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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Journal ArticleDOI
Nonoccurring Behavior Analytics: A New Area
TL;DR: The concept, intrinsic characteristics, significant challenges, main issues, research directions, and state-of-the-art work related to NOBs are outlined, followed by the prospects for and applications of NOB analytics.
Journal ArticleDOI
Terrestrial Ecosystem Carbon Fluxes Predicted from MODIS Satellite Data and Large-Scale Disturbance Modeling
Christopher Potter,Steven Klooster,Vanessa Genovese,Cyrus Hiatt,Shyam Boriah,Vipin Kumar,Varun Mithal,Ashish Garg +7 more
TL;DR: The Carnegie-Ames-Stanford (CASA) ecosystem model was used to estimate monthly carbon fluxes in terrestrial ecosystems from 2000 to 2009 as discussed by the authors, which was driven by NASA Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation cover properties and large-scale (1-km resolution) disturbance events detected in biweekly time series data.
Journal ArticleDOI
Mapping Burned Areas in Tropical Forests Using a Novel Machine Learning Framework
Varun Mithal,Guruprasad Nayak,Ankush Khandelwal,Vipin Kumar,Ramakrishna R. Nemani,Nikunj C. Oza +5 more
TL;DR: An application of a novel machine-learning framework on MODIS (moderate-resolution imaging spectroradiometer) data to map burned areas over tropical forests of South America and South-east Asia and indicates that the two products can be complimentary and a combination of the two approaches is likely to provide a more comprehensive assessment of tropical fires.
Proceedings ArticleDOI
Efficient parallel algorithms for search problems: applications in VLSI CAD
S. Arvindam,Vipin Kumar,V.N. Rao +2 more
TL;DR: Experimental results are presented to demonstrate that it is possible to speed up search-based algorithms by several orders of magnitude.
Book ChapterDOI
Exploiting Spatial Autocorrelation to Efficiently Process Correlation-Based Similarity Queries
TL;DR: A spatial autocorrelation-based search tree structure is used to propose new processing strategies for correlation-based similarity range queries and similarity joins and a preliminary evaluation of the proposed strategies is provided using algebraic cost models and experimental studies with Earth science datasets.