Chara Podimata

PhD Student in Computer Science at Harvard University


Harvard University, Maxwell Dworkin 223, 33 Oxford Street Cambridge, MA 02138
podimata[AT]g[DOT]harvard[DOT]edu

About


I'm 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.

Before joining Harvard, I was an intern for Google in Athens, Greece. I received my Diploma from National Technical University of Athens, where I was advised by Professor Dimitris Fotakis.

You can find my CV here [Last update: February 2021].

My birthname is Charikleia Podimata, but I go by Chara. To pronounce my name correctly: Ha (as in Harlem) and ra (as in rabbit).


Ben Edelman, Yo Shavit and I are organizing a tutorial called "How to Achieve Both Transparency and Accuracy in Predictive Decision Making: An Introduction to Strategic Prediction"!
Nika Haghtalab and I organized the "Incentive-Compatible and Incentive-Aware Learning" tutorial during EC '20.

News
  • [May 2021] Our paper with Alex was accepted to COLT2021!
  • [March 2021] Excited to announce that I'll be (remotely) joining Google NYC for a summer research internship!
  • [February 2021] Our paper with Akshay, Thodoris, and Rob was just accepted to STOC2021!
  • [January 2021] Ben Edelman, Yo Shavit and I will be presenting a tutorial during FAccT'21.
  • [December 2020] We adopted a puppy!
  • [November 2020] I will be attending the virtual INFORMS 2020 (11/09 at 2.15 - 3.15pm) and the virtual Rising Stars in EECS (11/09-10)! Hit me up if you want to chat!
  • [September 2020] Our paper Learning Strategy-Aware Linear Classifiers has been accepted to NeurIPS2020!
  • [June 2020] New paper on arXiv from my great spring internship with Alex on Adaptive Discretization for Adversarial Bandits with Continuous Action Spaces!
  • [June 2020] Our paper No-Regret and Incentive-Compatible Online Learning was accepted at ICML2020!
  • [March 2020] Nika Haghtalab and I will be presenting the Incentive-Compatible and Incentive-Aware Learning tutorial in EC20!
  • [February 2020] New paper on arXiv from my awesome collaboration with Akshay and Thodoris, which we started during my summer internship: Corrupted Multidimensional Binary Search: Learning in the Presence of Irrational Agents.
  • [February 2020] New paper on arXiv from my wonderful summer internship with Jenn, Dave, and Rupert: No-Regret and Incentive-Compatible Online Learning.
  • [January 2020] I was named a finalist for the Facebook 2020 PhD Fellowship!
  • [May 2019] New paper on arXiv: Grinding the Space: Learning to Classify Against Strategic Agents.
  • [January 2019] I was among the finalists for the 2019 Microsoft Research PhD Fellowship.
  • [November 2018] Our paper A Bridge between Liquid and Social Welfare in Combinatorial Auctions with Submodular Bidders has been accepted at AAAI-19!
  • [September 2018] New paper on arXiv: A Bridge between Liquid and Social Welfare in Combinatorial Auctions with Submodular Bidders. Check it out!
  • [June 2018] Our paper Strategyproof Linear Regression in High Dimensions was among the 5 shortlisted papers for the Best Paper Award at EC ' 18!
  • [April 2018] Two papers accepted in EC '18! Strategyproof Linear Regression in High Dimensions and Learning to Bid Without Knowing your Value.



Special thanks to Sophie Spatharioti for helping create this page!