Reetam Majumder Data-verified
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Assistant Professor
faculty
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Biography and Research Information
OverviewAI-generated summary
Reetam Majumder's research focuses on applying computational and statistical modeling techniques to address complex environmental and financial challenges. His work includes developing deep learning synthetic likelihood approximations for non-stationary spatial models to forecast extreme streamflow, and modeling extremal streamflow using flexible spatial processes. Majumder has also investigated wildfire management, developing a fire-use decision model to support climate change adaptation and a spatiotemporal optimization engine for prescribed burning in the Southeast US. Additionally, his research extends to financial applications, including optimal stock portfolio selection using multivariate hidden Markov models and daily precipitation generation for river basins with correlated emissions. Majumder has published 28 works, accumulating 107 citations and an h-index of 4.
Metrics
- h-index: 4
- Publications: 31
- Citations: 112
Selected Publications
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Semi-parametric bulk and tail regression using spline-based neural networks (2026)
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Semi-parametric bulk and tail regression using spline-based neural networks (2026)
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A Complete Density Correction using Normalizing Flows (CDC-NF) for CMIP6 GCMs (2025)
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