The Simons Center for Quantitative Biology (SCQB) is Cold Spring Harbor Laboratory’s home for mathematical, computational, and theoretical research in biology. Research at the SCQB focuses broadly on revealing how genomes work, how they evolve, and what makes them go wrong in disease. Members of the SCQB also develop computational tools and genomic technologies that are broadly useful to the community.
The SCQB is supported by a generous endowment from the Simons Foundation. Additional funding has been provided by the Starr Foundation and Lavinia and Landon Clay.
We are a growing group with positions at various levels.
Simons Center for Quantitative Biology Annual Reports
Our faculty are experts in the mathematical and physical sciences who address open problems in biology, often in close collaboration with experimentalists. Most research in the center falls in the general areas of gene regulation, evolutionary genomics, disease-related human genomics, and genomic technology development. However, our work also touches on neuroscience, immunology, and plant biology, among other fields.
Members of the SCQB maintain close collaborative ties across CSHL and with many other New York area groups, including Stony Brook University and the New York Genome Center.
Adam Siepel, Ph.D.
Sr. Scientific Administrator & Assistant to the Chair
Sr. Scientific Administrator
QB Science Manager
Quantitative Biology External Advisory Committee
This Simons Center for Quantitative Biology External Advisory Committee meets annually to provide strategic advice and general guidance.
Andrew G. Clark, Ph.D.
Professor of Molecular Biology and Genetics
David L. Donoho, Ph.D.
Anne T. and Robert M. Bass Professor of Humanities and Sciences
Professor of Statistics
Molly Przeworski, Ph.D.
Professor of Biological Sciences and Systems Biology
Eric D. Siggia, Ph.D.
Viola Ward Brinning and Elbert Calhoun Brinning Professor
Head of Laboratory of Theoretical Condensed Matter Physics
The Rockefeller University
Eero P. Simoncelli, Ph.D.
Silver Professor of Neural Science, Mathematics, Data Science, and Psychology | New York University
Scientific Director | Center for Computational Neuroscience, FlatIron Institute, Simons Foundation
Steven L. Salzberg, Ph.D. (Chair)
Bloomberg Distinguished Professor of Biomedical Engineering, Computer Science, and Biostatistics
Director, Center for Computational Biology
Johns Hopkins University
AI researchers ask: What’s going on inside the black box?
February 8, 2021
Although researchers have figured out how to train computers to recognize things, they have yet to understand how machines make those predictions.
How does anyone stay healthy in a world full of germs?
January 15, 2021
Computational biology is uncovering the immune system’s tricks for identifying foreign invaders.
Mice with too many chandelier cells lack depth perception
December 8, 2020
Chandelier cells should decrease in number as animals develop. Mice with too many cells lack depth perception.
Problems with depth perception caused by too many cells
December 7, 2020
Chandelier cells should decrease in number as animals develop. If too many remain, brain systems may not work properly.
How to figure out what you don’t know
November 30, 2020
Cold Spring Harbor Laboratory Assistant Professor Tatiana Engel discusses how a model like Ptolemy’s seems to explain the world and yet is wrong.
Birds of a feather do flock together
November 17, 2020
Researchers found a genetic mechanism for how brand new species acquire distinct traits.
How to figure out what you don’t know
October 26, 2020
Sometimes, what seems like a good way to understand the world turns out to be wrong. A new machine learning tool lets scientists find better answers.
How to tune out common odors and focus on important ones
May 11, 2020
The fly brain uses a simple computing trick to ignore prevalent odors and focus on newer but rarer odorants.
Predicting the evolution of genetic mutations
April 14, 2020
CSHL quantitative biologists have designed a computational approach for predicting the evolution of a rapidly mutating virus or cancer.
A science career path: David McCandlish
April 10, 2020
Assistant Professor David McCandlish is a quantitative biologist who walks the line between advanced mathematics and the life sciences at CSHL.
COVID-19 machine learning effort: Preprints are key
March 25, 2020
Preprint papers play a key role in a U.S. government-led machine learning effort to understand the COVID-19 pandemic.
