About me
Debangan Dey is an Assistant Professor in the Department of Statistics at Texas A&M University. His research focuses on building theory and methods for multivariate stochastic processes as a unifying AI framework to analyze intensive, multilevel, multimodal, longitudinal data collected across space and time. This type of data arises in studies employing Digital Health Technologies, such as smartphone apps and smartwatches, with contextual spatial information like weather, light, and greenspace etc. through location tracking. His work aims to uncover how mental health, sleep, physical activity, and the environment interact and evolve over time.
🚨 Looking for motivated students to work on statistical and machine learning methods for analyzing data from wearables ⌚️, smartphones 📱, with contextual location information📍!
⚽ 2026: New preprint — Do In-Match Hydration Breaks Alter Match Momentum? A Within-Match Case-Crossover Analysis of the 2026 FIFA World Cup. arXiv · GitHub.
🏆 2026: Awarded the NIH Director’s Challenge Innovation Award — Biologic Rhythms and Environmental Contexts in Human Health: Integrating Epidemiology and Circadian Science (PI: K. Merikangas, NIMH). Role: Biostatistician Consultant, leading data management and analytic strategies for multilevel circadian and metabolic data integration.
⚽ 2026: Built a FIFA World Cup 2026 Bracket Predictor with Claude — Monte Carlo bracket odds, live ticket & hotel prices, and a trip planner that ranks your 3 cheapest venues (with day-trip detection and Google Flights links) so you can finally settle the “go or not go” debate.
💻 2026: Released M²FPCA — an R package for Multivariate Functional Principal Component Analysis of mixed-type functional data (continuous, truncated, ordinal, and binary) via a latent Gaussian copula. Companion to SGCTools.
📰 2025: Published Associations between daily outdoor temperature and subjective real-time ratings of emotional states and sleep in mood disorder subtypes in Journal of Affective Disorders — Featured by Texas A&M: Warmer Days, Better Moods? It’s Complicated.
📄 2026: New preprint — Doubly-Unlinked Regression for Dependent Data with A. Burman and S. Choudhury.
📄 2026: New preprint — Multivariate Functional Principal Component Analysis for Mixed-Type mHealth Data: An Application to Mood Disorders with R. Ghosal, K. Merikangas, and V. Zipunnikov.
📄 2026: Major revision of preprint — Regression and Dimension Reduction for Multivariate Mixed-Type Data via Semiparametric Gaussian Copula with V. Zipunnikov.
📘 2026: Will be teaching STAT 632: Statistical Methodology II-Bayesian Modeling and Inference in Spring 2026.
🎤 2026: Presented Digital Phenotyping for Mixed-Type mHealth Data in the invited session Data-driven Advances in Mental Health Statistics: Novel Methods for mHealth, Neuroimaging, and Causal Discovery at IBC 2026, Seoul, Korea.
📄 2025: Published Graph-constrained analysis for multivariate functional data in Journal of Multivariate Analysis with S. Banerjee, M. A. Lindquist, and A. Datta.
📰 2024: Published Association Between Electronic Diary–Rated Sleep, Mood, Energy, and Stress With Incident Headache in a Community-Based Sample in Neurology with T. Lateef, A. Leroux, L. Cui, M. Xiao, V. Zipunnikov, and K. Merikangas. → Featured by: CNN, National Geographic