Kumar's research spans 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). 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. Kumar served as the Lead PI of a 5-year, $10 Million project, "Understanding Climate Change - A Data Driven Approach", funded by the NSF's Expeditions in Computing program that is aimed at pushing the boundaries of computer science research.
Research areas
Bioinformatics and Computational Biology
Data Mining, Databases, and Geographical Information Systems
High Performance Computing
Theoretical Foundations
