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. Kumar is internationally recognized for his pioneering work in AI/machine learning, high-performance computing (HPC), and their applications to addressing some of most pressing environmental challenges facing the humanity.
Kumar’s early work on accelerating AI problem-solving search led to the development of isoefficiency analysis, a groundbreaking framework for scaling up parallel algorithms. This work has profoundly impacted the design of practical parallel algorithms, with applications across fields such as molecular dynamics, fluid dynamics, structural mechanics, image processing, genetic programming, task scheduling, and training of large-scale neural networks. His group’s work on graph partitioning led to a series of software such as METIS, ParMETIS, hMETIS that are heavily used in the HPC community for executing computational simulation on large-scale parallel computers, and also in areas as diverse as computer circuit design, customer segmentation, social network analysis, storing large geographic data sets for efficient access. Novel clustering, association analysis, and anomaly detection developed by his group are amongst the most highly cited papers on these topics and are at the core of some of the most widely used software for analyzing large data sets.
Kumar’s research over the past two decades has been focused on advancing machine learning to help address some of the biggest challenges facing the humanity in the areas of climate change and food/water/energy security. In particular, his team’s work on identifying patterns and changes in the massive amounts of data being collected using Earth observing satellites dramatically advanced the state of the art in the monitoring of global forest cover, surface water bodies, and other land cover changes.
Kumar’s most recent major contribution is the creation of a brand-new field of research at the intersection of AI and Science termed knowledge-guided machine learning (KGML), where scientific knowledge is deeply integrated in the design and training of machine learning models to accelerate scientific discovery. Today, KGML is a rapidly growing field of research with hundreds of papers published annually across scientific disciplines. Techniques developed by Kumar’s group have greatly improved predictive models in areas such as aquatic sciences, hydrology, and agriculture.
Kumar has co-authored over 400 research articles, and co-edited or coauthored 11 books including two widely used textbooks “Introduction to Parallel Computing”, “Introduction to Data Mining”, and a recent edited collection, “Knowledge Guided Machine Learning”. His foundational research in data mining and high performance computing has been honored by the ACM SIGKDD 2012 Innovation Award, which is the highest award for technical excellence in the field of Knowledge Discovery and Data Mining (KDD), the 2016 IEEE Computer Society Sidney Fernbach Award, one of IEEE Computer Society’s highest awards in high performance computing, Test-of-time award from 2021 Supercomputing conference (SC21), and 2005 IEEE Computer Society’s Edward J. McCluskey Technical Achievement Award for contributions to the design and analysis of parallel algorithms, graph partitioning, and data mining.
