I am a Research Assistant Professor at Toyota Technological Institute at Chicago (TTIC). I work on topics at the convergence of mechanism and market design, optimization and operations research, and machine learning.
I received a PhD in Computer Science from Carnegie Mellon University, where I was advised by Nina Balcan and Tuomas Sandholm. During the PhD, I spent a summer working on recommender systems at Google Research. Before the PhD, I received a B.S. in math and computer science from Caltech.
My PhD Thesis: Mechanism Design and Integer Programming in the Data Age
Research@TTIC talk about some of my PhD research
Research Papers
- Revenue-Optimal Efficient Mechanism Design with General Type Spaces
with Nina Balcan and Tuomas Sandholm - Weakest Bidder Types and New Core-Selecting Combinatorial Auctions
with Nina Balcan and Tuomas Sandholm
AAAI Conference on Artificial Intelligence (AAAI), 2026 [Oral presentation] - New Sequence-Independent Lifting Techniques for Cover Inequalities and When They Induce Facets
with Ellen Vitercik, Nina Balcan, and Tuomas Sandholm
International Joint Conference on Artificial Intelligence (IJCAI), 2025
[Best poster award (honorable mention) at MIP workshop, 2024] - Increasing Revenue in Efficient Combinatorial Auctions by Learning to Generate Artificial Competition
with Nina Balcan and Tuomas Sandholm
AAAI Conference on Artificial Intelligence (AAAI), 2025
- Bicriteria Multidimensional Mechanism Design with Side Information
with Nina Balcan and Tuomas Sandholm
Conference on Neural Information Processing Systems (NeurIPS), 2023
[sigecom reading list] - Content Prompting: Modeling Content Provider Dynamics to Improve User Welfare in Recommender Ecosystems
with Martin Mladenov and Craig Boutilier
RecSys Workshop on Causality, Counterfactuals, and Sequential Decision Making (CONSEQUENCES), 2023
[recsys talk] - Structural Analysis of Branch-and-Cut and the Learnability of Gomory Mixed Integer Cuts
with Nina Balcan, Tuomas Sandholm, and Ellen Vitercik
Conference on Neural Information Processing Systems (NeurIPS), 2022 [Oral presentation]
[video] - Maximizing Revenue under Market Shrinkage and Market Uncertainty
with Nina Balcan and Tuomas Sandholm
Conference on Neural Information Processing Systems (NeurIPS), 2022
[video] - Improved Sample Complexity Bounds for Branch-and-Cut
with Nina Balcan, Tuomas Sandholm, and Ellen Vitercik
International Conference on Principles and Practice of Constraint Programming (CP), 2022 - Sample Complexity of Tree Search Configuration: Cutting Planes and Beyond
with Nina Balcan, Tuomas Sandholm, and Ellen Vitercik
Conference on Neural Information Processing Systems (NeurIPS), 2021 [Spotlight]
[video] - Learning Within an Instance for Designing High-Revenue Combinatorial Auctions
with Nina Balcan and Tuomas Sandholm
International Joint Conference on Artificial Intelligence (IJCAI), 2021
[proceedings version] [video] - Efficient Algorithms for Learning Revenue-Maximizing Two-Part Tariffs
with Nina Balcan and Tuomas Sandholm
International Joint Conference on Artificial Intelligence (IJCAI), 2020
[video] - Incentive Compatible Active Learning
with Federico Echenique
Innovations in Theoretical Computer Science Conference (ITCS), 2020
[itcs talk] - Learning Time Dependent Choice
with Zachary Chase
Innovations in Theoretical Computer Science Conference (ITCS), 2019 - Walks on Primes in Imaginary Quadratic Fields
