Graph Learning for Genomics and Neuroscience (Canceled, due to ongoing loss of electric power in Texas.)
Genevera Allen
February 17, 2021, Wednesday, 3:00 PM - 4:00 PM EDT
Abstract
Probabilistic Graphical Models are widely used tools to study relationships and visualize interactions in large and complex data sets. They represent probability distributions as a graph with edges denoting conditional dependence relationships between random variables. These models have been studied and developed in computer science, probability, and statistics, with wide ranging applications in artificial intelligence, computer vision, physics, systems biology, and neuroscience, to name a few. This talk will review structural learning in probabilistic graphical models, which seeks to learn the unknown edges or conditional dependencies between random variables and highlight new methods and theory from my research group for graph structural learning from big biomedical data. Specifically, I will discuss graph learning for non-Gaussian data and mixed data via data integration, for non-simultaneously recorded or non-aligned data via Graph Quilting, and for graph learning in the presence of latent variables. Additionally, I will present several applications and case studies using these approaches to make scientific discoveries in integrative genomics and neuroscience.
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 scientists make reproducible data-driven discoveries. Her work lies in the areas of interpretable machine learning, optimization, data integration, modern multivariate analysis, and graphical models with applications in neuroscience and bioinformatics. Dr. Allen is the recipient of several honors including a National Science Foundation Career award, the George R. Brown School of Engineering's Research and Teaching Excellence Award at Rice University, and in 2014, she was named to the "Forbes '30 under 30': Science and Healthcare" list. Dr. Allen received her PhD in statistics from Stanford University (2010), under the mentorship of Prof. Robert Tibshirani, and her bachelors, also in statistics, from Rice University (2006).