John "Jack" Kamm


Email: jk21 at sanger dot ac dot uk

About me

I am a postdoc at the Wellcome Trust Sanger Institute, supervised by Richard Durbin. Previously, I completed my Ph.D. in Statistics at UC Berkeley, supervised by Yun S. Song.

My research centers on the coalescent, a fundamental stochastic process for population genetics, and involves both a probabilistic side, discovering new insights about the coalescent, and a statistical side, using the coalescent to infer demographic history, recombination rates, and genealogies.

More generally, I am interested in statistics, mathematics, and applications to genetics. This includes topics such as graphical models, tensor analysis, time-series, and genome-wide association studies (GWAS). On a more personal level, I enjoy the Python programming language, the emacs text editor, board and video games, and vegetarian food.



  • Robust and scalable inference of population history from hundreds of unphased whole genomes
    Terhorst, J., Kamm, J.A., and Song, Y.S.
    Nature Genetics, Vol. 49 (2017) 303-309.
    {Journal} {Software}
  • Efficient computation of the joint sample frequency spectra for multiple populations.
    Kamm, J.A., Terhorst, J., and Song, Y.S.
    Journal of Computational and Graphical Statistics, Vol. 26, No. 1 (2017) 182-194.
    {Journal} {Preprint} {Software}
  • Two-Locus Likelihoods under Variable Population Size and Fine-Scale Recombination Rate Estimation.
    Kamm, J.A.*, Spence, J.P.*, Chan, J., and Song, Y.S. (*contributed equally)
    Genetics, Vol. 203 No. 3 (2016) 1381-1399 2016.
    {Journal} {Preprint} {Software}
  • The site frequency spectrum for general coalescents.
    Spence, J.*, Kamm, J.A.*, and Song, Y.S. (*contributed equally)
    Genetics, Vol. 202 No. 4 (2016) 1549-1561.
    {Journal} {Preprint}
  • Decoding coalescent hidden Markov models in linear time.
    Harris, K., Sheehan, S., Kamm, J.A., Song, Y.S.
    Proc. 18th Annual Intl. Conf. on Research in Computational Molecular Biology (RECOMB), LNBI Vol. 8394, (2014), pp 100-114.
    {Journal} {Preprint}
  • Approximate sampling formulae for general finite-alleles models of mutation.
    Bhaskar, A., Kamm, J.A., and Song, Y.S.
    Advances in Applied Probability, 44 (2012) 408-428.
    {Journal} {Preprint}


  • Inference of complex population histories using whole-genome sequences from multiple populations.
    Steinrücken, M., Kamm, J.A., and Song, Y.S.
    {Preprint} {Software}

Ph.D. Thesis

  • One and Two Locus Likelihoods Under Complex Demography.
    Statistics, UC Berkeley, December 2015.


  • LDpop: 2-locus likelihoods for recombination map estimation under variable population size.
    Probabilistic Modeling in Genomics @ Oxford, 2016.
  • momi: a new method for computing the multipopulation sample frequency spectrum.
    Probabilistic Modeling in Genomics @ Cold Spring Harbor, 2015.