Chen Institute Director's Seminar: Dr. Scott Linderman
Join us for a Chen Institute Director's Seminar on Tuesday, June 4 at 4:00PM in Chen 100.
Refreshments will be available in the breezeway before the seminar at 3:30PM.
Speaker: Dr. Scott Linderman
Title: Latent States of Brains and Behavior
Abstract: New recording technologies are revolutionizing neuroscience, allowing us to measure the spiking activity of hundreds to thousands of neurons in freely behaving animals. These technologies offer exciting opportunities to link brain activity to behavioral output, but they also pose statistical challenges. Neural and behavioral data are noisy, high-dimensional time series with nonlinear dynamics and substantial variability across subjects. I will present our work on state space models (SSMs) for such data. The key idea is that these high-dimensional measurements reflect the evolution of low-dimensional latent states, which shed light on how neural circuits compute and how natural behavior is structured. I will present our work on SSMs that disentangle discrete and continuous factors of variation in time series data, and I will highlight several ways in which we have used these techniques to gain new insight into the neural computations underlying naturalistic behavior. For example, we have used SSMs to connect stereotyped movements to moment-to-moment fluctuations in dopamine, to develop models of aging with continuous, whole-lifespan behavioral recordings, and to study how neural attractor dynamics encode persistent internal states during social interaction. Together, these projects demonstrate how our contributions to machine learning and statistics offer powerful new tools for linking brain activity and behavior.
Bio: Scott is an Assistant Professor of Statistics and, by courtesy, Electrical Engineering and Computer Science at Stanford University. He is also an Institute Scholar in the Wu Tsai Neurosciences Institute and a member of Stanford Bio-X and the Stanford AI Lab. His lab works at the intersection of machine learning and computational neuroscience, developing statistical methods to analyze large scale neural data. Previously, Scott was a postdoctoral fellow with Liam Paninski and David Blei at Columbia University, and he completed his PhD in Computer Science at Harvard University with Ryan Adams and Leslie Valiant. He obtained his undergraduate degree in Electrical and Computer Engineering from Cornell University and spent three years as a software engineer at Microsoft before graduate school.