Scholar
Suprateek Kundu
Google Scholar ID: 0t18nFwAAAAJ
Associate Professor, Department of Biostatistics, The University of Texas at MD Anderson Cancer
Bayesian analysis
Biostatistics
Data Science
Deep Learning
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Citations & Impact
All-time
Citations
689
H-index
16
i10-index
24
Publications
20
Co-authors
0
Contact
Email
suprateek.kundu@emory.edu
Publications
5 items
Bayesian Tensor-on-Tensor Varying Coefficient Model for Forecasting Alzheimer's Disease Progression
2026
Cited
0
Spatially-informed Image Harmonization Results in Improved Scanner Effect Removal and Prediction
2026
Cited
0
Bayesian Scalar-on-Tensor Quantile Regression for Longitudinal Data on Alzheimer's Disease
2026
Cited
0
Risk Prediction in Cancer Imaging Using Enriched Radiomics Features
2026
Cited
0
DeepJIVE: Learning Joint and Individual Variation Explained from Multimodal Data Using Deep Learning
2025
Cited
0
Resume (English only)
Research Experience
Associate Professor (tenured), Department of Biostatistics, The University of Texas at MD Anderson Cancer Center (2021–present)
Director, Data Analytics and Biostatistics (DAB) Core, Department of Medicine, Emory University (June 2019–present)
Assistant Professor, Department of Biostatistics, Emory University (2014–present)
Postdoctoral Research Associate, Department of Statistics, Texas A&M University & Department of Biostatistics, MD Anderson (Sep 2012–Jun 2014)
Research Assistant, Translational and Clinical Sciences Institute, UNC Chapel Hill (2008–2012)
Background
Associate Professor (tenured) in the Department of Biostatistics at The University of Texas at MD Anderson Cancer Center
Research focuses on practical statistical methods and theory for high-dimensional biomedical applications in epidemiology, neuroimaging, and -omics
Aims to integrate multi-scale and multi-modal data using prior biological or expert knowledge
Works on non- and semi-parametric Bayesian methods, density estimation, high-dimensional variable selection, graphical models, and deep learning
Operates at the intersection of statistics, machine learning, and computer science to build scalable solutions for complex multi-source data
Co-authors
0 total
Co-authors: 0 (list not available)
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