Scholar
Ilias Diakonikolas
Google Scholar ID: Vb3FLmkAAAAJ
University of Wisconsin-Madison
Citations & Impact
Citations
6,513
H-index
41
i10-index
123
Publications
20
Co-authors
0
Contact
Publications
Resume (English only)
Academic Achievements
- Recognized with the ACM Grace Murray Hopper Award, a Sloan Fellowship, an NSF CAREER Award, a Marie Curie Fellowship, a Google Faculty Research Award, best paper awards at NeurIPS and COLT, and the IBM Research Pat Goldberg Best Paper Award. His research has been supported by the NSF, DARPA, ONR, EPSRC, WARF, Google, and the European Commission. Co-authored 'Algorithmic High-Dimensional Robust Statistics' with Daniel Kane, published by Cambridge University Press.
Research Experience
- Currently, the Sheldon B. Lubar professor in the CS department at UW Madison, a member of the theory of computing group, machine learning@uw-madison, and the Institute for Foundations of Data Science. Also affiliated with the Statistics department, Wisconsin Institute for Discovery, and the Data Science Institute. Prior to joining UW, he was Andrew and Erna Viterbi Early Career Chair in Computer Science at USC, and a faculty member at the University of Edinburgh. Before that, spent two years at UC Berkeley as the Simons Postdoctoral Fellow in Theoretical Computer Science.
Education
- Ph.D. in Computer Science from Columbia University, advised by Mihalis Yannakakis; undergraduate studies at the National Technical University of Athens, Greece.
Background
- Research interests are in algorithms and machine learning. A major goal of his work is to understand the tradeoff between statistical efficiency, computational efficiency, and robustness for fundamental problems in statistics and machine learning. Areas of current focus include high-dimensional robust statistics, information-computation tradeoffs, foundations of deep learning, nonparametric estimation, distribution testing, and data-driven algorithm design. Also has strong interests in applied probability, algorithmic game theory, and their connections to machine learning.
Co-authors