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Kumar's current research interests span data mining, high-performance computing, and their applications in Climate/Ecosystems and health care. His research has resulted in the development of the concept of isoefficiency metric for evaluating the scalability of parallel algorithms, as well as highly efficient parallel algorithms and software for sparse matrix factorization (PSPASES) and graph partitioning (METIS, ParMetis, hMetis). He has authored over 300 research articles, and has coedited or coauthored 10 books including two text books "Introduction to Parallel Computing"and "Introduction to Data Mining", that are used world-wide and have been translated into many languages. Kumar's current major research focus is on bringing the power of big data and machine learning to understand the impact of human induced changes on the Earth and its environment.
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International Journal of Agriculture Extension and Social Developmentno. 3 (2024): 437-441
Proceedings of the 2024 SIAM International Conference on Data Mining (SDM)pp.334-342, (2024)
Nature Communicationsno. 1 (2024): 357-357
Critical Reviews in Oncogenesisno. 1 (2024): ix-x
AERONAUTICAL JOURNAL (2024)
ACM COMPUTING SURVEYSno. 4 (2023): 1-37
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