I joined the Prescient Design team at Genentech in June 2022 while completing the final stages of my Ph.D. at the NYU Center for Data Science as a 2018 NDSEG fellow. My doctoral work includes fundamental Bayesian machine learning methods development and practical applications to tasks like drug design. I also hold a M.S. in Operations Research from Cornell University (2019), and a B.S. summa cum laude in Applied Mathematics from University of Colorado, Denver (2017).
Fundamentally I am interested in online decision-making with incomplete information, particularly experimental design for scientific discovery. My work combines deep learning with Bayesian and frequentist uncertainty quantification methods to address challenges like the explore-exploit tradeoff, model misspecification, and model calibration.