S3: Speed Session 3 -- WSDS 2025

01. A Bayesian likely responder approach for the analysis of randomized controlled trials

11:45 AM - 1:15 PM

Co-authors: Annan Deng (New York University School of Medicine), Annan Deng (New York University School of Medicine)

Causal inference Precision medicine Treatment effect heterogeneity Data-driven subgroup analysis Uncertainty quantification

02. Bayesian Tree Model for Binary and Categorical Data under Informative Sampling

11:45 AM - 1:15 PM

Co-authors: Diya Bhaduri (University of Missouri-Columbia), Diya Bhaduri (University of Missouri-Columbia), Scott Holan (University of Missouri/U.S. Census Bureau), Daniell Toth (US Bureau of Labor Statistics)

Bayesian nonparametric CART models Informative sample design Categorical responses

03. Multi-state time-to-event modeling of Huntington Disease stage progression

11:45 AM - 1:15 PM

Co-authors: Jesus Vazquez, Madhuri Raman, Madhuri Raman, Sophia Cross (University of North Carolina at Chapel Hill), Yajie He, Aditya Krishnan (University of North Carolina at Chapel Hill), Dewei Lin (University of North Carolina at Chapel Hill), Sarah Lotspeich (Wake Forest University), Tanya Garcia (University of North Carolina at Chapel Hill)

multi-state modeling neurodegenerative disease censoring survival analysis accelerated failure-time model disease progression

Withdrawn - 04. Using Generative AI to Simulate Realistic Millimeter-Wave Channel Measurements

11:45 AM - 1:15 PM

Co-authors: Lucas Koepke (National Institute of Standards and Technology), Lucas Koepke (National Institute of Standards and Technology)

generative AI synthetic data wireless communication millimeter-wave

05. Overcoming Censoring in Predicting Huntington Disease Progression: a Comparative Modeling Study

11:45 AM - 1:15 PM

Co-authors: Abigail Foes, Kyle Grosser (University of North Carolina), Abigail Foes, Stellen Li (University of North Carolina at Chapel Hill), Vraj Parikh (University of North Carolina at Chapel Hill), Tanya Garcia (University of North Carolina at Chapel Hill), Sarah Lotspeich (Wake Forest University)

Huntington disease censored covariates survival predictive model

Withdrawn - 06. Modeling Longitudinal Microbiome Data with Application to a Melanoma Clinical Trial Evaluating Immune Checkpoint Inhibitor Treatment Response (NCT05102773)

11:45 AM - 1:15 PM

Co-authors: Caroline Dravillas (The Ohio State University Wexner Medical Center), Nyelia Williams (The Ohio State University Wexner Medical Center), Caroline Dravillas (The Ohio State University Wexner Medical Center), Marium Husain (Division of Medical Oncology, Department of Internal Medicine, The Ohio State University Comprehensi), Rebecca Hoyd (The Ohio State University Wexner Medical Center), Kari Kendra (Division of Medical Oncology, Department of Internal Medicine, The Ohio State University Comprehensi), Christin E. Burd (Department of Molecular Genetics, The Ohio State University), Daniel Spakowicz (The Ohio State University Wexner Medical Center)

microbiome cancer longitudinal zero-inflated models correlation structures mixed-effects

07. A Fast Scalable Spatiotemporal Modeling of task-fMRI Data

11:45 AM - 1:15 PM

Co-authors: Jungin Choi (Johns Hopkins University), Martin Lindquist (Johns Hopkins University), Jungin Choi (Johns Hopkins University), Abhirup Datta

Adaptive Spatial Modeling High-dimensional Inference Hemodynamic Response Function Task-based fMRI BOLD Signal

08. Performance comparisons of power penalized regression against forward stepwise, lasso, and relaxed lasso

11:45 AM - 1:15 PM

Co-authors: Ning Duan (University of Massachusetts Amherst), Ning Duan (University of Massachusetts Amherst), Maryclare Griffin (University of Massachusetts Amherst), QIAN ZHAO (University of Massachusetts)

lasso penalized regression variable selection

09. Independent Samples T-Test vs. Mann-Whitney U Test: Strengths & Weaknesses

11:45 AM - 1:15 PM

Co-authors: Abigail Leonhard, Abigail Leonhard

Mann-Whitney U test independent samples t-test biostatistics interpretability power

10. Building Trust in AI: The Crucial Role of Human Centered Machine Learning in Safety Critical Domains

11:45 AM - 1:15 PM

Co-authors: Akshata Moharir, Akshata Moharir

Explainable AI Interpretable Machine Learning Safety Critical Domain Explainable AI

11. Integrating Eye-Tracking into Education: A Case Study in Student Presentations

11:45 AM - 1:15 PM

Co-authors: Samyukta Vakkalanka (Georgetown University), Samyukta Vakkalanka (Georgetown University)