Pascal Notin

Pascal Notin

PhD Candidate in Computer Science

University of Oxford

I am a Ph.D. candidate in the Oxford Applied and Theoretical Machine Learning Group, part of the Computer Science Department at the University of Oxford, under the supervision of Yarin Gal.

My research is in machine learning and motivated by questions in computational biology and chemistry. From a machine learning standpoint, I have been focusing primarily on generative modeling, Bayesian deep learning, large-scale training and active learning. This has lead to the development of new methods for protein modeling, mutation effects prediction and de novo drug design.

I have co-created and am the lead organizer for the Machine Learning for Drug Discovery (MLDD) workshop at ICLR 2022. I am a co-organizer of the Workshop on Computational Biology (WCB) at ICML 2022.

I have several years of applied machine learning experience developing AI solutions, primarily within the healthcare and pharmaceutical industries. Prior to coming to Oxford, I was a Senior Manager at McKinsey & Company in the New York and Paris offices, where I was leading cross-disciplinary teams on fast-paced analytics engagements.

I obtained a M.S. in Operations Research from Columbia University, and a B.S. and M.S. in Applied Mathematics from Ecole Polytechnique.

I am funded by the Engineering and Physical Sciences Research Council and a recipient of a GSK scholarship.

Recent publications

Disease variant prediction with deep generative models of evolutionary data
GeneDisco: A Benchmark for Experimental Design in Drug Discovery
Viral Evolution and Antibody Escape Mutations using Deep Generative Models
Improving compute efficacy frontiers with SliceOut
Principled Uncertainty Estimation for High Dimensional Data