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Siddharth Prasad
Email: sprasad2 (at) cs (dot) cmu (dot) edu
Office: GHC 5113
CV, Google
Scholar, dblp
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I am a fourth-year PhD student in the Computer Science Department at Carnegie Mellon
University advised by
Nina Balcan and
Tuomas Sandholm.
My research interests span machine learning, integer programming,
mechanism design, algorithms, and their various interactions.
I was a student researcher at
Google Research during Summer 2022, hosted by
Craig Boutilier and
Martin Mladenov.
I received a B.S. in math and computer science from Caltech in 2019.
Research papers
- Bicriteria Multidimensional Mechanism Design with Side Information
(with Nina Balcan and
Tuomas Sandholm).
Manuscript.
[arXiv]
- 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).
[arXiv]
[poster]
[video]
- Maximizing Revenue under Market Shrinkage and Market
Uncertainty
(with Nina Balcan and
Tuomas Sandholm).
Conference on Neural Information Processing Systems (NeurIPS), 2022.
[slides]
[poster]
[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.
[arXiv]
[slides]
- 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).
[arXiv]
[slides]
[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]
[slides]
[video]
- Efficient Algorithms for Learning
Revenue-Maximizing Two-Part Tariffs
(with Nina Balcan and
Tuomas Sandholm).
International Joint Conference on Artificial Intelligence (IJCAI), 2020.
[slides]
[video]
-
Incentive Compatible Active Learning
(with Federico Echenique).
Innovations in Theoretical Computer Science Conference (ITCS), 2020.
[arXiv]
[slides]
[video]
-
Learning Time Dependent Choice
(with Zachary Chase).
Innovations in Theoretical Computer Science Conference (ITCS), 2019.
[arXiv]
[slides]
Selected Talks
Teaching
I was a teaching assistant for the following courses.
Other
![[poster]](slides/poster_gmi_cuts.png){kind=link}
![[poster]](slides/poster_market_shrinkage.png){kind=link}