Pascal Notin

PhD Candidate, Department of Computer Science, University of Oxford

pascal.notin [AT]


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 interests lie at the intersection of Bayesian Deep Learning, Generative models and Computational biology. The current focus of my work is to develop methods to quantify and leverage uncertainty in models for structured representations (e.g., sequences, graphs) with applications in biology and medicine.

I have several years of applied machine learning experience developing AI solutions, primarily within the healthcare and pharmaceutical industries (e.g., Real World Evidence, disease prediction, clinical trials).
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 the IEOR department at 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 CASE scholarship.



Improving black-box optimization in VAE latent space using decoder uncertainty

Pascal Notin*, José Miguel Hernández-Lobato, Yarin Gal


Large-scale clinical interpretation of genetic variants using evolutionary data and deep learning

Jonathan Frazer*, Pascal Notin*, Mafalda Dias*, Aidan N. Gomez, Kelly Brock, Yarin Gal, Debora S. Marks


SliceOut: Training Transformers and CNNs faster while using less memory

Pascal Notin*, Aidan N. Gomez, Joanna Yoo, Yarin Gal


Principled Uncertainty Estimation for High Dimensional Data

Pascal Notin*, José Miguel Hernández-Lobato, Yarin Gal



A copy of my resume is available here.