V
Vipin Kumar
Researcher at University of Minnesota
Publications - 678
Citations - 67181
Vipin Kumar is an academic researcher from University of Minnesota. The author has contributed to research in topics: Parallel algorithm & Computer science. The author has an hindex of 95, co-authored 614 publications receiving 59034 citations. Previous affiliations of Vipin Kumar include University of Maryland, College Park & United States Department of the Army.
Papers
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Proceedings ArticleDOI
Gopher: Global observation of Planetary Health and Ecosystem Resources
Ashish Garg,Varun Mithal,Yashu Chamber,Ivan Brugere,Vijay Chaudhari,Marc T. Dunham,Vikrant Krishna,Sairam Krishnamurthy,Sruthi Reddy Vangala,Shyam Boriah,Michael Steinbach,Vipin Kumar,Albert Cho,J.D. Stanley,Teji Abraham,Juan Carlos Castilla-Rubio,Christopher Potter,Steven Klooster +17 more
TL;DR: Land cover change, especially deforestation, is a priority issue for policymakers at the local, national and international scale and policymakers at the UN Framework Convention on Climate Change negotiations are addressing land use change by developing a framework for Reducing Emissions from Deforestation and Degradation (REDD).
Proceedings Article
Integrative Biomarker Discovery for Breast Cancer Metastasis from Gene Expression and Protein Interaction Data Using Error-tolerant Pattern Mining.
TL;DR: A novel error-tolerant pattern mining approach for integrated analysis of gene expression and protein interaction data and efficiently discovers patterns in a bottomup fashion from the gene-expression data that are more biologically plausible and genes discovered are more reproducible across studies.
Posted Content
Physics-Guided Recurrent Graph Networks for Predicting Flow and Temperature in River Networks
Xiaowei Jia,Jacob A. Zwart,Jeffrey M. Sadler,Alison P. Appling,Samantha K. Oliver,Steven L. Markstrom,Jared Willard,Shaoming Xu,Michael Steinbach,Jordan S. Read,Vipin Kumar +10 more
TL;DR: A physics-guided machine learning approach that combines advanced machine learning models and physics-based models to improve the prediction of water flow and temperature in river networks is proposed and shown to produce better performance when generalized to different seasons or river segments with different streamflow ranges.
Book ChapterDOI
PSPASES: Building a High Performance Scalable Parallel Direct Solver for Sparse Linear Systems
TL;DR: PSASES is described, one of the first efficient, portable, and robust scalable parallel solvers for sparse symmetric positive definite linear systems that has developed and could solve the largest sparse system ever solved by a direct method.
Proceedings ArticleDOI
A new clustering algorithm for transaction data via caucus
TL;DR: Experiments indicate that compare to prior work, caucus-based method can derive clusters of better quality as well as reduce the execution time considerably.