I am a 4th-year PhD Candidate in Applied Math and Computational Science at the University of Pennsylvania. I am fortunate to be advised by Michael Kearns. Before Penn, I worked at TGG Group, a consulting firm applying econometrics and behavioral science; before that, I studied Mathematics and Economics at Columbia University.
My main research interests are in algorithmic learning theory, as well as the implications of learning for societal concerns like fairness and privacy, and to markets and strategic interactions. I am also interested in the interaction of computer science and economics more broadly, especially in the areas of algorithmic game theory and mechanism design. I am particularly interested in exploring these questions in dynamic contexts and in relation to reinforcement learning.
- (Fall 2020) I will be speaking about my work on fairness in mortgage lending with the Philly Fed at INFORMS 2020 .
- (Summer 2020) I will be speaking about our paper, Differentially Private Call Auctions and Market Impact at EC 2020 .
- (Summer 2020) I am interning at Facebook, graciously hosted by Brian Lan , Okke Schrijvers and Riccardo Colini-Baldeschi working on algorithms for auctions in theory and practice.
- (March-May 2020) I worked with Minchul Shin and Simon Freyaldenhoven at the Federal Reserve Bank of Philadelphia on fairness in the mortgage lending system.
- (January 2020) I discussed The Effects of Competition and Regulation on Error Inequality in Data-Driven Markets at ACM FAT* 2020.
- (December 2019) I will be discussing the preliminary results from The Effects of Competition and Regulation on Error Inequality in Data-Driven Markets at the 2019 AI for Social Good Workshop at NeuRIPS.
- (Summer 2019) I will be spending Summer 2019 at the FATE group at Microsoft Research Montreal, graciously hosted by Ben Fish.
- (July 2019) I will be presenting a poster for Hidden Information, Teamwork, and Prediction in Trick-Taking Card Games at RLDM.
- (June 2019) I will be giving a talk on The Price of Privacy in the Keynesian Beauty Contest at EC.
- Algorithms and Learning for Fair Portfolio Design . With E. Diana, T. Dick, M. Kearns, A. Roth,Z. Schutzman, S. Sharifi-Malvajerdi, and J. Ziani. Arxiv Version.
- Differentially Private Call Auctions and Market Impact. EC 2020. With E. Diana, M. Kearns, A. Roth, S. Sharifi-Malvajerdi, and J. Ziani. (Arxiv Version)
- The Effects of Competition and Regulation on Error Inequality in Data-Driven Markets. ACM FAT* 2020. With B. Fish. (Publication Version). Also presented as an oral/poster presentation at the 2019 NeuRIPS AI for Social Good Workshop. AISG Best Poster winner.
- Equilibrium Characterization for Data Acquisition Games. IJCAI 2019. With J. Dong, S. Jabbari, M. Kearns, and Z. Schutzman. (Publication Version) (Arxiv Version)
- The Price of Privacy in the Keynesian Beauty Contest. EC 2019. With Z. Schutzman. (Publication Version) (Arxiv Version)
- Hidden Information, Teamwork, and Prediction in Trick-Taking Card Games. (Extended Abstract). RLDM 2019. With M. Fereydounian, M. Hayhoe, and H. Kumar. (Publication Version)
- Fair Algorithms for Learning in Allocation Problems. ACM FAT* 2019. With S. Jabbari, C. Jung, M. Kearns, S. Neel, A. Roth, and Z. Schutzman. (Publication Version) (Arxiv Version)