Recent & Upcoming Talks

  • Multivariate Principal Component Analysis for Mixed-Type Functional Data with application to mHealth
    Joint Statistical Meetings (JSM), 2024
    Slides (PDF)

  • Covariance Estimation and Functional Principal Component Analysis for Mixed-Type Functional Data Joint Statistical Meetings (JSM), 2023 Slides (PDF)

  • Semiparametric Gaussian Copula Regression Modelling of Mixed Data Types (SGCRM)
    ENAR Spring Meeting, 2021
    Slides (PDF)

  • Using Mobile Technologies to Investigate Impaired Sleep, Mood, and Energy as Real-Time Triggers of Migraine International Conference on Ambulatory Monitoring of Physical Activity and Movement (ICAMPAM), 2021 Slides (PDF)

  • Modeling of Mixed-Type Intensive Longitudinal Data via Semiparametric Gaussian Copula
    Mobile Apps and Sensors in Surveys, 2021
    Slides (PDF)

  • Graphical Gaussian Process Models for Highly Multivariate Spatial Data
    Joint Statistical Meetings (JSM), 2021, Travel Award Winner Slides (PDF)

  • Connecting Population-level AUC and Latent R² via Semiparametric Gaussian Copula
    ENAR Spring Meeting, 2020 Slides (PDF)

  • Connecting Population-level AUC and Latent R² via Semiparametric Gaussian Copula and Rank Correlations
    Joint Statistical Meetings (JSM), 2019, Travel Award Winner Slides (PDF)

  • Joint Modeling of Binary and Continuous Measurements in Large Health Surveys and its Application to Network Analysis, Frailty, and Mortality in NHANES 1999–2010
    ENAR Spring Meeting, 2019
    Slides (PDF)

Debangan Dey is an incoming Assistant Professor in the Department of Statistics at Texas A&M University. He develops statistical and machine learning methods to analyze complex data streams arising from digital health technologies and environmental scinces. These data are often high-frequency, mixed, and multimodal and can be generalized as multivariate stochastic processes that evolve over both space and time.

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