SHORT BIO

Alexandros Dimakis is a UT Austin Professor and the co-director of the National AI Institute on the Foundations of Machine Learning. He received his Ph.D. from UC Berkeley and the Diploma degree from NTU in Athens, Greece. He has published more than 150 papers and received several awards including the James Massey Award, NSF Career, a Google research award, the UC Berkeley Eli Jury dissertation award, and several best paper awards. He served as an Associate Editor for several journals, as an Area Chair for major Machine Learning conferences (NeurIPS, ICML, AAAI) and as the chair of the Technical Committee for MLSys 2021. He is an IEEE Fellow for contributions to distributed coding and learning. His research interests include Information Theory and Machine Learning.

RESEARCH AREAS

Learning-oriented Approaches in Optimization, Fair Division and Mechanism Design

Uncovering the true value of data

FairGraph: Mitigating and Explaining Unfairness in Graphs

Archimedes joins forces with the Growthfund for the digitization of its services!

Studying Multilingual Neural Models with Symbolic Approaches

Data Driven Approach to Approximation, Counting and Mechanism Design (D2ALGO)

LEAP: A Deep-Learning Framework Towards an Efficient and Adaptive AI Engine for Visual Perception

Game Theory/ Optimization/ Multi-Agent Learning