Graph Learning for Functional Neuronal Connectivity

Genevera Allen, Rice University
Host: Johns Hopkins Department of Computer Science

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. Dr. 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.

Speaker Biography

Genevera Allen is an Associate Professor of Electrical and Computer Engineering, Statistics, and Computer Science at Rice University and an investigator at the Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital and Baylor College of Medicine. She is also the Founder and Faculty Director of the Rice Center for Transforming Data to Knowledge, informally called the Rice D2K Lab.

Dr. Allen’s research focuses on developing statistical machine learning tools to help people make reproducible data-driven discoveries. Her work lies in the areas of interpretable machine learning, data integration, modern multivariate analysis, and graphical models with applications in neuroscience and bioinformatics. In 2018, Dr. Allen founded the Rice D2K Lab, a campus hub for experiential learning and data science education.

Dr. Allen is the recipient of several honors for both her research and teaching including a National Science Foundation Career Award, Rice University’s Duncan Achievement Award for Outstanding Faculty, and the George R. Brown School of Engineering’s Research and Teaching Excellence Award; in 2014, she was named to the “Forbes ’30 under 30′: Science and Healthcare” list. Dr. Allen received her Ph.D. in statistics from Stanford University (2010), under the mentorship of Prof. Robert Tibshirani, and her bachelors, also in statistics, from Rice University (2006).