PhD Candidate
Harvard University
Areas of Interest
- Artificial Intelligence
- Theory
- Algorithmic Economics
Poster
Algorithms for Incentive-Compatible and Incentive-Aware Learning
Abstract
Research done in the area of Incentive-Compatible and Incentive-Aware Learning addresses questions related to strategic behavior in Machine Learning (ML). These questions are of utmost importance nowadays, since ML algorithms are increasingly being used in real-world decision-making that affects our everyday lives; from the online advertising auctions that guide our purchasing behavior, to the complex algorithms that decide which news articles to serve us. In this poster, I highlight my results and broader vision in the area to build a theory of incentives for ML algorithms and to study their societal implications, by drawing intuition from game theory while providing novel online learning algorithms that are tailored to these settings.
Bio
I am a fifth year PhD student in the EconCS group at Harvard University, where I am advised by Professor Yiling Chen. My research interests lie mostly on the intersection of Theoretical Computer Science, Economics and Machine Learning and specifically on learning under the presence of strategic agents, online learning, and mechanism design. During the summer of 2019 and spring of 2020, I had the pleasure of being an intern at Microsoft Research in New York City, mentored by Jennifer Wortman Vaughan and Alex Slivkins respectively. During EC20, together with Nika Haghtalab, I co-taught my first tutorial on "Incentive-Compatible and Incentive-Aware Learning".
Before joining Harvard, I was an intern for Google in Athens, Greece. I received my diploma from the National Technical University of Athens, where I was advised by Professor Dimitris Fotakis. My birthname is Charikleia Podimata, but I go by Chara. To pronounce my name correctly: Ha (as in Harlem) and ra (as in rabbit).
