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 led 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, co-organized the GeneDisco challenge, and co-organize the Workshop on Computational Biology (WCB) at ICML.
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.
My research is funded by the Engineering and Physical Sciences Research Council and a GSK scholarship.
Tranception combines large autoregressive transformers with inference-time retrieval to achieve SOTA performance on protein fitness prediction.
We learn a distribution over protein sequences with Bayesian VAEs which we then leverage to predict the effects of mutations in human proteins.