Talk Title: State Space Models for Neural and Behavioral Data

Abstract:  New recording technologies have revolutionized 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 computational and 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 show how we have developed SSMs to study attractor dynamics underlying persistent internal states of social interaction, how these models have shed light on time-varying goal states underlying natural behavior, and how these experimental collaborations have motivated further methodological innovations. Altogether, these projects demonstrate how our advances in statistics and machine learning offer powerful new tools for gaining insight into the neural computations underlying naturalistic behavior.

Bio: I'm an Assistant Professor of Statistics and an Institute Scholar in the Wu Tsai Neurosciences Institute at Stanford University. I hold a courtesy appointment in Computer Science, and I'm a member of Stanford Bio-X and the Stanford AI Lab. I was a postdoctoral fellow with Liam Paninski and David Blei at Columbia University, and I completed my PhD in Computer Science at Harvard University with Ryan Adams and Leslie Valiant. I obtained my undergraduate degree in Electrical and Computer Engineering from Cornell University and spent three years as a software engineer at Microsoft prior to graduate school.

Followed by a reception with food and drink in 3^rd floor atrium