Tatiana Engel named 2020 Sloan Fellow
February 12, 2020
Assistant Professor Tatiana Engel is named a 2020 Sloan Fellow for her work on computational models of decision-making.
What Google could learn from a fruit fly
January 21, 2020
By tapping into life’s algorithms, scientists are finding elegant solutions to some of the hardest problems in computer science.
The non-human living inside of you
January 9, 2020
A large part of human DNA doesn’t aid the normal workings of the body. This “junk DNA” contains ancient viruses that may spur diseases like ALS.
Finally, machine learning interprets gene regulation clearly
December 26, 2019
Machine learning and a new kind of easily-interpretable artificial neural network is helping scientists make sense of crucial gene regulation.
Saket Navlakha taps the power of biology and computers
December 17, 2019
Associate Professor Saket Navlakha is bringing ideas about “algorithms in nature” to the Simons Center for Quantitative Biology.
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.
Peter Koo wants to understand how machines learn biology
September 20, 2019
Dr. Peter Koo joins the CSHL faculty as an assistant professor. His focus is on exploring how artificial intelligence integrates with biology and genomics.
Of mice and model organisms
July 31, 2019
An in-depth look at how veterinarians at CSHL help take care of the various organisms that help researchers answer fundamental biological questions.
There’s more to smell than meets the nose
July 22, 2019
Neuroscience researchers work to figure out our brains process smells, including what features are essential to identifying and separating odors.
Quantifying how the brain smells
July 22, 2019
Neuroscience researchers at CSHL are trying to figure out how the brain processes smells and what features of odors are important in that process.
Seeing with sequencing—A public lecture with three CSHL experts
April 19, 2019
Quantitative biologists discuss how physics, modern computing power, and a new perspective on biology can make sense of our complex genomes.
NIH grant awarded for interneuron research
April 4, 2019
CSHL postdoc Maggie Crow will use her NIH grant to pursue the quantification and analysis of specific types of neurons in the brain.
Hannah Meyer joins CSHL Quantitative Biology faculty
March 26, 2019
Hannah Meyer is the newest Quantitative Biology Fellow at CSHL, studying how our immune system learns to identify and fight pathogens.
Genetic ‘usual suspects’ identified in researchers’ new list
March 4, 2019
An exhaustive ranked list of “usual suspect” genes involved in disease may prove invaluable for future research and drug discovery.
David McCandlish named Sloan Research Fellow
February 19, 2019
Assistant Professor David McCandlish has been named a 2019 Sloan Research Fellow for his promising work in the field of quantitative biology.
How does math help us understand the brain?
January 31, 2019
An exploration of how computational neuroscientist Tatiana Engel uses math to understand how the brain makes decisions.
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.
Molly Hammell wins CZI award for ALS study
December 5, 2018
Associate Professor Molly Hammell wins award for proposed study to find transposable elements that are implicated in ALS.
The big problem of small data: A new approach
October 18, 2018
You’ve heard of “big data” but what about small? Researches have crafted a modern approach that could solve a decades-old problem in statistics.
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.
Portrait of a Neuroscience Powerhouse
April 27, 2018
A relatively small neuroscience group at CSHL is having an outsized impact on a dynamic and highly competitive field
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
New method can more precisely determine when a cell has ‘cashed’ RNA ‘checks’ written by active genes
January 26, 2018
CSHL scientists have designed software that enables biologists to determine with unprecedented accuracy how much protein a given cell is making.
Base Pairs Episode 13: A lesson in class
December 15, 2017
We share three stories about classification in life sciences and how genetic information is changing how we define important categories.
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.
Brain cell types are defined by gene activity that shapes their communication patterns
October 20, 2017
CSHL researchers have identified key families of genes in neurons which drive communication.
First cell-type census of mouse brains: surprises about structure, male-female differences
October 5, 2017
A multiyear project in the Brain Initiative, qBrain is already revealing the brain as never before.
Neuron types in the brain are defined by gene activity that shapes their communication patterns
September 21, 2017
Neurons are defined by determining which cells they connect with and how they communicate across synapses
Base Pairs Episode 11.5: What Silicon Valley and biology research share
September 15, 2017
A few favorite moments from our talk with theoretical physicist and quantitative biologist, Associate Professor Gurinder “Mickey” Atwal.
