John Paparrizos is an assistant professor of Computer Science and Engineering at The Ohio State University (OSU). He leads The DATUM Lab, the Data Analytics, Understanding, Mining, and Management Lab. John works on the foundations of the next generation of data-intensive and machine-learning applications. In particular, his research in databases, data science, machine learning, and artificial intelligence focuses on adaptive solutions for managing and analyzing structured and unstructured data, such as relational, time-series, multimedia, text, graphs, Web, and Internet-of-Things data.

For his research, John has received multiple awards and distinctions. Specifically, his doctoral work was recognized at the 2019 ACM SIGKDD Doctoral Dissertation Award competition, which honors internationally "outstanding work by graduate students in data science, machine learning, and data mining." While still a postdoc, he won the NetApp Faculty Award, which recognizes faculty of all levels "pursuing cutting‑edge research in data management." In addition, he won the inaugural ACM SIGMOD Research Highlight Award, which recognizes projects across premier database conferences that "exemplify core database research, address an important problem, and have the potential of significant impact." Recently, John received the 2023 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."

John's work has been featured in popular media outlets, including the Washington Post, Forbes, Guardian, Fortune, MIT Technology Review, and on the front page of the New York Times. His work has been adopted across scientific areas (e.g., computer science, social science, space science, engineering, econometrics, biology, neuroscience, and medicine), Fortune 500 enterprises (e.g., Exelon, Nokia, and many financial firms), and organizations such as the European Space Agency. In particular, John's open-source codes have been downloaded over 100k times and ported to established third-party libraries. In addition, his solutions have been taught at several universities, including Brown, Columbia, Purdue, and UChicago. John regularly serves on the organization and program committees of the top database systems and data analytics (e.g., ACM SIGMOD, VLDB, and IEEE ICDE), data mining (e.g., ACM SIGKDD), machine learning (e.g., ICML, NeurIPS, and ICLR), and artificial intelligence (e.g., AAAI and IJCAI) conferences. John is an ACM and AAAI Lifetime member.

John received his Ph.D. from Columbia University, under the guidance of Luis Gravano, his M.S. from Ecole Polytechnique Fédérale de Lausanne (EPFL), working with Karl Aberer, and his B.S. from Aristotle University of Thessaloniki (AUTH), under the supervision of Athena Vakali. Before joining OSU, he obtained postdoctoral training at the University of Chicago, working with Michael Franklin and Aaron Elmore. He held internships or visiting positions at Microsoft Research, Yahoo! Labs, Logitech, University of Illinois Urbana-Champaign (UIUC), and Université Paris Cité. John's studies were supported by multiple prestigious merit-based fellowships and scholarships, including from the Onassis Foundation.

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