Jes Frellsen
Jes Frellsen
Associate Professor of Machine Learning
Department of Computer Science
IT University of Copenhagen


I am an Associate Professor in Machine Learning at the IT University of Copenhagen (ITU). Previously I was a postdoctoral researcher with Professor Zoubin Ghahramani in the Machine Learning group at the University of Cambridge. Before that I was a graduate student and postdoc at the Bioinformatics Centre, University of Copenhagen, where I have worked with Professor Anders Krogh and Dr Thomas Hamelryck.

Research interests

I work on statistical machine learning with particular interest in:

  • Bayesian modelling and inference
  • Deep Learning
  • Directional Statistics
  • Markov chain Monte Carlo methods
  • Application in Bioinformatics

Research group

Postgraduate members:

I am also supervising 6 Master's Thesis students.


Phone: +45 7218 5030

Office: 4B05

Postal address:
IT University of Copenhagen
Rued Langgaards Vej 7
2300 København S

Short biography

Academic positions

Aug 2016 - Associate Professor in Machine Learning
Department of Computer Science, IT University of Copenhagen
May 2013 -
Jun 2016
Postdoctoral researcher with Zoubin Ghahramani
Department of Engineering, University of Cambridge
Dec 2011 -
Apr 2013
Postdoctoral researcher with Anders Krogh
Bioinformatics Centre, University of Copenhagen
Mar 2011 -
Aug 2011
Postdoctoral researcher with Thomas Hamelryck
Bioinformatics Centre, University of Copenhagen


2011 Ph.d. in Bioinformatics
Bioinformatics Centre, University of Copenhagen.
"Probabilistic methods in macromolecular structure prediction"
Supervisor: Thomas Hamelryck, co: Jesper Ferkinghoff-Borg
2007 M.Sc. in Bioinformatics, University of Copenhagen
(I was awarded the highest possible grade for my master thesis)
2005 B.Sc. in maths and CS, University of Copenhagen
2004-2005 EAP exchange student at the University of California, Santa Cruz
(worked in Kevin Karplus's Lab group wither and spring)


