Ph.D., University of California, Santa Cruz, 2005
I am a computer scientist who is fascinated by the challenge of making sense of vast quantities of genetic data. My research group focuses in particular on questions involving human evolution and transcriptional regulation.
Modern genomic technologies make it relatively easy to generate rich data sets describing genome sequences, RNA expression, chromatin states, and many other aspects of the storage, transmission, and expression of genetic information. For many problems in genetics today, the limiting step is no longer in data generation, but in integrating, interpreting, and understanding the available data. Addressing these challenges requires expertise both in the practical arts of data analysis and in the theoretical underpinnings of statistics, computer science, genetics, and evolutionary biology.
My group focuses on a diverse collection of research questions in this interdisciplinary area. Over the years, our research has touched on topics including the identification of recombinant strains of HIV, the discovery of new human genes, the characterization of conserved regulatory elements in mammalian genomes, and the estimation of the times in early human history when major population groups first diverged. A general theme in our work is the development of precise mathematical models for the complex processes by which genomes evolve over time, and the use of these models, together with techniques from computer science and statistics, both to peer into the past, and to address questions of practical importance for human health. Recently, we have increasingly concentrated on research at the interface of population genomics and phylogenetics, with a particular focus on humans and the great apes. We also have an active research program in computational modeling and analysis of transcriptional regulation in mammals and Drosophila, in close collaboration with Prof. John Lis at Cornell University.
Kuhlwilm, M. and Gronau, I. and Hubisz, M. J. and de Filippo, C. and Prado-Martinez, J. and Kircher, M. and Fu, Q. and Burbano, H. A. and Lalueza-Fox, C. and de la Rasilla, M. and Rosas, A. and Rudan, P. and Brajkovic, D. and Kucan, Z. and Gusic, I. and Marques-Bonet, T. and Andres, A. M. and Viola, B. and Paabo, S. and Meyer, M. and Siepel, A. and Castellano, S. (2016) Ancient gene flow from early modern humans into Eastern Neanderthals. Nature 530(7591) pp. 429-433.
Gulko, B. and Hubisz, M. J. and Gronau, I. and Siepel, A. (2015) A method for calculating probabilities of fitness consequences for point mutations across the human genome. Nature Genetics 47(3) pp. 276-283.
Rasmussen, M. D. and Hubisz, M. J. and Gronau, I. and Siepel, A. (2014) Genome-wide inference of ancestral recombination graphs. PLoS Genetics 10(5) pp. e1004342.
Gronau, I. and Hubisz, M. J. and Gulko, B. and Danko, C. G. and Siepel, A. (2011) Bayesian inference of ancient human demography from individual genome sequences. Nat Genet 43(10) pp. 1031-4.
Pollard, K. S. and Hubisz, M. J. and Rosenbloom, K. R. and Siepel, A. (2010) Detection of nonneutral substitution rates on mammalian phylogenies. Genome Res 20(1) pp. 110-21.Additional materials of the author at
CSHL Institutional Repository
Re-learning how to read a genome
CSHL receives $50 million to establish Simons Center for Quantitative Biology
Dr. Adam Siepel, Cornell University - Similarity in primate DNA
John Simon Guggenheim Memorial Foundation Fellowship, 2012-2013.
Microsoft Research Faculty Fellowship Program, 2007.