CS Gerald M. Masson Distinguished Lecture Series: Genevera Allen

Description

Genevera Allen, an associate professor of electrical and computer engineering, statistics, and computer science at Rice University, will give a talk as part of the Computer Science Gerald M. Masson Distinguished Lecture Series titled "Graph Learning for Functional Neuronal Connectivity."

Please attend the event by using the Zoom link (meeting ID: 913 9242 3371).

Abstract:

Understanding how large populations of neurons communicate and jointly fire in the brain is a fundamental open question in neuroscience. Many approach this by estimating the intrinsic functional neuronal connectivity using probabilistic graphical models. But there remain major statistical and computational hurdles to estimating graphical models from new large-scale calcium imaging technologies and from huge projects which image up to one hundred thousand neurons in the active brain.

In this talk, Genevera Allen will highlight a number of new graph learning strategies her group has developed to address many critical unsolved challenges arising with large-scale neuroscience data. Specifically, she will focus on Graph Quilting, in which she derives a method and theoretical guarantees for graph learning from non-simultaneously recorded and pairwise missing variables. Allen will also highlight theory and methods for graph learning with latent variables via thresholding, graph learning for spikey data via extreme graphical models, and computational approaches for graph learning with huge data via minipatch learning. Finally, she will demonstrate the utility of all approaches on synthetic data, as well as real calcium imaging data for the task of estimating functional neuronal connectivity.