Krzysztof Onak

Krzysztof Onak is the Shibulal Family Career Development Assistant Professor in the Faculty of Computing & Data Sciences at Boston University. He is actively engaged in research and teaching, with a focus on theoretical foundations of algorithms for big data and their applications in AI and machine learning.

• PhD, Massachusetts Institute of Technology (2010)
• Researcher, IBM T.J. Watson Research Center
• Simons Postdoctoral Fellow, Carnegie Mellon University

His research interests include theoretical computer science, streaming algorithms, sublinear-time algorithms, and parallel and distributed computing models. He works on algorithmic techniques for taming big data, such as sampling, sketching, and dimensionality reduction, with applications in machine learning and artificial intelligence. His work bridges theory and practice, addressing challenges in modern data processing frameworks like MapReduce.

The most recent publications highlight a strong focus on efficient graph algorithms in distributed and streaming settings, dynamic data structures, and fairness in machine learning. His work frequently appears in top-tier venues such as STOC, FOCS, SODA, and ICML, demonstrating both theoretical depth and practical relevance.

Scientific awards and recognitions include:

• ACM ICPC World Champion
• Gold Medalist, International Olympiad in Informatics

Krzysztof Onak advises several PhD students and postdoctoral researchers, including Esty Kelman, Dragos Ristache, Themistoklis Haris, and Zi Song Yeoh. He has secured research funding through academic appointments and fellowships, and he is actively involved in the theoretical computer science community as a program committee member and workshop organizer. He has taught courses such as Algorithmic Techniques for Taming Big Data and Algorithms for Data Science.

He is involved in organizing key academic events, including the Workshop on Emerging Models of Colossal Computation (E=mc²), the Workshop on Local Algorithms (WOLA 2022), and the Simons Semesters on Algorithms for the Massive Parallel Computation Model. He also contributes to the community through SUBLINEAR.INFO, a curated list of open problems in sublinear algorithms.