Chara Podimata

FODSI Postdoctoral Fellow at UC Berkeley


Melvin Calvin Lab Room 318, Simons Institute, CA 94720
podimata[AT]g[DOT]harvard[DOT]edu
My Google Scholar Profile

About


I'm a FODSI postdoctoral fellow affiliated with UC Berkeley and the Simons Institute. For Fall 2022, I'll be a research fellow for the semester on "Data-Driven Decision Processes". Starting July 2023, I'll be an Assistant Professor of OR/Stat at MIT. Previously, I obtained my PhD in CS and was a member of the EconCS group at Harvard, where I was advised by Professor Yiling Chen. My research interests lie mostly on the intersection of Theoretical Computer Science, Economics and Machine Learning and specifically on incentive-aware machine learning, online learning, and mechanism design. During my PhD, my research was generously supported by a Microsoft Dissertation Grant and a Siebel Scholarship.

During the summer of 2019 and spring of 2020, I was an intern at Microsoft Research in New York City, mentored by Jennifer Wortman Vaughan and Alex Slivkins respectively. During the summer of 2021, I was an intern at Google in New York City, hosted by Renato Paes Leme.

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: August 2022].

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

News
  • [August 2022] My tutorial "Incentive-Aware Machine Learning: A Tale of Robustness, Fairness, Improvement, and Performativity" was accepted for NeurIPS22.
  • [May 2022] My paper with Renato and Jon was accepted at COLT22! My paper with Yahav, Steven, and Juba was accepted at ICML22!
  • [April 2022] I'm joining the OR/Stat group at MIT Sloan as an Assistant Professor from July 2023!
  • [April 2022] I will be a FODSI postdoc at UC Berkeley from August 2022 until July 2023!
  • [September 2021] I am the recipient of a Siebel Scholarship!
  • [June 2021] I was granted an MSR Dissertation Grant!
  • [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!