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Siddharth PrasadEmail: sprasad2 (at) cs (dot) cmu (dot) eduOffice: GHC 5113 CV, Google Scholar, dblp |
I am a final-year PhD student in the Computer Science Department at Carnegie Mellon University advised by Nina Balcan and Tuomas Sandholm. My research is at the interface of mechanism and market design, optimization and operations research (specifically integer programming), and machine learning.
In Summer 2022 I worked on recommender systems at Google Research as a student researcher with Craig Boutilier and Martin Mladenov. I received a B.S. in math and computer science from Caltech in 2019.
PhD Thesis: Mechanism Design and Integer Programming in the Data Age
Committee: Nina Balcan, Tuomas Sandholm, Gérard Cornuéjols, Craig Boutilier, Peter Cramton
Research Papers
- 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 (top 2% of submissions)
[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 presentation (top 3% of submissions)
[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