely and reliably
• Generalized Bayes with frequentist flavors: Developing “prior-free,” “calibrated,” prediction-centric Bayesian frameworks for decision-making under uncertainty

Research themes

• Decision-making under uncertainty (AI4Science)
  • Multi‑objective Bayesian optimization for molecular design
  • Productionalizing ML-guided design of antibodies, small molecules, and molecular glues tailored to project-specific desiderata
• Inference and prediction in high dimensions
  • Semantic uncertainty quantification for LLMs
  • Approximate / variational inference
  • Post-hoc calibration (e.g. conformal prediction, semiparametric methods)
  • Out‑of‑distribution generalization, diagnostics for distribution shifts
• Sampling in high dimensions
  • Diversity-steered sampling strategies for LLMs
  • Guidance for diffusion-based generative models
  • Sampling algorithms for non‑log‑concave distributions