What Silicon Valley and biology research share
September 15, 2017
Further discussion with Associate Professor Gurinder "Mickey" Atwal about the need for more math and physics in biology.
Base Pairs Episode 11: Biology, behind the screens
August 15, 2017
A “behind the screens” look at how biology is addressing its “most wonderful problem”—too much data. Associate Professor Gurinder S. “Mickey” Atwal.
Biology, behind the screens
August 15, 2017
Associate Professor Gurinder "Mickey" Atwal talks about the essential mystery at the center of quantitative biology.
New statistical method finds shared ancestral gene variants involved in autism’s cause
June 21, 2017
Researchers find children with autism are genetically more like other autistic children than their unaffected siblings.
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.
Math teacher wins school popularity contest (again)
June 7, 2017
Math isn’t exactly known as “everyone’s favorite subject,” yet Associate Professor Mickey Atwal has won the Watson School of Biological Science’s.
Newly discovered mutations impair key cell pathways in pancreatic cancer
May 8, 2017
Researchers have found important new clues to the development of pancreatic cancer.
August 15, 2016
This episode on Base Pairs explores how genetic information to better understand human history.
Neanderthals mated with modern humans much earlier than previously thought
May 17, 2016
Using several methods of DNA analysis, a research team has found strong evidence of interbreeding between Neanderthals and modern humans.
Neanderthals mated with modern humans much earlier than previously thought, study finds
February 12, 2016
Using several different methods of DNA analysis, an international research team has found what they consider to be strong evidence of interbreeding.
The biggest beast in the Big Data forest? One field’s astonishing growth is, well, ‘genomical’!
July 6, 2015
Scientists work to figure out how to capture, store, process and interpret all that genome-encoded biological information.
CSHL quantitative biologist Michael Schatz awarded 2015 Sloan Foundation Research Fellowship
February 20, 2015
Associate Professor Michael Schatz receives a 2015 Alfred P. Sloan Foundation Research Fellowship
Harnessing data from nature’s great evolutionary experiment
January 21, 2015
Scientists develop a computational method to estimate the importance of each letter in the human genome
New senior faculty join CSHL
November 10, 2014
Interviews with new faculty Adam Siepel and Douglas Fearon.
Re-learning how to read a genome
November 10, 2014
Study suggests a unified model for how DNA is read, offering insight into how genes evolve
CSHL receives $50 million to establish Simons Center for Quantitative Biology
July 7, 2014
CSHL announced a $50 million gift from Jim and Marilyn Simons to establish the Simons Center for Quantitative Biology.
Researchers propose new way to make sense of ‘Big Data’
February 15, 2014
New application of a classic concept challenges the latest statistical tools.
An error-eliminating fix overcomes big problem in ‘3rd-gen’ genome sequencing
June 29, 2012
The next “next-gen” technology in genome sequencing has gotten a major boost.
Autism study validates importance of spontaneous causal mutations and sheds new light on gender skew
June 8, 2011
A clinically extensive study of 1000 families with one autistic child and one unaffected sibling has validated a controversial theory.
Financial industry pioneers give major boost to next-frontier effort at CSHL in Quantitative Biology
September 24, 2008
Major grants from foundations led by Jim Simons and Hank Greenberg help launch CSHL's Center for Quantitative Biology
2002 CSHL Symposium focuses on cardiovascular system
May 28, 2002
The 67th Symposium on Quantitative Biology focuses on the cardiovascular system
As data generation has grown increasingly efficient and inexpensive, the interpretation of large data sets has emerged as a limiting step for advances in biology. Researchers at the SCQB aim to make sense of this “big data” through the development of innovative modeling, algorithmic, and machine-learning methods, drawing broadly from techniques in mathematics, computer science, and physics. Research in the center is diverse but is permeated by the following four major themes: Gene Regulation, Evolutionary Genomics, Genomic Disease Research, and Genomic Technology.
