John Paparrizos :: Homepage

About

John Paparrizos is an assistant professor of Computer Science and Engineering at The Ohio State University (OSU), where he leads The DATUM Lab—the Data Analytics, Techniques, Understanding, and Mining Lab. He is also affiliated with the School of Informatics at Aristotle University of Thessaloniki (AUTh), Greece. His research develops foundational methods at the intersection of data management and artificial intelligence. Advancing the next generation of data-intensive and AI applications requires rethinking every layer—from how data is queried and indexed, to the automation of analytics, and the rigorous evaluation of progress—to deliver solutions that are more adaptive, accurate, scalable, and trustworthy.

His research has been recognized with several prestigious honors, including the ACM SIGMOD Research Highlight Award, "Best of SIGMOD" and "Best of VLDB" citations, recognition at the 2019 ACM SIGKDD Doctoral Dissertation Award competition, and the NetApp Faculty Award. Recently, he received the 2023 IEEE TCDE Rising Star Award, recognizing early-career researchers in data engineering, the 2025 ACM SIGMOD Test-of-Time Award, honoring works with the greatest impact on the field, and the 2026 NSF CAREER Award, the NSF's most prestigious recognition for early-career faculty.

John's work has been featured in major international outlets, including the New York Times, Washington Post, Forbes, Guardian, Fortune, and MIT Technology Review. His methods and systems have been adopted across computer science, biology, medicine, social science, and neuroscience, as well as by Fortune 500 companies and organizations such as the European Space Agency. His open-source tools have been downloaded over 300,000 times, are integrated into widely-used third-party libraries, and his techniques have appeared in curricula at Brown, Purdue, UChicago, Columbia, OSU, and AUTh.

John actively serves on the program and organizing committees of leading venues in data management (e.g., ACM SIGMOD, VLDB, and IEEE ICDE), data mining (e.g., ACM SIGKDD and IEEE ICDM), machine learning (e.g., ICML, ICLR, and NeurIPS), and artificial intelligence (e.g., AAAI and IJCAI). He has served as an Associate Editor at the Data Mining and Knowledge Discovery journal, Area Chair at SIGMOD, SIGKDD, ICLR, and NeurIPS, and has received Distinguished Reviewer Awards from SIGMOD, VLDB, and SIGKDD. He is a Lifetime Member of ACM and AAAI, and a member of IEEE and ELLIS.

John earned his Ph.D. from Columbia University under the supervision of Luis Gravano. He holds an M.S. from École Polytechnique Fédérale de Lausanne (EPFL), where he worked with Karl Aberer, and a B.S. from Aristotle University of Thessaloniki under the mentorship of Athena Vakali. His postdoctoral research was conducted at the University of Chicago, collaborating with Michael Franklin and Aaron Elmore. He has held appointments at Microsoft Research, Yahoo! Labs, Logitech, University of Illinois Urbana-Champaign, and Université Paris Cité.