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, Understanding, Mining, and Management Lab. He is also affiliated with the School of Informatics at Aristotle University of Thessaloniki (AUTh), Greece. His research focuses on the development of foundational technologies for data-intensive and machine learning applications. His work in databases, data science, machine learning, and artificial intelligence explores adaptive, effective, and scalable solutions for analyzing structured and unstructured data, including relational, time-series, multimedia, text, graph, web, and IoT data.

His research has been recognized with numerous prestigious awards and honors. His doctoral work was recognized at the 2019 ACM SIGKDD Doctoral Dissertation Award competition, which celebrates "outstanding work by graduate students in data science, machine learning, and data mining." During his postdoctoral training, he was awarded the NetApp Faculty Award, which honors researchers at all career stages who are "pursuing cutting‑edge research in data management." He also received the inaugural ACM SIGMOD Research Highlight Award, given to projects presented at premier database venues that "exemplify core database research, address an important problem, and have the potential of significant impact." In 2023, he was recognized with the IEEE TCDE Rising Star Award for "breakthroughs in time series data management, as well as contributions to adaptive methodologies for data intensive and machine learning applications." Most recently, John received the 2025 ACM SIGMOD Test-of-Time Award, which honors research papers that have had the greatest impact on the field—spanning research, products, and methodology—over the preceding decade. The award citation recognized his contributions to "advancing time-series clustering through a shape-based approach, achieving a distinctive synthesis of high accuracy, computational efficiency, and broad-domain applicability."

John's work has been featured in major international media outlets, including the New York Times (front page), Washington Post, Forbes, Guardian, Fortune, and MIT Technology Review. His methods and systems have been adopted across multiple scientific disciplines—including computer science, biology, medicine, social science, and neuroscience—as well as by Fortune 500 companies such as Exelon and Nokia, and by organizations like the European Space Agency. His open-source tools have been downloaded over 100,000 times and are integrated into widely-used third-party libraries. His techniques have also been incorporated into curricula at universities such as Brown, Columbia, Purdue, and the University of Chicago.

John actively serves on the program and organizing committees of leading conferences in data management (e.g., ACM SIGMOD, VLDB, and IEEE ICDE), data mining (ACM SIGKDD and IEEE ICDM), machine learning (ICML, NeurIPS, and ICLR), and artificial intelligence (AAAI and IJCAI). He is a Lifetime Member of both the ACM and AAAI.

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 (UIUC), and Université Paris Cité. His academic path has been supported by several prestigious fellowships and scholarships, including one from the Onassis Foundation.

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