Read time 6 minutes | Tuesday, 8 September 2026
We live in an age of big data—and that’s a boon for biologists. Neuroscientists listen in on the chatter of thousands of neurons, recording the individual activity of each one. Geneticists sequence entire genomes in a matter of hours. Rich information about animal behavior is captured in a simple video, tracked across thousands of frames.
But what does all that data add up to?
Indeed, as researchers churn out more and more data, the challenge has become finding meaning within it. This puts scientists with deep training in mathematics and computational methods in demand. At Cold Spring Harbor Laboratory, biologists with those skills come together through the Simons Center for Quantitative Biology (SCQB), where they use computational tools to uncover new insights about life on Earth.
Scientists at the SCQB work primarily in genomics, neuroscience, and related fields. Their studies are diverse, probing some of biology’s most fundamental principles, with broad implications for human health, disease, agriculture, and artificial intelligence.
What unifies these scientists is also what sets them apart from your classically trained biologist. It’s a certain way of seeing the world.
“When you have quantitative training, different things interest you,” says Professor Justin Kinney, head of the quantitative biology program at CSHL and chair of the SCQB. “The questions you pose as a result are liable to be quite different. That allows us to advance science in a different direction than through standard experimental investigations.”
Seeing life differently
Take, for example, CSHL Associate Professor Saket Navlakha. When you look at nature, you might see plants and animals. He sees algorithms. Navlakha’s curiosity about how living things solve computational problems has led him to research how fruit flies recognize new smells and how Chinese money plants segment their leaves.
The natural algorithms he uncovers sometimes outperform state-of-the-art computational methods. Thus, they may offer ways to improve popular technology applications. Of course, it’s not all about smarter machines and faster computers. When quantitative biologists seek out meaningful patterns in vast quantities of data, what they find can have staggering biomedical implications.
For example, CSHL Professor Ivan Iossifov has built tools to help researchers mine the genomes of hundreds of thousands of people with autism, as well as their family members. Their search for clues into the origins of the developmental disorder has led to better diagnostics, enabling families to access care sooner. Iossifov is now working with other CSHL faculty members to investigate the immune system’s potential role in autism development.
CSHL Associate Professor David McCandlish and Professor Zachary Lippman speak with CBS reporter Carolyn Gusoff. Video: CBS News.
A quantitative way of thinking can steer the research of CSHL scientists outside the SCQB in new directions, too. One example comes from the ongoing collaboration between Associate Professor David McCandlish, a computational scientist in the SCBQ, and CSHL Professor Zachary Lippman, a plant biologist interested in food crops.
Manipulating genomes to modify traits is not as straightforward as plant breeders would like, largely due to complicated interactions among genes. However, the computational tools that McCandlish develops deal with exactly that kind of genomic complexity. Together, McCandlish and Lippman have tested and analyzed the effects of hundreds of combinations of engineered and natural mutations, pointing toward targeted strategies for growing more and better food crops.
But that’s not all. Because the genomic insights they’ve uncovered are so fundamental, their work may also have implications for developing new, safer medicines with fewer and less harmful side effects.
Discovery, accelerated
One set of tools that is empowering every lab in one way or another is artificial intelligence. Here, recent technological advances have been so dramatic that Kinney says, “it’s been a real whirlwind of a year.” New AI tools are changing the way SCQB scientists work day-to-day, from streamlining searches of scientific literature to writing code, and the impact is profound. “When we have ideas for algorithms, we can now spin up tests of those very fast,” Kinney says. “We can do science quicker.”
Many SCQB scientists develop AI tools of their own. CSHL Associate Professor Peter Koo builds AI to interrogate widely used genomic models that analyze DNA regulation. By identifying areas for improvement in these models, his work enables scientists to experiment with stronger hypotheses—i.e., less slop, more substance.
That’s important when you consider the work of scientists like CSHL Professor Adam Siepel. The former SCQB chair has developed algorithms to construct evolutionary family trees of both animals and viruses. Now, he’s applying machine-learning techniques to study how cancer evolves and spreads.
AI in Biology was the topic of the 90th annual Cold Spring Harbor Symposium on Quantitative Biology, held in May 2026.
Meanwhile, on the neuroscience side, CSHL Assistant Professor Helen Hou and collaborators have created a computer vision system to analyze facial movements in mice, enabling researchers to detect subtle behavioral patterns. Other SCQB neuroscientists, like CSHL Assistant Professors Benjamin Cowley and David Klindt, use artificial neural networks as models of the brain, creating opportunities to explore how our nervous system processes information and understands the world.
As CSHL scientists take advantage of massive leaps in computing power and AI, the Lab is planning a significant expansion of its quantitative biology program. A new Biological and Artificial Intelligence (BioAI) building will open in 2027, providing lab space and computational resources for a growing faculty, with a focus on further integrating AI, neuroscience, genomics, and other areas of biology.
At the same time, the SCQB is committed to training a new generation of scientists with expertise in both quantitative methods and biological systems. The School of Biological Sciences’ new BioAI Ph.D. program allows graduate students with master’s level training in fields such as math, physics, engineering, and computer science to enter CSHL labs directly. There, they learn to apply their quantitative skills to important biological problems.
“An institution where biology is king”
SCQB scientists take advantage of the lack of traditional departmental boundaries, which allows interdisciplinary research to flourish at CSHL. Under Kinney’s leadership, the SCQB has integrated its neuroscience and genomics programs through shared seminars and lecture series.
Whereas at other, more structured institutions, scientists investigating neural circuits and gene regulation might not interact, “in the Simons Center, those researchers talk to each other,” Kinney says. “They see how computational and experimental methods are used in a wide variety of systems, and it helps pollinate cross-disciplinary science.”
As for Kinney, his work combines mathematical theory, AI, and experiments to explore how cells control gene expression. His lab, too, has benefited from the cross-pollination that runs through CSHL. For example, the Interdisciplinary Scholars in Experimental and Quantitative Biology program offers CSHL postdocs opportunities to receive mentorship from both quantitative and experimental biologists.
It was through this program that postdocs Mandy Wong and Yuma Ishigami connected Kinney’s group with the lab of CSHL Professor Adrian Krainer, a renowned molecular biologist. Together, they’re now working on projects comparing how different RNA therapeutics work and what causes them to behave differently in patients.
“The old model for collaboration was that an experimentalist would generate data, and then they would look for a quantitative researcher to help analyze the data,” Kinney says. “What we’re trying to do is organically grow interactions that are much deeper.”
Looking ahead, Kinney says that while the SCQB’s goals are ambitious, CSHL’s unique academic environment sets its scientists up to succeed. “We want to come up with conceptually new ways of studying biology that yield new insights into how life works.” What makes that possible, he says, is that “we are pursuing this work not in a machine learning department or a physics department or a math department, but in an institution where biology is king.”
Written by: Jennifer Michalowski, Science Writer | [email protected] | 516-367-8455
About
Justin Kinney
Professor and Chair, Simons Center for Quantitative Biology
Ph.D., Princeton University, 2008
artificial intelligencebig dataJustin Kinneymachine learningQuantitative BiologySimons Center for Quantitative Biology
