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06/10/2026 | Press release | Distributed by Public on 06/10/2026 10:38

UC San Diego Professor Daniel Kane Awarded Gödel Prize, Highest Theoretical Computer Science Honor

Published Date

June 10, 2026

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University of California San Diego computer science and mathematics professor Daniel M. Kane has been awarded the 2026 Gödel Prize, one of the highest honors in theoretical computer science.

Kane is a professor in both the Department of Computer Science and Engineering in the UC San Diego Jacobs School of Engineering and the Department of Mathematics in the UC San Diego School of Physical Sciences.

Kane is one of six researchers who co-authored Robust Estimators in High Dimensions without the Computational Intractability, the work that is being honored. The paper was published in 2019 in the SIAM Journal on Computing.

The effort showed for the first time that a broad class of high-dimensional statistical problems can be solved both efficiently and robustly, even when part of the data has been arbitrarily corrupted. This work helped establish a new research area at the intersection of theoretical computer science, machine learning, and statistics.

In addition to Kane, the co-authors on the 2019 paper are Ilias Diakonikolas, Gautam Kamath, Jerry Li, Ankur Moitra, and Alistair Stewart. The research was first presented at the conference IEEE FOCS (Annual Symposium on Foundations of Computer Science) 2016.

The Gödel Prize is awarded annually by ACM SIGACT and the European Association for Theoretical Computer Science. It is widely regarded as theoretical computer science's most prestigious honor.

"This is an amazing accomplishment for Daniel," said Steven Swanson, chair of the Department of Computer Science and Engineering at the UC San Diego Jacobs School of Engineering. "His amazing capabilities are no secret in our department, and it's great to see him and his work receive this kind of public recognition."

In the award citation, the award committee noted that "the paper fundamentally changed our understanding of what is algorithmically possible in robust high-dimensional learning. It introduced tools and ideas that became central to a broad subsequent literature, and helped launch modern algorithmic high-dimensional robust statistics, with major impact across theoretical computer science, statistics, and machine learning."

More from the citation: "This paper resolves a longstanding problem in robust statistics: whether one can efficiently learn a high-dimensional distribution in the presence of adversarial corruptions without suffering dimension-dependent degradation in accuracy. In a breakthrough result, the authors give polynomial-time algorithms whose error guarantees are independent of the dimension. Before this work, known approaches were either computationally intractable in high dimensions (such as the Tukey median) or had error guarantees that degraded polynomially with the dimension.

At the core of the contribution is a striking and elegant principle: if corruptions significantly distort low-order empirical moments (such as the empirical mean), then they must also induce anomalously large spectral structure in higher-order empirical moments (such as the second moment), and this structure can be exploited algorithmically to detect and remove corrupted points."

Kane is the coauthor of the related book Algorithmic High-Dimensional Robust Statistics with Ilias Diakonikolas, who is also an awardee of the 2026 Gödel Prize.

Learn more about research and education at UC San Diego in: Artificial Intelligence

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