Chara Podimata

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Chara Podimata

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Chara Podimata is the Class of 1942 Career Development Assistant Professor and an Assistant Professor of Operations Research/Statistics in MIT Sloan. 

She is interested in social aspects of computing and more specifically, the effects of humans adapting to machine learning algorithms used for consequential decision-making.

While studying for her PhD, Chara interned at MSR and Google, and her research was supported by a Microsoft Dissertation Grant and a Siebel Scholarship.  She received her PhD from Harvard, advised by Yiling Chen and then was a FODSI postdoctoral fellow at UC Berkeley.

Outside of research, she spends her time adventuring with her pup, Terra.

More information can be found at her personal webpage: https://www.charapodimata.com/.

Publications

"Is Knowledge Power? On the (Im)possibility of Learning from Strategic Interactions."

Ananthakrishnan, Nivasini, Nika Haghtalab, Chara Podimata, and Kunhe Yang, Working Paper. August 2024. arXiv Preprint.

"Grace Period is All You Need: Individual Fairness without Revenue Loss in Revenue Management."

Patrick Jaillet, Chara Podimata, and Zijie Zhou. In Proceedings of the 20th Conference on Web and Internet Economics (WINE24), May 2024. arXiv Preprint.

"Preferences Evolve And So Should Your Bandits: Bandits with Deterministically Evolving Preference Effects."

Khashayar Khosravi, Renato Paes Leme, Chara Podimata, and Apostolis Tsorvantzis. In Proceedings of the 25th conference on Economics and Computation (EC24), February 2024. arXiv Preprint.

"When Should you Offer an Upgrade: Online Upgrading Mechanisms for Resource Allocation."

Patrick Jaillet, Chara Podimata, Andrew Vakhutinsky, and Zijie Zhou. In Proceedings of the 20th Conference on Web and Internet Economics (WINE24), February 2024. arXiv Preprint.

"Can Probabilistic Feedback Drive User Impacts in Online Platforms?"

Jessica Dai, Bailey Flanigan, Nika Haghtalab, Meena Jagadeesan, and Chara Podimata. In Proceedings of the 27th International Conference on Artificial Intelligence and Statistics (AISTATS24), January 2024. arXiv Preprint.

"Calibrated Stackelberg Games: Learning Optimal Commitments Against Calibrated Agents."

Nika Haghtalab, Chara Podimata, and Kunhe Yang. In Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS 2023), New Orleans, LA: December 2023.

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