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 (2017) Spherical convolutions and their application in molecular modelling. Advances in Neural Information Processing Systems 30 (NIPS 2017). Spotlight Presentation. Online version
  2. Brouwer T, Frellsen J, Liò P (2017) Comparative Study of Inference Methods for Bayesian Matrix Factorisation. 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.