Chris is a computer scientist turned bioinformatician with a passion for systematically modelling biological mechanisms through machine learning. Towards reaching this goal, he contributed and pushed the community of learned protein sequence representations in order to find new, principled ways to describe biological entities. Bio-sequence representation learning, for instance through transformer models, is today an established research field with impactful applications like the lightning fast prediction of protein 3D structure. Chris remains focused on trying to address problems for which data and intuition remain scarce, for instance those allowing the design of new proteins with therapeutic or industrial use.
ACM Gordon Bell COVID Finalist
ACM Gordon Bell Finalist
Awards Presentation
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