Scholar
Chara Podimata
Google Scholar ID: YatAkqAAAAAJ
Assistant Professor of OR/Stat, MIT
incentive-aware machine learning
bandits
AI policy
human-facing algorithmic design
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Citations & Impact
All-time
Citations
568
H-index
11
i10-index
11
Publications
20
Co-authors
35
list available
Contact
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GitHub
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Publications
12 items
Do LLMs Track Public Opinion? A Multi-Model Study of Favorability Predictions in the 2024 U.S. Presidential Election
2026
Cited
0
Contextual Dynamic Pricing with Heterogeneous Buyers
2025
Cited
0
Desirable Effort Fairness and Optimality Trade-offs in Strategic Learning
2025
Cited
0
Large-Scale, Longitudinal Study of Large Language Models During the 2024 US Election Season
2025
Cited
0
User Altruism in Recommendation Systems
2025
Cited
0
Incentive-Aware Machine Learning; Robustness, Fairness, Improvement&Causality
2025
Cited
0
Incentivizing Desirable Effort Profiles in Strategic Classification: The Role of Causality and Uncertainty
2025
Cited
0
Online Scheduling for LLM Inference with KV Cache Constraints
2025
Cited
0
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Resume (English only)
Academic Achievements
Amazon Research Award (2023)
MacArthur Foundation x-grant
Google Research Scholar Award (2025)
MIT grant from the GenAI Consortium (MGAIC)
Microsoft Dissertation Grant during PhD
Siebel Scholarship during PhD
Research Experience
Assistant Professor of Operations Research and Statistics at MIT
Lead Researcher at Archimedes/Athena RC
Former FODSI Postdoctoral Fellow at UC Berkeley
Summer 2021 intern at Google NYC, hosted by Renato Paes Leme
Intern at Microsoft Research NYC in summer 2019 (mentored by Jennifer Wortman Vaughan) and spring 2020 (mentored by Alex Slivkins)
Previously interned at Google in Athens, Greece
Background
Class of 1942 Career Development Assistant Professor of Operations Research and Statistics at MIT
Lead Researcher at Archimedes/Athena RC
Research interests lie at the intersection of Theoretical Computer Science, Economics, and Machine Learning
Specific focus areas include incentive-aware machine learning, social computing, online learning, and mechanism design
Recently exploring policy questions related to AI and recommendation systems
Co-authors
35 total
Zhiwei Steven Wu
Carnegie Mellon University
Nika Haghtalab
University of California, Berkeley
Nisarg Shah
Associate Professor, University of Toronto
Yang Liu
Computer Science and Engineering, UC Santa Cruz
Ariel Procaccia
Alfred and Rebecca Lin Professor of Computer Science, Harvard University
Juba Ziani
Georgia Institute of Technology (ISyE)
Co-author 7
Akshay Krishnamurthy
University of Massachusetts Amherst
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