CSHL Menu

Quantitative Biology

Siepel Lab
The Simons Center for Quantitative Biology (SCQB) is Cold Spring Harbor Laboratory’s home for mathematical, computational, and quantitative experimental research.

Research in the SCQB centers on genomics (how genomes work, how they evolve, and what makes them go wrong in disease) and neuroscience (how brains are structured and how they process information). In addition to advancing biological knowledge, members of the SCQB develop broadly useful experimental, computational, and mathematical methods for the wider research community. BioAI—the interface of cutting-edge biological research and revolutionary artificial intelligence technologies—is a major theme of much of this work.

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.

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.

Leadership

Interim Chair

Justin Kinney, Ph.D.

QB/AI Seminar Series Lead

Hannah Meyer, Ph.D.

Center Staff

Sr. Scientific Administrator & Assistant to the Chair

Susan Fredricks

Sr. Scientific Administrator

Antonia Little

Assistant Director of Administration, Cancer & Simons Centers

Katie Brenner

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
Cornell University

David L. Donoho, Ph.D.
Anne T. and Robert M. Bass Professor of Humanities and Sciences
Professor of Statistics
Stanford University

Eric D. Siggia, Ph.D.
Viola Ward Brinning and Elbert Calhoun Brinning Professor
Head of Laboratory of Theoretical Condensed Matter Physics
The Rockefeller University

Steven L. Salzberg, Ph.D. (Chair)
Bloomberg Distinguished Professor of Biomedical Engineering, Computer Science, and Biostatistics
Director, Center for Computational Biology
Johns Hopkins University

Simons Center for Quantitative Biology Annual Reports

In addition to its research activities, the SCQB serves as a hub for education, training and research in the quantitative life sciences.

QB seminar event

Events

QB/AI Seminar Series

The QB/AI 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.

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
  • Biological Data Science
  • NY Populations Genomics Workshop

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.

Journal Clubs

Members of the SCQB host a bi-weekly Sequence/Function Journal club and a monthly Deep Learning journal club during the academic calendar year.

QB postdocs 2018

Opportunities for Postdoctoral Researchers

The CSHL Fellows Program

The CSHL 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.

QB coursework

Course Work

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 through independent study programs and online coursework.

Benjamin Cowley

Benjamin Cowley

How do we identify and describe the step-by-step computations of the brain? The Cowley group identifies data-driven models of neural responses and behavior by coupling data collection with model training during closed-loop experiments. We condense these models into compact, interpretable forms—allowing us to describe the complicated computations of the brain in a clear and concise way.

Helen Hou

Helen Hou

The brain-body interaction is a two-sided coin: The brain can control movement of the body to fulfill behaviors, and behavior itself can affect brain function. We study how the brain orchestrates motor and physiological control in natural and innate behaviors, focusing on facial expression.

Ivan Iossifov

Ivan Iossifov

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.

Mitra Javadzadeh

Mitra Javadzadeh

Sensory stimuli evoke activity in millions of neurons spread across multiple brain regions. This activity evolves over time, not only due to the constantly changing outside world, but also as a result of the internal interactions within these brain-wide networks. We aim to understand how distributed neural population dynamics are organized, and how they underlie our robust and yet flexible perception.

Justin Kinney

Justin Kinney

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.

David Klindt

David Klindt

Our research explores how biological systems, such as the brain, learn from sensory data and generalize knowledge to new situations, inspiring the development of more robust artificial intelligence models. By investigating neural representations and leveraging expertise in computational neuroscience and AI, we aim to uncover groundbreaking insights at the intersection of biology and technology.

Peter Koo

Peter Koo

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.

Alexei Koulakov

Alexei Koulakov

The complexity of the mammalian brain challenges our ability to explain it. My group applies methods from mathematics and theoretical physics to understand the brain. We are generating novel ideas about neural computation and brain development, including how neurons process information, how brain networks assemble during development, and how brain architecture evolved to facilitate its function.

Alexander Krasnitz

Alexander Krasnitz

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.

Dan Levy

Dan Levy

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.

David McCandlish

David McCandlish

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.

Hannah Meyer

Hannah Meyer

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.

Partha Mitra

Partha Mitra

A theoretical physicist by training, my research is centered around intelligent machines. I do both theoretical and experimental work. The theoretical work is focussed on analyzing distributed/networked algorithms in the context of control theory and machine learning, using tools from statistical physics. My lab is involved in brain-wide mesoscale circuit mapping in the Mouse as well as in the Marmoset. An organizing idea behind my research is that there may be common underlying mathematical principles that constrain evolved biological systems and human-engineered systems.

Saket Navlakha

Saket Navlakha

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.

Adam Siepel

Adam Siepel

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.

Doreen Ware

Doreen Ware

When we think of evolution, we often think about physical changes, like a plant developing broader leaves to collect more solar energy. Such evolution actually occurs within the plant’s DNA. I am using computational analysis and modeling to visualize how plant genomes have evolved over time, particularly those of staple crops. We are learning from this work to improve the range and yield of modern plants.