Genentech: Samuel Stanton | Machine Learning Scientist, Prescient Design, Computational Sciences

Scientists / Our Scientists

Years at Genentech

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).

Featured Publication

Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders

(2022) arXiv:2203.12274

Stanton, S., Maddox, W., Gruver, N., Maffettone, P., Delaney, E., Greenside, P., Wilson, A.G.

View Abstract

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.

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Samuel Stanton

Machine Learning Scientist, Prescient Design, Computational Sciences

Featured Publication

Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders