Stavros Niarchos Foundation Brain Insight Lecture

Driving brain breakthroughs with data science

Obtaining rigorous, plentiful data is key for scientific research. But that is only one part of the larger picture. Increasingly abundant and complex, these data require researchers across disciplines to come together and devise new tools to separate the signal from the noise. What secrets about the brain can emerge from data-driven exploration? And how can innovative analysis techniques open doors for the next questions neuroscientists pursue? In this event, three experts bring together perspectives spanning statistics, genomics, and neuroscience to discuss the stories data can tell us.

Genevera Allen, PhD, Professor of Statistics at Columbia University, Principal Investigator at Columbia's Zuckerman Institute, and member of the Center for Theoretical Neuroscience and the Irving Institute for Cancer Dynamics, will begin by describing her research building novel statistical tools to revolutionize how neuroscientists make discoveries. As experimental techniques continually improve, so do the quality, quantity, and diversity of brain data collected in the lab. Analyzing datasets that include troves of measurements, signals, images, and video, Dr. Allen seeks to unearth rare, hidden insights about the brain buried in mountains of information. For example, how can new statistical approaches reveal the relationship between brain activity and human traits? And how can we ensure that the conclusions drawn are meaningful and true?

Tarjinder (TJ) Singh, PhD, Assistant Professor of Computational and Statistical Genomics in the Department of Psychiatry at Columbia University’s Vagelos College of Physicians and Surgeons and the New York State Psychiatric Institute, an associate faculty member at the New York Genome Center, and an affiliate of Columbia’s Zuckerman Institute, will then discuss his research using genomics and big data to understand the causes of psychiatric disorders. Aimed at uncovering the mechanisms that underlie diseases like schizophrenia and bipolar disorder, Dr. Singh’s work pulls together large-scale genomic data from hundreds of thousands of individuals to identify potential risk factors. Which genes are associated with increased risk? And how can this work inform new therapeutic approaches to treatment?

Following the two talks, Dr. Jeff Johnston, Postdoctoral Research Scientist in the lab of Dr. Stefano Fusi at Columbia’s Zuckerman Institute, will moderate a discussion and Q&A with the speakers. Audience questions are welcomed, either submitted during registration or live at the event.

About the experts

Genevera Allen, PhD, is a Professor of Statistics at Columbia University. She is also a member of the Center for Theoretical Neuroscience, the Zuckerman Mind Brain Behavior Institute, and the Irving Institute for Cancer Dynamics. Prior to joining Columbia, Dr. Allen spent fourteen years at Rice University in the Departments of Electrical and Computer Engineering, Statistics, and Computer Science; she was also the Founder and served as the Faculty Director of Rice’s data science education center, informally known as the Rice D2K Lab. Dr. Allen’s research develops new statistical machine learning tools to help people make reliable discoveries from data. She is known for her methods and theory work in the areas of unsupervised learning, interpretable machine learning, data integration, graphical models, and high-dimensional statistics. Her work is motivated by solving real scientific problems, especially in the areas of neuroscience and bioinformatics. Dr. Allen is the recipient of several honors including a National Science Foundation Career Award, Rice University’s Duncan Achievement Award for Outstanding Faculty, and in 2014, she was named to the “Forbes ’30 under 30′: Science and Healthcare” list. She is also an elected fellow of the American Statistical Association, Institute of Mathematical Statistics, and International Statistics Institute. Dr. Allen serves as an Action Editor for the Journal of Machine Learning Research, an Associate Editor for the Journal of the American Statistical Association: Theory and Methods and the Journal of the Royal Statistical Society: Series B, on the editorial board of Foundations and Trends in Machine Learning and Annual Reviews of Statistics and Its Application, and finally as a Series Editor for Springer Texts in Statistics. Dr. Allen received her Ph.D. in statistics from Stanford University, under the mentorship of Prof. Robert Tibshirani, and her bachelors, also in statistics, from Rice University.

Tarjinder (TJ) Singh, PhD, is an Assistant Professor of Computational and Statistical Genomics in the Department of Psychiatry at Columbia University’s Vagelos College of Physicians and Surgeons and the New York State Psychiatric Institute. He is an associate faculty member at the New York Genome Center and an affiliate of the Zuckerman Mind Brain Behavior Institute. Singh earned his B.A. in Biology, Economics, and Mathematics from Williams College, followed by a Ph.D. in Biological Sciences from the University of Cambridge. He completed postdoctoral training and served as an Instructor at Massachusetts General Hospital and Harvard Medical School, working in the Stanley Center for Psychiatric Research at the Broad Institute. His research integrates large-scale genomic and transcriptomic datasets with statistical and machine learning methods to elucidate the genetic and molecular architecture of severe neuropsychiatric disorders, including schizophrenia, bipolar disorder, and suicidality. Dr. Singh’s work has contributed to international gene discovery efforts, identifying specific genes in which rare coding variants confer substantial risk for psychiatric illness. His lab focuses on advancing gene discovery by analyzing large-scale biobank sequencing data and medical records to define clinically meaningful psychiatric subtypes. The group integrates single-cell multiomic profiling and CRISPR-based assays in patient-derived neurons and postmortem brain tissue to investigate disease mechanisms. Dr. Singh leads or co-leads several interdisciplinary initiatives that connect genomic discovery with translational neuroscience, including the BD² Bipolar Disorder Discovery Platform and projects funded by the Brain & Behavior Research Foundation. His long-term goal is to develop integrative frameworks that connect genetic findings from population studies to cell-type-specific mechanisms, ultimately enabling biologically informed treatments for psychiatric illnesses.

Jeff Johnston, PhD, is a postdoctoral research scientist at Columbia University in the lab of Dr. Stefano Fusi in the Center for Theoretical Neuroscience and at the Zuckerman Mind Brain Behavior Institute. He completed his graduate work with David Freedman at the University of Chicago, where he received an NRSA predoctoral fellowship from the NIH and the best dissertation award in computational neuroscience. Dr. Johnston’s work focuses on how the activity of neural populations are both shaped by and, in turn, shape behavior. His work uses a combination of approaches including data analysis, computer simulations, and mathematical theory. Recently, Dr. Johnston has shown that “abstract” representations, which support the ability to generalize particular behaviors from one context to another, emerge naturally in artificial neural networks trained to perform multiple tasks, suggesting a method that may be at work in the brain. He has also developed a geometric theory that captures the tradeoff between the representational geometry that enables this generalization and one that enables flexible behavior.

This talk is part of the Stavros Niarchos Foundation Brain Insight Lecture series, offered free to the public to enhance understanding of the biology of the mind and the complexity of human behavior. The lectures are hosted by Columbia’s Mortimer B. Zuckerman Mind Brain Behavior Institute and supported by the Stavros Niarchos Foundation. When registering, please indicate whether you wish to attend in person or watch the livestream virtually. All registrants will receive a Zoom link for the livestream and a recording of the lecture afterwards.