Kaspar Märtens

Kaspar Märtens

Statistical machine learning, probabilistic inference, deep generative models, Gaussian Processes

Kaspar is a DPhil student at the Department of Statistics, University of Oxford, supervised by Christopher Yau and Chris Holmes.

He has a broad interest in statistical machine learning and Bayesian inference techniques, with a focus on tackling real problems in genomics and healthcare. His PhD research focuses on developing probabilistic latent variable models (such as Gaussian Process Latent Variable Models and Variational Autoencoders) with applications to biomedical data.

Kaspar is also an R enthusiast and organises the Oxford R user group meetings.



  • K. Märtens , K. Campbell , C. Yau , Decomposing feature-level variation with Covariate Gaussian Process Latent Variable Models, in Proceedings of the 36th International Conference on Machine Learning (ICML), 2019, vol. 97, 4372–4381.
  • K. Märtens , M. Titsias , C. Yau , Augmented Ensemble MCMC sampling in Factorial Hidden Markov Models, in Proceedings of Machine Learning Research (AISTATS), 2019, vol. 89, 2359–2367.


  • K. Märtens , J. Hallin , J. Warringer , G. Liti , L. Parts , Predicting quantitative traits from genome and phenome with near perfect accuracy, Nature Communications, vol. 7, 11512, 2016.
  • J. Hallin , K. Märtens , A. I. Young , M. Zackrisson , F. Salinas , L. Parts , J. Warringer , G. Liti , Powerful decomposition of complex traits in a diploid model, Nature Communications, vol. 7, 13311, 2016.
  • R. Kolde , K. Märtens , K. Lokk , S. Laur , J. Vilo , seqlm: an MDL based method for identifying differentially methylated regions in high density methylation array data, Bioinformatics, vol. 32, no. 17, 2604–2610, 2016.


  • K. Lokk , V. Modhukur , B. Rajashekar , K. Märtens , R. Mägi , R. Kolde , M. Koltšina , T. K. Nilsson , J. Vilo , A. Salumets , . others , DNA methylome profiling of human tissues identifies global and tissue-specific methylation patterns, Genome Biology, vol. 15, no. 4, 3248, 2014.