[DSI] Genevera Allen (Rice) - Graph Learning for Functional Neuronal Connectivity

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October 18, 2021 at 3:00pm - 4:00pm
JCL 390 & Livestream
Event Audience:
Genevera Allen

Speaker: Genevera Allen Associate Professor, Rice University

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).

Abstract: Graph Learning for Functional Neuronal Connectivity

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, I will highlight a number of new graph learning strategies my group has developed to address many critical unsolved challenges arising with large-scale neuroscience data. Specifically, we will focus on Graph Quilting, in which we derive a method and theoretical guarantees for graph learning from non-simultaneously recorded and pairwise missing variables. We 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, we 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.

This event will take place both in-person at John Crerar Library Building Room 390 and online via Zoom or Youtube. Please register and choose your preference for additional details.

Part of the Data Science Institute Distinguished Speaker Series:

Defining The Field of Data Science

As data science evolves from buzzword to a mature and singular field, its research questions dive deeper into the foundations of this new discipline. The Fall 2021 Distinguished Speaker Series convenes world-class experts actively exploring and expanding the fundamental methods and approaches that transform large and complex datasets into knowledge and action, fueling new applications in areas such as artificial intelligence, healthcare, and the social sciences. Join the new UChicago Data Science Institute for provocative talks and discussion that will illuminate the bedrock and promise of the flourishing field of data science.

This convening is open to all invitees who are compliant with UChicago vaccination requirements and, because of ongoing health risks, particularly to the unvaccinated, participants are expected to adopt the risk mitigation measures (masking and social distancing, etc.) appropriate to their vaccination status as advised by public health officials or to their individual vulnerabilities as advised by a medical professional. Public convening may not be safe for all and carries a risk for contracting COVID-19, particularly for those unvaccinated. Participants will not know the vaccination status of others and should follow appropriate risk mitigation measures.”

Host: Data Science Institute

Type: talk