George Karypis received 2025 ACM SIGKDD Innovation Award

Department of Computer Science & Engineering (CS&E) Distinguished McKnight University Professor George Karypis has received the 2025 Association of Computing Machinery Special Interest Group on Knowledge Discovery and Data Mining (ACM SIGKDD) Innovation Award. The award recognizes one individual or one group of collaborators whose outstanding technical innovations have had a lasting impact in advancing the theory and practice of the field, and was conferred on the opening day of the 2025 SIGKDD conference in Toronto. 

“At the end of the day, an award like this is an award for the entire research group rather than an individual,” Karypis said. “As faculty, we wouldn’t be where we are without our students that we are fortunate to work with over the years and our other colleagues. It is a great accomplishment and I am really thankful to all of the people involved who helped me get here.”

Karypis was honored for his pioneering work in graph partitioning, recommendation systems, and scalable algorithms for data mining and machine learning - work that is both foundational and has had tremendous real-world impact. He has been involved in the KDD community since the conference began and has held a number of leadership positions, including the PC chair for the technical program in 2019. 

“I have had the pleasure of working with George as a faculty colleague for the past 30 years, and it has been especially rewarding to witness his remarkable and rapid transition from a PhD student to a towering figure in data mining and high-performance computing,” said Vipin Kumar, a C&SE Regents Professor and KDD Innovation Award winner (2012). “His impact is reflected not only in his influential publications—many of which have received Test-of-Time awards from top tier venues, but also in his widely used software tools that are extensively used in a wide range of applications and have brought huge visibility to our department and university.”

Karypis joined the faculty of the Department of Computer Science & Engineering in 1999. He is the co-author of two books and has contributed to the development of multiple software libraries. Additionally, he is an IEEE Fellow and served on the program committees of many conferences and workshops, and on the editorial boards of the IEEE Transactions on Big Data, ACM Transactions on Knowledge Discovery from Data, Data Mining and Knowledge Discovery, Journal of Data Science and Analytics, Social Network Analysis and Data Mining Journal, International Journal of Data Mining and Bioinformatics, the journal on Current Proteomics, Advances in Bioinformatics, and Biomedicine and Biotechnology. 

“There are two areas that I spend a lot of my time on,” Karypis said. “One is building foundational models for some of the less studied domains and data types, like time series prediction, structured data, and material design. The second major research focus is in machine learning for systems and systems for machine learning. The idea behind these approaches is to optimize computer systems for machine learning workloads, especially for generative AI, as well as leveraging machine learning to optimize the original system. One project I am working on is building generative AI approaches to optimize the codebases used to build generative AI models, so that in turn it can help build better generative AI models.”

Karypis and Kumar are two of the many renowned faculty at the University of Minnesota in the field of data mining. In particular, interdisciplinary work at UMN situated at the interface between large-scale data mining and physical science has led the way for the knowledge-guide machine learning paradigm that was pioneered in CS&E.

“Vipin Kumar was actually my PhD advisor and we continue to work together,” Karypis said. “I also have worked with Joseph Konstan and the late John Riedl in GroupLens, as well as Jaideep Srivastava. In addition to over 35 PhD students, 20 masters students, and several post-docs, I have been fortunate to work with a number of collaborators outside of the computer science department. I have spent a lot of time working on computational biology and informatics, and Professor Wei-Shou Hu from Chemical Engineering and Material Science has been a great mentor and colleague to me. He retired this year. Nikolaos Sidiropoulos was also an influential collaborator from Electrical Engineering. He now works at the University of Virginia. I recently have worked with Ellad Tadmor from Aerospace Engineering and Mechanics. But the thing I am most grateful for is my students.”

Learn more about this work at the Karypis Lab website.