[Colloquium] [Reminder] DSI Talk: Genevera Allen, Rice University (10/18, 3pm)

[Rob Mitchum]

*Data Science Institute Distinguished Speaker Series*

*Genevera Allen*
*Associate Professor of Electrical and Computer Engineering, Statistics,
and Computer Science*
*Rice University*

*Monday, October 18th*
*3:00 p.m. - 4:00 p.m.*
*In-Person (JCL 390) or Live Stream (Zoom, YouTube
<https://youtu.be/jh4dvvhnBDc>)*
*Register
<https://www.eventbrite.com/e/genevera-allen-rice-graph-learning-for-functional-neuronal-connectivity-tickets-185024472177>
with
preference for in-person or remote*


*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.

*Bio:* 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).
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