Reetam Majumder
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Assistant Professor
Also affiliated: United States Geological Survey (2024); North Carolina State University (2022–2025); Northeast Climate Adaptation Science Center (2024); Southeast Climate Adaptation Science Center (2024–2025); University of Maryland, Baltimore County (2021–2022); University of Toledo (2017)
Research Areas
Biomedical Subjects
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Biography and Research Information
OverviewAI-generated summary
Reetam Majumder's research focuses on developing and applying advanced computational and statistical methods to address complex scientific and environmental challenges. His work includes the development of deep learning synthetic likelihood approximations for non-stationary spatial models to forecast extreme streamflow, and modeling extremal streamflow using deep learning approximations and flexible spatial processes. Majumder has also investigated decision models for wildfire management and climate change adaptation, as well as spatiotemporal optimization engines for prescribed burning in the Southeastern United States. His research portfolio also extends to financial modeling, including optimal stock portfolio selection using multivariate hidden Markov models and daily precipitation generation with hidden Markov models. Majumder has a publication record of 32 papers, with 117 citations, and an h-index of 4.
Metrics
- h-index: 4
- Publications: 28
- Citations: 126
Positions
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Assistant Professor 2024–presentUniversity of Arkansas Department of Mathematical Sciences ORCID
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Postdoctoral Fellow 2021–2024North Carolina State University Southeast Climate Adaptation Science Center ORCID
Selected Publications
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A New Mixture Model for Spatiotemporal Exceedances with Flexible Tail Dependence (2026)
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pySPQR: A Python Package for Density Estimation using Deep Learning (2026)Journal of the Arkansas Academy of Science OpenAlex
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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)
Collaboration Network
Top Collaborators
- A Complete Density Correction using Normalizing Flows (CDC-NF) for CMIP6 GCMs
- A New Mixture Model for Spatiotemporal Exceedances with Flexible Tail Dependence
- Semi-parametric bulk and tail regression using spline-based neural networks
- Semi-parametric bulk and tail regression using spline-based neural networks
- A Complete Density Correction using Normalizing Flows (CDC-NF) for CMIP6 GCMs
- A Complete Density Correction using Normalizing Flows (CDC-NF) for CMIP6 GCMs
- A Complete Density Correction using Normalizing Flows (CDC-NF) for CMIP6 GCMs
- pySPQR: A Python Package for Density Estimation using Deep Learning
- A New Mixture Model for Spatiotemporal Exceedances with Flexible Tail Dependence
- A New Mixture Model for Spatiotemporal Exceedances with Flexible Tail Dependence
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