Vipin Kumar
University of Minnesota · Computer Science and Engineering
Active 1968–2026
Academic metrics are sourced from OpenAlex and public funding records; values may differ from Google Scholar.
About
Vipin Kumar is a Regents Professor and the William Norris Land Grant Chair in Large-Scale Computing at the University of Minnesota's Department of Computer Science & Engineering. He joined the department in 1989 and has been promoted to full professor, holding his current titles since 2005 and 2015 respectively. His research interests encompass data mining, high-performance computing, and their applications in climate/ecosystems and healthcare. Kumar's work has led to the development of the isoefficiency metric for evaluating the scalability of parallel algorithms, as well as highly efficient parallel algorithms and software for sparse matrix factorization and graph partitioning. His current major research focus is on leveraging big data and machine learning to understand the impact of human-induced changes on the Earth and its environment. Kumar's contributions to the field have been widely recognized, with over 127,000 citations, and he has received numerous awards including the IEEE Technical Achievement Award, the ACM SIGKDD Innovation Award, and the IEEE Fellow distinction. He is also the director of the Data Science Initiative at the university and has been actively involved in advancing climate modeling and environmental predictions through artificial intelligence and machine learning.
Research topics
- Computer Science
- Artificial Intelligence
- Machine Learning
- Climatology
- Geography
- Environmental science
- Data Mining
- Cartography
- Engineering
- Geology
Selected publications
Satellite-based remote sensing data set of global surface water storage change from 1992 to 2018
Earth system science data · 2020 · 91 citations
Abstract. The recent availability of freely and openly available satellite remote sensing products has enabled the implementation of global surface water monitoring at a level not previously possible. Here we present a global set of satellite-derived time series of surface water storage variations for lakes and reservoirs for a period that covers the satellite altimetry era. Our goals are to promote the use of satellite-derived products for the study of large inland water bodies and to set the s…
ACM Transactions on Interactive Intelligent Systems · 2021 · 80 citations
Major societal and environmental challenges involve complex systems that have diverse multi-scale interacting processes. Consider, for example, how droughts and water reserves affect crop production and how agriculture and industrial needs affect water quality and availability. Preventive measures, such as delaying planting dates and adopting new agricultural practices in response to changing weather patterns, can reduce the damage caused by natural processes. Understanding how these natural and…
Water Resources Research · 2022 · 53 citations
Senior authorCorrespondingAbstract Streamflow prediction is a long‐standing hydrologic problem. Development of models for streamflow prediction often requires incorporation of catchment physical descriptors to characterize the associated complex hydrological processes. Across different scales of catchments, these physical descriptors also allow models to extrapolate hydrologic information from one catchment to others, a process referred to as “regionalization”. Recently, in gauged basin scenarios, deep learning models ha…
Physics Guided Machine Learning Methods for Hydrology
arXiv (Cornell University) · 2020 · 29 citations
Senior authorCorrespondingStreamflow prediction is one of the key challenges in the field of hydrology due to the complex interplay between multiple non-linear physical mechanisms behind streamflow generation. While physics based models are rooted in rich understanding of the physical processes, a significant performance gap still remains which can be potentially addressed by leveraging the recent advances in machine learning. The goal of this work is to incorporate our understanding of hydrological processes and constra…
ExoTST: Exogenous-Aware Temporal Sequence Transformer for Time Series Prediction
2024-12-09 · 6 citations
articleAccurate long-term predictions are the foundations for many machine learning applications and decision-making processes. Traditional time series approaches for prediction often focus on either autoregressive modeling, which relies solely on past observations of the target “endogenous variables”, or forward modeling, which considers only current covariate drivers “exogenous variables”. However, effectively integrating past endogenous and past exogenous with current exogenous variables remains a s…
Recent grants
BIGDATA: F: Advancing Deep Learning to Monitor Global Change
NSF · $1.5M · 2018–2025
III-CTX: Collaborative Research: Spatio-Temporal Data Mining For Global Scale Eco-Climatic Data
NSF · $298k · 2007–2011
NSF · $664k · 2019–2023
Frequent coauthors
- 246 shared
Philip S. Yu
University of Illinois Chicago
- 244 shared
Rakesh Agrawal
Purdue University West Lafayette
- 243 shared
Rao Kotagiri
University of Melbourne
- 164 shared
Michael Steinbach
University of Minnesota System
- 120 shared
Xiaowei Jia
University of Pittsburgh
- 83 shared
Ankush Khandelwal
University of Minnesota System
- 82 shared
Xin Yao
- 81 shared
Xin Yao
Southern Medical University
Labs
Vipin KumarPI
Awards & honors
- IEEE Computer Society Taylor L. Booth Education Award (2025)
- AAAI Fellow (2023)
- ACM/IEEE Supercomputing Conference Test of Time Award (2021)
- Institute on the Environment Fellow (2016)
- Regents Professorship (2015)
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