Kinney and McCandlish are interested in developing both theoretical and experimental methods, along with computational and mathematical tools, for elucidating the relationship between biological sequences and biological functions ranging from gene expression to protein function.
Hammell studies several topics related to gene regulation, including the behavior of small non-coding RNAs, inference of gene regulatory networks, and the impact of transposable elements on gene expression. She has also developed methods for the analysis of single-cell RNA-seq data.
Siepel is broadly interested in modeling the regulation of gene expression in mammals, ranging from transcription factor binding and chromatin accessibility, to transcription initiation and elongation, to the determination of RNA stability.
McCandlish develops theory and mathematics to address a number of open questions in evolutionary genetics, including the dynamics of evolution when mutation is rate-limiting or exhibits biased patterns, and the evolutionary implications of epistasis, i.e. interactions between mutations or genes.
Siepel uses evolutionary methods to identify regulatory elements, to reconstruct early human history, including interbreeding events with Neandertals, and to estimate the fitness consequences of new mutations in the human genome. He is also applying similar methods to agriculturally important plants such as maize and rice.
In addition, Iossifov uses evolutionary signatures to aid in the identification of genes associated with autism spectrum disorder, and Krasnitz uses phylogenetic methods to study the evolution of tumors.
Genomic Disease Research
Iossifov aims to understand the genetics of autism spectrum disorder (ASD) through the analysis of large genomic data sets, in close collaboration with Mike Wigler’s research group and the New York Genome Center.
Krasnitz develops mathematical and statistical tools to characterize the cellular composition, genomic disruptions, evolutionary history, and invasive capacity of malignant tumors, often in collaboration with clinical oncologists.
Hammell studies the role of transposable element activation in neurodegenerative diseases, particularly amyotrophic lateral sclerosis (ALS) and fronto-temporal dementia (FTD).
Atwal is interested in diverse modeling and statistical inference problems having to do with cancer and immunology, often through consideration of single-cell sequencing data.
Genomic Technology Development
Kinney is a pioneer in the development of massively parallel reporter assays for characterizing the relationship between regulatory sequences and gene expression, including both transcription and RNA splicing.
More detailed information about research at the SCQB is available from the faculty websites of the SCQB members and associate members.
In addition to its research activities, the SCQB serves as a hub for education, training and research in the quantitative life sciences.
For more information please contact SCQB@cshl.edu.
SCQB Seminar Series
The SCQB Seminar Series is a weekly symposium featuring a rotating roster of graduate students, postdocs and invited guests. Seminars are held most Wednesdays at noon during the academic calendar year.
See a list of previously invited speakers (pdf).
QB Meetings and Conferences
Members and Associate Members of the SCQB faculty organize relevant QB Meetings and Conferences hosted at CSHL and around the NY area.
- Probabilistic Modeling in Genomics (ProbGen), November 4-7, 2018
- Biological Data Science, November 7-10, 2018
- NY Populations Genomics Workshop (NY popgen), January 7, 2019
QB Scientific Tea
The SCQB community which includes faculty, postdocs, graduate students, staff and special guests are invited to attend weekly catered informal gatherings to discuss their research and other relevant topics.
Members of the SCQB host a bi-weekly Sequence/Function Journal club and a monthly Deep Learning journal club during the academic calendar year.
Opportunities for Postdoctoral Researchers
The Simons Center for Quantitative Biology Fellows Program
The CSHL Simons Center for Quantitative Biology Fellows Program supports research fellows, who function independently but with mentoring from the senior faculty. The program is designed for exceptional quantitative biologists who have recently received their Ph.D. or M.D. degree and who are sufficiently talented and experienced to forgo standard postdoctoral training.
Interdisciplinary Scholars in Experimental and Quantitative Biology Program (ISEQB)
The Interdisciplinary Scholars in Experimental and Quantitative Biology (ISEQB) is an innovative funding opportunity for postdoctoral research open to applications in all areas of research at CSHL, including genetics, cancer, plant biology and neuroscience. The ISEQB is designed to help recruit new postdocs or fund existing CSHL postdocs who are interested in both wet-lab and dry-lab research. This program aims to catalyze collaborative research as well as promote the growth of the QB community at CSHL. For details on how to apply, affiliates of CSHL can visit the ISEQB flyer.
