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Data-Driven Drug Discovery and Molecular Informatics

Miguel is a PhD student working on probabilistic machine learning for drug design. He is jointly supervised by Andreas Bender, Sergio Bacallado (Department of Pure Mathematics and Mathematical Statistics) and Carl Rasmussen (Department of Engineering). He is interested in Gaussian processes, variational inference, machine learning for graphs, Bayesian optimization and Monte Carlo methods. He is also interested in automatic control of type-1 diabetes. Previously he studied a Bachelor's in Biotechnology, a MPhil in Scientific Computing, a MSc in Mathematical Engineering and a MRes in Mathematical Genomics and Medicine. In his spare time he enjoys running, bouldering and paragliding.