Thomas Wim Hamelryck
Professor
Computational and RNA Biology
Ole Maaløes Vej 5
2200 København N.
Programming Languages and Theory of Computing
Universitetsparken 5
2100 København Ø
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
ID: 5765
Most downloads
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2333
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Protein structure validation and refinement using amide proton chemical shifts derived from quantum mechanics
Research output: Contribution to journal › Journal article › Research › peer-review
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1802
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Parallel GPGPU Evaluation of Small Angle X-ray Scattering Profiles in a Markov Chain Monte Carlo Framework
Research output: Chapter in Book/Report/Conference proceeding › Book chapter › Research › peer-review
Published -
1487
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A probabilistic model of RNA conformational space
Research output: Contribution to journal › Journal article › Research › peer-review
Published