Liam Paninski, Ph.D.

Columbia UniversityLiam Paninski, Ph.D.’s Website

Liam Paninski is a professor in statistics and neuroscience at Columbia University, the co-director of the Grossman Center for the Statistics of Mind (a research and training center with a focus on big data in neuroscience) and the director of Ph.D. studies for the statistics department. He is also a member of the Center for Theoretical Neuroscience, the Institute for Data Science and Engineering and the Kavli Institute for Brain Science at Columbia.

Paninski’s group develops statistical methodology and theory for analyzing neural data, and has a particular interest in understanding information encoding and decoding by large neural populations, with expertise in large retinal data, calcium imaging video data and neural prosthetics research. Other interests include methodologies for estimating connectivity in neuronal networks, for optimal experimental design, for efficient inference in high-dimensional state-space models and for fast Monte Carlo methods. Paninski teaches two relevant Ph.D. courses, in computational statistics and in statistical analysis of neural data.

Paninski received his Ph.D. from New York University in 2003 and has been a faculty member at Columbia since 2005. He has received the NSF CAREER Award, McKnight Scholar Award, MIT Technology Review’s TR35, and Sloan Research Fellowship awards, and is co-author, with Wulfram Gerstner, Werner Kistler and Richard Naud, of a new textbook, Neuronal Dynamics, and has another book under contract, Statistical Analysis of Neural Data, with Rob Kass, Uri Eden and Emery Brown.

Current Projects:

Integrative approaches to understanding whole-brain computation

Discovering repeating neural motifs representing sequenced behavior

International Brain Lab

Leveraging dynamical smoothness to predict motor cortex population activity

Neural Circuit Dynamics Underlying Sequence and Variability

Neural Dynamics of a Multi-timescale Social Behavior

Past Projects:

Analyzing a complex motor act at the mesoscopic scale

Understanding neural computations across the global brain

Spatiotemporal structure of neural population dynamics in the motor system

Circuit mechanisms of social cognitive computations

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