John P. Cunningham, Ph.D.Assistant Professor, Statistics, Columbia University
John P. Cunningham studies the seemingly effortless mental capabilities that belie the sophisticated computational machinery at work in our nervous systems, and investigates the computational properties of our neural systems. Specifically, he develops machine learning, optimization, and signal processing algorithms for analysis of neural data, primarily in the human and primate cortex. He is particularly interested in the internally driven structure of neural population responses — structure that need not be simply correlated to external behaviors — and the analytical approaches needed to study this structure. His work has two foci: first, to advance scientific understanding of the neural basis of movement and other complex cortical processes; second, to advance data analytical methods in applied statistics, machine learning, and computational learning in general.
Cunningham received a B.A. in computer science from Dartmouth College in 2002, and a M.S. and Ph.D. in electrical engineering from Stanford University in 2009, during which he was a close collaborator and sometime visitor to the Gatsby Computational Neuroscience Unit, University College London. He did two years of postdoctoral work at the University of Cambridge (2010 and 2011), where he was a research associate in the Department of Engineering with the Machine Learning Group, and where he was the Sackler Research Fellow of Christ’s College. He has presented and co-organized workshops at major conferences, was an invited speaker of the British Neuroscience Association in 2011, and an invited lecturer at the Machine Learning Summer School in 2012. Since 2013, he has been at Columbia University as an assistant professor in statistics, where he is also a member of the Grossman Center for the Statistics of Mind, the Center for Theoretical Neuroscience, the Institute for Data Science and Engineering; the Zuckerman Mind, Brain and Behavior Initiative; and the Neuroscience Graduate Program. Cunningham’s work has been funded by the Michael J. Flynn Graduate Fellowship, the Engineering and Physical Sciences Research Council (UK), and the Simons Foundation.
Integrative approaches to understanding whole-brain computation
Leveraging dynamical smoothness to predict motor cortex population activity
Neural dynamics of a multi-timescale social behavior
Interaction of sensory signals and internal dynamics during decision-making
Spatiotemporal structure of neural population dynamics in the motor system