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.
John Simon Guggenheim Memorial Foundation Fellowship, 2012-2013.
Alfred P. Sloan Foundation Research Fellowship, 2009-2011.
David & Lucile Packard Foundation Fellowship for Science and Engineering, 2007.
Microsoft Research Faculty Fellowship Program, 2007.
National Science Foundation (NSF) CAREER Award, 2007.
Bridge to education
December 15, 2019
CSHL’s DNA Learning Center builds new bridges between unique science education and diverse groups.
Making sense of the genome…at last
December 6, 2019
Quantitative biologists like Cold Spring Harbor Laboratory’s Adam Siepel are finally making sense of the flood of data contained in the human genome.
The Lab partners with award-winning magazine
December 6, 2019
Nautilus, an award-winning science magazine, has partnered with CSHL to bring the story of the lab’s scientists and research to a brand-new audience.
Research profile: Adam Siepel
November 12, 2019
Adam Siepel, Chair of the Simons Center for Quantitative Biology, uses advanced computational methods to solve complex biological questions.
How does natural selection affect the genome?
December 18, 2018
Adam Siepel explains how natural selection can tell researchers how informative sifting through the complex human genome will be.
How much are we learning? Natural selection is science’s best critic
December 17, 2018
Researchers determine that natural selection and our evolutionary history may be the best guides for future research.
A science writer’s quest to understand heredity
May 30, 2018
LabDish spoke with science writer Carl Zimmer about what he learned about heredity as he zig-zagged through CSHL while writing his new book.
Evolving sets of gene regulators explain some of our differences from other primates
January 29, 2018
What makes us different from our primate relatives? Gene regulation is one important evolutionary factor
A lesson in class
December 15, 2017
In this episode of Base Pairs, we discuss how genetic information is changing how we define important categories.
Reconstructing ancient human history from DNA
June 20, 2017
Free public lecture featuring Adam Siepel, Ph.D., CSHL Professor and Chair of the Simons Center for Quantitative Biology.