I am a Research Scientist at Spotify, where I work on foundation models for personalization and recommender systems, as well as on multi-objective optimization.
I completed my PhD in Computer Science at Harvard University, advised by David Parkes. My research explored the intersection of deep learning, mechanism design, and multi-agent systems. During my PhD, I spent two years at Google Research working on projects at the intersections of mechanism design and LLMs, and auctions and reinforcement learning.
Before Harvard, I was a Research Fellow at Microsoft Research India. I received my B.Tech in Electrical Engineering with a minor in Computer Science from IIT Kharagpur.
Outside of research, I’m an active member of the MIT Outing Club and love hiking and skiing in the White Mountains of New Hampshire. I also love salsa dancing.
Email | CV | Google Scholar | Github
Publications
Foundation Models for Personalization
Who Are We Recommending To? Recommender Systems in the Agentic Web
H. Abdollahpuri, K. Kretschman, SS. Ravindranath, J. Doremus, M. Lalmas
Twentieth ACM Conference on Recommender Systems (RecSys), 2026
[ArXiv]
Deep Learning and Mechanism Design
Data Market Design through Deep Learning
SS. Ravindranath*, Y. Jiang*, DC Parkes
— Mathematics of Operations Research (MOR), forthcoming, 2026
— Neural Information Processing Systems (NeurIPS), 2023
[ArXiv]
[Code]
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
[ArXiv]
[Code]
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
— Book Chapter: 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
[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]
LLMs and Mechanism Design
Framing and Signaling: An LLM-Based Approach to Information Design
(Alphabetical) P. Duetting, S. Hossain, T. Lin, RP. Leme, SS. Ravindranath, H. Xu, S. Zuo
Economics and Computation (EC), 2026
[ArXiv]
Strategic Foundation Models
D. Goktas, A. Greenwald, T. Osogami, R. Patel, K. Leyton-Brown, G. Schoenebeck, D. Cornelisse, C. Daskalakis, I. Gemp, J. Horton, DC. Parkes, DM. Pennock, A. Prakash, SS. Ravindranath, MO. Smith, G. Swamy, E. Vinitsky, S. Wasserkrug, M. Wellman, J. Wu, H. Xu, J. Zhang, Y. Zhang, S. Zhao, Q. Zhu.
Position paper, 2025.
[Paper]
Others
From Predictions to Decisions: Using Lookahead Regularization
N. Rosenfeld, S. Hilgard, SS. Ravindranath, DC. Parkes
Neural Information Processing Systems (NeurIPS), 2020
[ArXiv]
[Code]
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]