About
I'm a Class of 1942 Career Development Assistant Professor of OR/Stat at MIT and a Lead Researcher at Archimedes/Athena RC. My research interests lie mostly at the intersection of Theoretical Computer Science, Economics and AI and specifically on AI auditing (e.g., [1], [2]) and evaluation (e.g., [3], [4]), AI safety (e.g., [5]), incentive-aware AI (e.g., [6], [7]), social computing (e.g., [8], [9]), online learning (e.g., [10], [11]), and mechanism design (e.g., [12]). Recently, much of my thinking has focused on auditing the information ecosystems that LLMs create around elections for different subgroups of the population, how to quantify what those audits reveal, and how to run them automatically and at scale.
Funding. My research is supported by an Amazon Research Award (2023), a MacArthur Foundation x-grant, a Google Research Scholar Award (2025), an MIT grant from the GenAI Consortium (MGAIC), a MITHIC grant (joint with Adam Berinsky and Charles Stewart), and an MIT-Google grant.
Advising. I'm very lucky to be working with an incredible set of students. You can read about my advising philosophy as a professor in my Advising Statement. The statement was drafted in collaboration with Bailey Flanigan.
You can find my CV here [Last update: June 2026].
My birthname is Charikleia Podimata, but I go by Chara. To pronounce my name correctly, just pretend that the "C" is silent, i.e., Hara.
LLM Election Observatory
Ahead of the 2026 US midterm elections, my team launched the LLM Election Observatory: a live dashboard tracking the information that frontier models are giving to users about election processes, candidates, and voter-relevant issues, and how their responses vary with the political affiliation, race, gender, or location attributed to the person asking. The project is joint with my students Nicolas Emmenegger, Alessandro Morosini, and Young-Kyung Kim and collaborators Adam Berinsky and Charles Stewart.
Podcast
As part of the LeT-All initiative, I'm hosting the “Probably Approximately Correct Learners”! Once a month, I host an interview with a well-known member of the Learning Theory and AI community (broadly defined), where we discuss topics ranging from their latest research passion to how they maintain work-life balance and the effects of AI in their work. You can listen on Spotify, Apple Podcasts, YouTube, or wherever you get your podcasts.