  1. Boomsma W, Frellsen J (2018) Spherical convolutions and their application in molecular modelling. Appearing at the conference on Advances in Neural Information Processing Systems 31 (NIPS 2017). Selected for Spotlight Presentation.
  2. Brouwer T, Frellsen J, Liò P (2017) Comparative Study of Inference Methods for Bayesian Matrix Factorisation. Appearing at the for European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) 2017. arXiv: 1707.05147
  3. Navarro AKW, Frellsen J, Turner RE (2017) The Multivariate Generalised von Mises: Inference and applications. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17), 2394-2400. arXiv: 1602.05003
  4. Brouwer T, Frellsen J, Liò P (2016) Fast Bayesian non-negative matrix factorisation and tri-factorisation. Advances in Approximate Bayesian Inference Workshop at NIPS 2016, Barcelona, Spain. arXiv: 1610.08127
  5. Frellsen J, Winther O, Ghahramani Z, Ferkinghoff-Borg J (2016) Bayesian generalised ensemble Markov chain Monte Carlo. Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS), Cadiz, Spain. JMLR: W&CP volume 41. [pdf] [supplementary]
  6. Hamelryck T, Boomsma W, Ferkinghoff-Borg J, Foldager J, Frellsen J, Haslett J, Theobald D (2015) Proteins, physics and probability kinematics: a Bayesian formulation of the protein folding problem. In: Dryden IL, Kent JT (eds.), Geometry Driven Statistics. Wiley. doi:10.1002/9781118866641.ch18
  7. Boomsma W, Tian P, Frellsen J, Jesper Ferkinghoff-Borg, Thomas Hamelryck, Kresten Lindorff-Larsen, and Michele Vendruscolo (2014) Equilibrium simulations of proteins using molecular fragment replacement and NMR chemical shifts. PNAS, 111(38):13852-13857. doi:10.1073/pnas.1404948111
  8. Kerpedjiev P*, Frellsen J*, Lindgreen S, Krogh A (2014) Adaptable probabilistic mapping of short reads using position specific scoring matrices. BMC Bioinformatics 15:100. doi:10.1186/1471-2105-15-100
    *Joint first author.
  9. Frellsen J*, Menzel P*, Krogh A (2014) Algorithms for mapping high-throughput DNA sequences. In: Brahme A (ed.), Comprehensive Biomedical Physics, Volume 6: Bioinformatics. Elsevier. pp 41-50. doi:10.1016/B978-0-444-53632-7.01103-5.
    *Joint first author.
  10. Frellsen J, Hamelryck T, Ferkinghoff-Borg J (2013) Combining the multicanonical ensemble with generative probabilistic models of local biomolecular structure. Proceedings 59th ISI World Statistics Congress, pp 139-144, 25-30 August 2013, Hong Kong. International Statistical Institute, The Hague, The Netherlands, December 2013. Online version
  11. Menzel P*, Frellsen J*, Plass M, Rasmussen SH, Krogh A (2013) On the Accuracy of Short Read Mapping. In: Shomron N (ed.), Deep Sequencing Data Analysis, Methods in Molecular Biology. Humana Press. pp. 39-59. doi:10.1007/978-1-62703-514-9_3.
    *Joint first author.
  12. Olsson S, Frellsen J, Boomsma W, Mardia KV, Hamelryck T (2013) Inference of structure ensembles of flexible biomolecules from sparse, averaged data. PLoS ONE 8(11): e79439. doi:10.1371/journal.pone.0079439
  13. Valentin JB, Andreetta C, Boomsma W, Bottaro S, Ferkinghoff-Borg J, Frellsen J, Mardia KV, Tian P, Hamelryck T (2013) Formulation of probabilistic models of protein structure in atomic detail using the reference ratio method. Proteins 82: 288-299. doi:10.1002/prot.24386
  14. Boomsma W, Frellsen J, Harder T, Bottaro S, Johansson KE, et al. (2013) PHAISTOS: A framework for Markov chain Monte Carlo simulation and inference of protein structure. Journal of Computational Chemistry 34(19):1697-1705. doi:10.1002/jcc.23292
  15. Hamelryck T, Haslett J, Mardia K, Kent JT, Valentin J, Frellsen J, Ferkinghoff-Borg J (2013) On the reference ratio method and its application to statistical protein structure prediction. Proceedings of the 32th Leeds Annual Statistical Research Workshop, pp 53-57. Leeds University Press. Online version
  16. Frellsen J, Mardia KV, Borg M, Ferkinghoff-Borg J, Thomas Hamelryck (2012) Towards a General Probabilistic Model of Protein Structure: The Reference Ratio Method. Hamelryck T et al. (eds.), Bayesian Methods in Structural Bioinformatics, Statistics for Biology and Health. Springer-Verlag. doi:10.1007/978-3-642-27225-7_4
  17. Mardia KV and Frellsen J (2012) Statistics of Bivariate von Mises Distributions. Hamelryck T et al. (eds.), Bayesian Methods in Structural Bioinformatics, Statistics for Biology and Health. Springer-Verlag. doi:10.1007/978-3-642-27225-7_6 [errata]
  18. Boomsma W, Frellsen J, and Hamelryck T (2012) Probabilistic Models of Local Biomolecular Structure and Their Applications. Hamelryck T et al. (eds.), Bayesian Methods in Structural Bioinformatics, Statistics for Biology and Health. Springer-Verlag. doi:10.1007/978-3-642-27225-7_10
  19. Olsson S, Boomsma W, Frellsen J, Bottaro S, Harder T, Ferkinghoff-Borg J, Hamelryck T (2011) Generative probabilistic models extend the scope of inferential structure determination. Journal of Magnetic Resonance 213(1):182-186. doi:10.1016/j.jmr.2011.08.039
  20. Mardia KV, Frellsen J, Borg M, Ferkinghoff-Borg J, Hamelryck T (2011) A statistical view on the reference ratio method. Proceedings of the 30th Leeds Annual Statistical Research Workshop, pp 55-61. Leeds University Press. Online version
  21. Hamelryck T, Borg M, Paluszewski M, Paulsen, Frellsen J, Andreetta C, Boomsma W, Bottaro S, Ferkinghoff-Borg J (2010) Potentials of Mean Force for Protein Structure Prediction Vindicated, Formalized and Generalized. PLoS ONE 5:e13714. doi:10.1371/journal.pone.0013714
  22. Harder T, Boomsma W, Paluszewski M, Frellsen J, Johansson KE, Hamelryck T (2010) Beyond rotamers: a generative, probabilistic model of side chains in proteins. BMC Bioinformatics 11:306. doi:10.1186/1471-2105-11-306
  23. Borg M, Mardia KV, Boomsma W, Frellsen J, Harder T, Stovgaard K, Ferkinghoff-Borg J, Røgen P, Hamelryck T (2009) A probabilistic approach to protein structure prediction: PHAISTOS in CASP9. The 28th Leeds Annual Statistical Research Workshop, pp 65-70. Leeds University Press. Online version
  24. Frellsen J*, Moltke I*, Thiim M, Mardia KV, Ferkinghoff-Borg J, Hamelryck T (2009) A Probabilistic Model of RNA Conformational Space. PLoS Computational Biology 5(6): e1000406. doi:10.1371/journal.pcbi.1000406. *Joint first author.
  25. Boomsma W, Borg M, Frellsen J, Harder T, Stovgaard K, Ferkinghoff-Borg J, Krogh A, Mardia KV and Hamelryck, T (2008) PHAISTOS: protein structure prediction using a probabilistic model of local structure. Proceedings of CASP8, pp 82-83. Cagliari, Sardinia, Italy, December 3-7 2008.
  26. Marstrand TT, Frellsen J, Moltke I, Thiim M, Valen E, Retelska D, Krogh A (2008) Asap: A Framework for Over-Representation Statistics for Transcription Factor Binding Sites. PLoS ONE 3(2): e1623. doi:10.1371/journal.pone.0001623

Selected talks


Muninn A software package for estimating generalized ensemble weights in Markov chain Monte Carlo simulations.
Project manager and core developer.
Phaistos A Markov chain Monte Carlo framework for protein structure simulations.
Core developer.
BARNACLE A small Python library for probabilistic sampling of RNA 3D structure.
Project manager and core developer.
Mocapy++ A Dynamic Bayesian Network toolkit.
BWA-PSSM A probabilistic short read mapper.