I am a fifth year PhD candidate in Computer Science at Harvard University, where I am fortunate to be advised by Prof. David Parkes. My research explores topics at the intersection of Deep Learning, Algorithmic Economics and Multi-Agent Systems.

During my PhD, I spent two years as a Student Researcher at Google Research. I collaborated with the Algorithms and Optimization Team in NYC on topics at the intersection of mechanism design and LLMs, and with the Market Algorithms team in Mountain View on Auctions and Reinforcement Learning.

Before coming to Harvard, I was a Research Fellow at Microsoft Research, India with the Machine Learning and Optimization group. I received a B.Tech in Electrical Engineering with a minor in Computer Science from the Indian Institute of Technology, Kharagpur.

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Email  |  CV  |  Google Scholar  |  Github

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Research Interests

• Machine Learning for Mechanism Design
  My research focuses on how learning-based methods can be applied to problems in algorithmic design, particularly in settings involving strategic agents. I’ve worked on a range of problems—from auctions and matching markets to data markets—developing new frameworks that help uncover new mechanisms for allocation and pricing or protocols for matching.
  
  
• AI for Science
  A guiding influence in my work is the broader vision of AI for Science. Although my work has primarily focused on economic systems, it shares a common structure with efforts in areas like drug discovery and materials science, where researchers are building new architectures and modeling approaches that encode domain intuition. I’m excited to help develop toolboxes that can help accelerate scientific discovery as well.
  
  
• LLMs and Multi-Agent Systems
  Recently, I’ve been exploring projects at the intersection of LLMs and multi-agent systems, with a focus on what market design looks like when LLMs act as economic agents. This opens up new questions around robustness, equilibrium behavior, and design principles for markets populated by agentic models.

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Selected Publications

Optimal Auctions through Deep Learning: Advances in Differentiable Economics (Alphabetical) P. Dutting, Z. Feng, H. Narasimhan, DC. Parkes, SS. Ravindranath
— Journal of the ACM (JACM), September 2023
— Communications of the ACM (CACM), Volume 64 (8), August 2021
— International Conference on Machine Learning (ICML), 2019
[ArXiv] [Code]

Data Market Design through Deep Learning
SS. Ravindranath*, Y. Jiang*, DC Parkes
— Neural Information Processing Systems (NeurIPS), 2023
[ArXiv] [Code]

Deep Learning for Two-Sided Matching
SS. Ravindranath, Z. Feng, S. Li, J. Ma, SD. Kominers, DC. Parkes
Under submission
— Workshop on Matching Under Preferences (MATCH-UP), 2022
— Book Chapter: Machine Learning for Matching Markets
    (Alphabetical) Z. Feng, DC. Parkes, SS. Ravindranath
    In F. Echenique N. Immorlica and V. Vazirani, editors
    Online matching theory and market design. Cambridge University Press, 2022.
[ArXiv] [Code]

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All Publications and Preprints

Strategic Foundation Models
Denizalp Goktas, Amy Greenwald, Takayuki Osogami, Roma Patel, Kevin Leyton-Brown, Grant Schoenebeck, Daphne Cornelisse, Constantinos Daskalakis, Ian Gemp, John Horton, David C Parkes, David M Pennock, Arjun Prakash, Sai Srivatsa Ravindranath, Max Olan Smith, Gokul Swamy, Eugene Vinitsky, Segev Wasserkrug, Michael Wellman, Jibang Wu, Haifeng Xu, Jiayao Zhang, Yichi Zhang, Sadie Zhao, Quanyan Zhu.
Position paper, 2025.
[Paper]

Automated Mechanism Design: A Survey
MJ. Curry, Z. Fan, Y. Jiang, SS. Ravindranath, T. Wang, DC. Parkes
SIGecom Exchanges, 2025
[Paper]

Deep Reinforcement Learning for Sequential Combinatorial Auctions
SS. Ravindranath, Z. Feng, D. Wang, M. Zaheer, A. Mehta, DC. Parkes
Under submission
[ArXiv] [Code]

Data Market Design through Deep Learning
SS. Ravindranath*, Y. Jiang*, DC Parkes
Neural Information Processing Systems (NeurIPS), 2023
[ArXiv] [Code]

< a href="https://arxiv.org/abs/1706.03459.pdf">Optimal Auctions through Deep Learning: Advances in Differentiable Economics (Alphabetical) P. Dutting, Z. Feng, H. Narasimhan, DC. Parkes, SS. Ravindranath
— Journal of the ACM (JACM), September 2023
— Communications of the ACM (CACM), Volume 64 (8), August 2021
— International Conference on Machine Learning (ICML), 2019
[ArXiv] [Code]

Deep Learning for Two-Sided Matching
SS. Ravindranath, Z. Feng, S. Li, J. Ma, SD. Kominers, DC. Parkes
— Workshop on Matching Under Preferences (MATCH-UP), 2022
— Book Chapter: Machine Learning for Matching Markets
    (Alphabetical) Z. Feng, DC. Parkes, SS. Ravindranath
    In F. Echenique N. Immorlica and V. Vazirani, editors
    Online matching theory and market design. Cambridge University Press, 2022.
[ArXiv] [Code]

From Predictions to Decisions: Using Lookahead Regularization
N. Rosenfeld, S. Hilgard, SS. Ravindranath, DC. Parkes
Neural Information Processing Systems (NeurIPS), 2020
[ArXiv] [Code]

Machine Learning for Optimal Economic Design
(Alphabetical) P. Dutting, Z. Feng, N. Golowich, H. Narasimhan, DC. Parkes, SS. Ravindranath
In JF Laslier, H. Moulin, MR. Sanver, WS. Zwicker, editors,
The Future of Economic Design. Springer, 2019
[Book Chapter]

Salient Object Detection via Objectness Measure
SS. Ravindranath, RV. Babu
International Conference on Image Processing (ICIP), 2015
[ArXiv] [Code]

Learning Objective functions for Improved Image retrieval
SS. Ravindranath, M. Gygli, LV. Gool
MediaEval 2015 Workshops
[Paper]