School of Biological Sciences QB Bootcamp at CSHL
The School of Biological Sciences QB Bootcamp is a 2.5-day rapid introduction to Python and the computer cluster at CSHL taught each Fall by the SCQB faculty to provide incoming students with working knowledge in programming in preparation for the full-semester Specialized Discipline Course in Quantitative Biology.
Specialized Discipline Course in Quantitative Biology at CSHL
The Specialized Discipline Course in Quantitative Biology is a 16-week course that aims to equip incoming students with basic training in computer programming, modern statistical methods and physical biology. Using a probabilistic and Bayesian approach, the course covers probabilities, statistical fluctuations, Bayesian inference, significance testing, fluctuations, diffusion, information theory, neural signal processing, dimensional reduction, Monte Carlo methods, population genetics and DNA sequence analyses.
Advanced Coursework in Quantitative Biology
The Simons Center for Quantitative Biology (SCQB) provides Advanced Coursework in Quantitative Biology to graduate students, postdocs and scientific staff. With support from the School for Biological Sciences, we are currently offering Coursera’s Machine Learning through a Massive Open Online Course with onsite teaching assistance provided by SCQB postdoctoral researchers
Alexander J, Kendall J, McIndoo J, Rodgers L, Aboukhalil R, Levy D, Stepansky A, Sun G, Chobardjiev L, Riggs M, Cox H, Hakker I, Nowak DG, Laze J, Llukani E, Srivastava A, Gruschow S, Yadav SS, Robinson B, Atwal G, Trotman L, Lepor H, Hicks J, Wigler M, Krasnitz A. Utility of single-cell genomics in diagnostic evaluation of prostate cancer. Cancer Res. 78(2):348-358, 2018.
Buja A, Volfovsky N, Krieger AM, Lord C, Lash AE, Wigler M, Iossifov I. Damaging de novo mutations diminish motor skills in children on the autism spectrum. Proc. Natl. Acad. Sci. U.S.A., 115(8):E1859-E1866, 2018.
Danko CG, Choate LA, Marks BA, Rice EJ, Zhong W, Chu T, Martins AL, Dukler N, Coonrod SA, Wojno EDT, Lis JT, Kraus WL, Siepel A. Dynamic evolution of regulatory element ensembles in primate CD4+ T-cells. Nat. Ecol. Evol., 2(3):537-548, 2018.
Bienvenu F, Akcay E, Legendre S, and McCandlish DM. The genealogical decomposition of a matrix population model with applications to the aggregation of stages. Theor. Popul. Biol., 115:69–80, 2017.
Huang Y-F, Gulko B, Siepel A. Fast, scalable prediction of deleterious noncoding variants from functional and population genomic data. Nat. Genet. 49(4):618–624, 2017.
Kato M, Vasco DA, Sugino R, Narushima D, Krasnitz A. Sweepstake evolution revealed by population-genetic analysis of copy-number alterations in single genomes of breast cancer. R. Soc. Open Sci., 4(9):171060, 2017.
Kumar V, Rosenbaum J, Zihua W, Forcier T, Ronemus M, Wigler M, Levy D. Partial bisulfite conversion for unique template sequencing. Nucleic Acids Res., 46(2):e10, 2017.
Stoltzfus A and McCandlish DM. Mutational Biases Influence Parallel Adaptation. Mol. Biol. Evol., 34(9):2163–2172, 2017.
Adams RM, Mora T*, Walczak AM*, Kinney JB*. Measuring the sequence-affinity landscape of antibodies with massively parallel titration curves. eLife 5:e23156, 2016.
Atwal GS, Kinney JB. Learning quantitative sequence–function relationships from massively parallel experiments. J Stat Phys. 162(5):1203–1243, 2016.
Kuhlwilm M, Gronau I, Hubisz MJ, de Filippo C, Prado J, Kircher M, Fu Q, Burbano HA, Lalueza-Fox C, de la Rasilla M, Rosas A, Rudan P, Brajkovic D, Kucan Z, Gusic I, Marques-Bonet T, Andres AM, Viola B, Paabo S, Meyer M, Siepel A, and Castellano S. Ancient gene flow from early modern humans into Eastern Neanderthals. Nature, 530(7591):429–433, 2016.
