Dr. Karypis is a Distinguished McKnight University Professor in the Department of Computer Science & Engineering at the University of Minnesota, Minneapolis. He is known worldwide for his seminal contributions in the areas of data mining, recommender systems, and high-performance computing. He has (co-)authored a large number of highly cited papers in these areas on topics related to clustering, graph mining, pattern discovery, collaborative filtering, and graph partitioning. He has received many awards including the "IEEE ICDM 10-Year Highest-Impact Paper Award" r his work that developed computationally efficient algorithms to mine large graph databases and the International World Wide Web Conference's "Seoul Test of Time Award" for his work that pioneered an entirely new way of building recommender systems that exploit relations between items.
Dr. Karypis has been successful in bringing his innovative research ideas into practice via a wide-range of high-quality software packages in the areas of data mining, high-performance computing, circuit design, chemical informatics, recommender systems, and scientific computing. For example, he developed CLUTO, a comprehensive clustering toolkit that is known to virtually any data mining researcher worldwide, and the METIS family of serial and parallel graph and hypergraph partitioning algorithms that is used extensively in high-performance computing, VLSI circuit design, data mining, and social network analysis. His software has been incorporated into well over 200 different commercial software systems used by millions of people worldwide and in several hundred academic and government codes.
2017 IEEE ICDM Nomination and Evaluation Committees