Thomas Wim Hamelryck

Thomas Wim Hamelryck

Associate Professor

I am a specialist in Bayesian modelling and probabilistic machine learning. Currently, my research mostly focuses on probabilistic machine learning applied to protein structure prediction and protein evolution. I am particularly interested in the use of deep probabilistic programming, making use of the deep probabilistic programming languages Pyro and Numpyro, and the application of directional statistics to represent non-Euclidean data. My group also contributes to the development of Pyro and Numpyro.

Primary fields of research

Probabilistic machine learning, deep probabilistic progarmming, statistical structural bioinformatics

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