Wang Z, Andrews P, Kendall J, Ma B, Hakker I, Rodgers L, Ronemus M, Wigler M, Levy D. SMASH, a fragmentation and sequencing method for genomic copy number analysis. Genome Res. 26(6):844–851, 2016.
Members of the SCQB have created a number of freely available software tools and web resources for the research community. Here is a list of all the available software tools.
Developed by the Kinney lab, SUFTware (Statistics Using Field Theory) provides fast and lightweight Python implementations of Bayesian Field Theory algorithms for low-dimensional statistical inference. SUFTware currently supports the one-dimensional density estimation algorithm DEFT (Density Estimation Using Field Theory)
Computational genomics; transcriptomics; epigenomics; gene regulation; big data; precision medicine
Computational and theoretical neuroscience; machine learning; statistical physics
Gene networks; gene function prediction; guilt by association; neuropsychiatric; hub genes; multifunctionality; computational genomics
Theoretical neurobiology; quantitative principles of cortical design; computer science; applied mathematics
Genomics of psychiatric disorders; genomics of cancer; computational genomics; plant genomics
Neuroscience and theoretical biology
Genomics; genome evolution; genetic diversity; gene regulation; plant biology; computational biology
Autism, SFARI, AGRE, SNVs, CNVs, whole-genomeexome sequencing, single-cell sequencing and bulksingle RNA sequencing
To ensure that cells function normally, tens of thousands of genes must be turned on or off together. To do this, regulatory molecules—transcription factors and non-coding RNAs—simultaneously control hundreds of genes. My group studies how the resulting gene networks function and how they can be compromised in aging associated diseases, such as neurodegeneration and cancer.
Every gene has a job to do, but genes rarely act alone. Biologists have built models of molecular interaction networks that represent the complex relationships between thousands of different genes. I am using computational approaches to help define these relationships, work that is helping us to understand the causes of common diseases including autism, bipolar disorder, and cancer.
Research in the Kinney Lab combines mathematical theory, machine learning, and experiments in an effort to illuminate how cells control their genes. These efforts are advancing the fundamental understanding of biology and biophysics, as well as accelerating the discovery of new treatments for cancer and other diseases.
Deep learning has the potential to make a significant impact in basic biology and cancer, but a major challenge is understanding the reasons behind their predictions. My research develops methods to interpret this powerful class of black box models, with a goal of elucidating data-driven insights into the underlying mechanisms of sequence-function relationships.
Many types of cancer display bewildering intra-tumor heterogeneity on a cellular and molecular level, with aggressive malignant cell populations found alongside normal tissue and infiltrating immune cells. I am developing mathematical and statistical tools to disentangle tumor cell population structure, enabling an earlier and more accurate diagnosis of the disease and better-informed clinical decisions.
We have recently come to appreciate that many unrelated diseases, such as autism, congenital heart disease and cancer, are derived from rare and unique mutations, many of which are not inherited but instead occur spontaneously. I am generating algorithms to analyze massive datasets comprising thousands of affected families to identify disease-causing mutations.
Some mutations are harmful but others are benign. How can we predict the effects of mutations, both singly and in combination? Using data from experiments that simultaneously measure the effects of thousands of mutations, I develop computational tools to predict the functional impact of mutations and apply these tools to problems in protein design, molecular evolution, and cancer.
A properly functioning immune system must be able to recognize diseased cells and foreign invaders among the multitude of healthy cells in the body. This ability is essential to both prevent autoimmune diseases and fight infections and cancer. We study how a specific type of immune cells, known as T cells, are educated to make this distinction during development.
Biological systems must solve problems to survive, and their solutions can be viewed as “algorithms.” Our goal is to uncover these algorithms, translate them to improve computer science, and use them to spark new hypotheses about biological function and dysfunction.
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 molecular evolution and transcriptional regulation, with applications to cancer and other diseases as well as to plant breeding and agriculture.