Schedule
Click Talk Titles to view Abstracts
Friday October 21, 2022
*8:30am – 9:00am
Breakfast and Registration
*9:00am – 9:30am
Welcome and Introduction
*9:30am – 10:20am
Keynote Speaker: Ed George, University of Pennsylvania
Talk Title: “The Remarkable Flexibility of BART”
Chair: Mohsen Pourahmadi
*10:30am – 11:45am
Session 1 – Advances in Matrix and Tensor Learning
Organizer & Chair: Raymond Wong
Ranjan Maitra, Iowa State University
Talk Title: “Reduced-Rank Tensor-on-Tensor Regression and Tensor-variate Analysis of Variance”
Zhengwu Zhang, University of North Carolina, Chapel Hill
Talk Title: “Tensor Decomposition and its Applications in Brain Network Analysis”
Hua Zhou, University of California, Los Angeles
Talk Title: “Orthogonal Trace-sum Maximization: Applications, Local Algorithms, and Global Optimality”
Session 2 – Cutting Edge Machine Learning Techniques and their Applications
Organizer: Bani Mallick
Chair: Yang Ni
Genevera Allen, Rice University
Talk Title: “Fast, Model-Agnostic Confidence Intervals for Feature Importance via Minipatch Ensembles”
Sujay Sanghavi, University of Texas at Austin
Talk Title: “Ignoring Causality to Improve Ranking”
James Sharpnack, University of California, Davis
Talk Title: “Lessons in Domain Adaption and an Unsupervised Hunt for Gravitational Lenses”
*12:00pm – 12:50pm
Keynote Speaker: Moshe Vardi, Rice University
Talk Title: “How to be an Ethical Computer Scientist“
Chair: Nick Duffield
*1:00pm – 1:45pm
Working Lunch
*2:00pm – 2:50pm
Keynote Speaker: James Scott, University of Texas at Austin
Talk Title: “BART and its Variations: Three Applications in Obstetrics”
Chair: Matthias Katzfuss
*3:00pm – 4:15pm
Session 3 – Foundations of Approximate Bayesian Inference
Organizer: A. Bhattacharya, D. Pati
Chair: Patricia Ning
Jonathan Huggins, Boston University
Talk Title: “Trustworthy Variational Inference”
Ryan Martin, NC State University
Talk Title: “All Bayesian Inference is Approximate”
Yun Yang, University of Illinois Urbana-Champaign
Talk Title: “Mean-field Variational Inference via Wasserstein Gradient Flow”
Session 4 – Recent Advances in Complex Spatial Data
Organizer & Chair: Huiyan Sang
Abhirup Datta, John Hopkins University
Talk Title: “Bridging Machine Learning Methods and Gaussian Processes for Geospatial Analysis”
Guanyu Hu, University of Missouri
Talk Title: “Bayesian Nonparametric Learning for Spatial Point Process”
Qiwei Li, University of Texas at Dallas
Talk Title: “Bayesian Methods for Spatially Resolved Transcriptomics Data Analysis”
*4:15pm – 5:30pm
Poster Session
*6:30pm
Conference Dinner (Invitation Only)
Saturday October 22, 2022
*8:30am – 9:00am
Breakfast and Registration
*9:00am – 9:50am
Keynote Speaker: Ryan Tibshirani, Carnegie Mellon University
Talk Title: “An Overview of Conformal Prediction”
Chair: Ron Devore
*10:00am – 11:15am
Session 5 – Machine Learning and Biomedical Applications
Organizer: Brani Vidakovic
Chair: Scott Bruce
Debashree Ray, Johns Hopkins
Talk Title: “Using Meta-Analysis Summary Statistics to Identify Genetic Overlap between Diseases”
Peter Mueller, University of Texas, Austin
Talk Title: “Creating a Synthetic Control Cohort from RWD using a Bayesian Nonparametric Common Atoms Model”
Katja Ickstadt, University of Dortmund Germany
Talk Title: “Variable Selection Methods for High-Dimensional Data using Cross Leverage Scores”
Session 6 – Mathematical Data Science
Organizer & Chair: Simon Foucart
Gilad Lerman, University of Minnesota
Talk Title: “Fair and Robust Shape Estimation via Matrix-Valued Cubic-Regularized Newton”
Sui Tang, University of California, Santa Barbara
Talk Title: “Data-driven Discovery of Particle-Swarming Models with Gaussian Process”
Mark Iwen, Michigan State University
Talk Title: “Low-Distortion Embeddings of Submanifolds of R^n: Lower Bounds, Upper Bounds, and Terminal Embeddings”
*11:25am – 12:15pm
Keynote Speaker: Peter Hoff, Duke University
Talk Title: “Core Shrinkage Covariance Estimation for Matrix-variate Data”
Chair: Samiran Sinha
*12:15pm – 12:45pm
Lunch
*12:45pm – 2:00pm
Session 7 – Learning from Dynamical System
Organizer: Dileep Kalathil
Chair: Raj Guhaniyogi
Nilanjana Laha, Texas A&M University
Talk Title: “Optimal Dynamic Treatment Regimes via Smooth Surrogate Losses”
Aryan Mokhtari, University of Texas, Austin
Talk Title: “Representation Learning with Model Agnostic Meta-Learning”
Dileep Kalathil, Texas A&M University
Talk Title: “Robust Reinforcement Learning using Offline Data”
Session 8 – Data Science for Behavioral and Mental Health
Organizer: T. Chaspari
Chair: Sharmishtha Guha
Edison Thomaz, University of Texas at Austin
Talk Title: “Continual Learning for Behavior Monitoring with Prototypical Networks”
Ioannis Pavlidis, University of Houston
Talk Title: “Hidden micro-stressors: It’s the little things that count”
Adela Timmons, University of Texas at Austin
Talk Title: “Software Development and Machine Learning Models for Sensing and Fostering Child Mental Health”
*2:00pm – 2:30pm
Poster Distribution & Conference Wrap-up