AI at an Inflection Point: Catalyzing a New Era of Discovery

#artificial-intelligence

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Artificial intelligence is entering a transformative new phase that is driven by the convergence of massive data availability, breakthroughs in machine learning, and the exponential rise of computational power. In this talk, Prof Vipin Kumar explores how these forces have brought us to a historic inflection point, where AI is no longer just a tool for analysis, but a catalyst for accelerating discovery and reimagining entire fields. We will trace the arc from early data-driven models to today’s generative AI systems and examine emerging paradigms such as Knowledge-Guided Machine Learning (KGML) that integrate scientific principles with data-driven learning. These approaches offer a path toward more accurate and generalizable predictive models that are beginning to have  transformative impact across science and engineering.

Biography

Vipin Kumar is a Regents Professor at the University of Minnesota, where he holds the William Norris Endowed Chair in the Department of Computer Science and Engineering. He served as the Head of the Department from 2005 to 2015 and as Director of the Army High Performance Computing Research Center (AHPCRC) from 1998 to 2005.

He is also widely known for his foundational contributions to data mining and parallel computing, including co-authoring some of the most widely used textbooks in these areas, such as Introduction to Data Mining and Introduction to Parallel Computing. These textbooks have been adopted globally and have played a major role in shaping computer science education over the past two decades.  Kumar has played a pioneering role in bringing together the data science and Earth science communities to address one of the most pressing societal challenges of our time: understanding the impact of human-induced changes on the Earth system. A major focus of his recent work is on Knowledge-Guided Machine Learning (KGML)—an emerging area that combines scientific knowledge with data-driven methods to build more accurate and generalizable AI models even under data sparse scenarios. His group has applied KGML to a wide range of scientific challenges, including modeling freshwater systems, hydrological modeling, and agricultural emissions.

Kumar’s  wide ranging contributions in artificial intelligence and high-performance computing have been recognized by numerous professional honors including 2012 ACM SIGKDD Innovation Award (the highest award for technical excellence in the field of Knowledge Discovery and Data Mining), the 2016 IEEE Computer Society Sidney Fernbach Award, one of IEEE Computer Society's highest awards in high-performance computing,   2020  Test of Time award from Supercomputing (the largest conference of the HPC community) for his work on graph partitioning, and the 2025 IEEE Taylor L. Booth Education Award, which recognizes his outstanding contributions to computing education and mentorship. He has been elected as a Fellow of  the Association for Advancement of Artificial Intelligence (AAAI), the American Association for Advancement for Science (AAAS), Association for Computing Machinery (ACM),  Institute of Electrical and Electronics Engineers (IEEE), and Society of Industrial and Applied Math (SIAM).