Chara Podimata is an Assistant Professor of OR/Stat 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/.
Renato Paes Leme, Chara Podimata, and Jon Schneider. In Proceedings of the 35th Annual Conference on Learning Theory (COLT22), edited by Suriya Gunasekar. London, UK: July 2022. arXiv Preprint.
Yahav Bechavod, Chara Podimata, Zhiwei Steven Wu, and Juba Ziani. In Proceedings of the 39th International Conference on Machine Learning (ICML22), Baltimore, MD: July 2022. arXiv Preprint.
Podimata, Chara. Harvard University, 2022.
Chara Podimata and Aleksandrs Slivkins. In Proceedings of the 34th Annual Conference on Learning Theory (COLT21), edited by Suriya Gunasekar. Boulder, CO: August 2021. arXiv Preprint.
Akshay Krishnamurthy, Thodoris Lykouris, Chara Podimata, and Robert Schapire. In Proceedings of the 53rd ACM Symposium on Theory of Computing (STOC21), Virtual, Italy: June 2021. arXiv Preprint .