Ilias Diakonikolas - Episode 76
From ACM ByteCast by Association for Computing Machinery (ACM)
October 22, 2025 · 38 min · Episode 76
About this episode
Ilias Diakonikolas discusses his research in robust statistics and machine learning, along with his journey and insights for future researchers.
In this episode of ACM ByteCast, Bruke Kifle hosts 2024 ACM Grace Murray Hopper Award recipient Ilias Diakonikolas, Professor at the University of Wisconsin, Madison, where he researches the algorithmic foundations of machine learning and statistics. Ilias received the prestigious award for developing the first efficient algorithms for high-dimensional statistical tasks that are also robust, meaning they perform well even when the data significantly deviates from ideal modelling assumptions. His other honors and recognitions include a Sloan Fellowship, the NSF CAREER Award, the best paper award at NeurIPS 2019, and the IBM Research Pat Goldberg Best Paper Award. He authored a textbook titled Algorithmic High-Dimensional Robust Statistics.In the interview, Ilias describes his early love of math as a student in Greece, which led him on a research journey in theoretical statistics and algorithms at Columbia University and, later, at UC Berkeley. He defines “robust statistics” and how it aids in detecting “data poisoning.” Ilias and Bruke explore statistical v. computational efficiency, the practical applications of this research in machine learning and trustworthy AI, and future…
People in this episode
Host: Bruke Kifle
Guest: Ilias Diakonikolas
Topics covered
- machine learning
- statistics
- algorithmic design
- robust statistics
- data poisoning
- trustworthy AI
Keywords
- algorithmic foundations
- high-dimensional statistics
- Sloan Fellowship
- NSF CAREER Award
- data efficiency
- AI applications
- research advice
Mentioned in this episode
Organizations: University of Wisconsin, ACM, Columbia University, UC Berkeley, IBM, NeurIPS
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