Tao Lin
Tao Lin

PhD in Computer Science

Harvard University

About Me

Starting in July 2025, I will be a postdoctoral researcher at Microsoft Research, in the Economics and Computation group in New England, hosted by Alex Slivkins.

I obtained my PhD degree in Computer Science from Harvard University, where I am very fortunate to be advised by Yiling Chen. My research lies in the intersection between economics and machine learning. I have been working on “mechanism design + machine learning” problems since my undergraduate study at Peking University, working with Xiaotie Deng. Recently, I focused more on information design problems, like Bayesian persuasion. I also investigate the incentive issues in real-world machine learning systems, such as ad auction platforms and recommender systems. From 2023 to 2024, I interned at ByteDance and Google. I received the Siebel Scholarship in 2024.

I will be an assistant professor in the School of Data Science at the Chinese University of Hong Kong, Shenzhen, starting in 2026.

Contact: tlin@g.harvard.edu

Interests
  • Information Design
  • Mechanism Design
  • Machine Learning
Education
  • PhD in Computer Science

    Harvard University

  • BSc in EECS

    Peking University

๐Ÿ—ž๏ธ News
๐Ÿ“š My Research
My research direction is Learning-Based Incentive Design. It is motivated by the question of “how to design a multi-agent system so that self-interested agents are incentivized to achieve a globally desirable outcome”. In particular, I study mechanism design and informamtion design problems with machine-learning-based decision-makers. I am also fascinated by the incentive issues in real-world machine learning systems, such as ad auction platforms and recommender systems.
๐Ÿ“‘ Featured Publications

Generalized Principal-Agent Problem with a Learning Agent

๐Ÿ“ƒ Working Papers
(2025). Learning a Game by Paying the Agents. Working paper.
๐Ÿ“ƒ Conference Publications
(2025). Generalized Principal-Agent Problem with a Learning Agent. International Conference on Learning Representations (ICLR spotlight).
(2025). Information Design with Unknown Prior. Proceedings of Innovations in Theoretical Computer Science (ITCS).
(2024). User-Creator Feature Polarization in Recommender Systems with Dual Influence. Advances in Neural Information Processing Systems (NeurIPS).
(2024). Bias Detection via Signaling. Advances in Neural Information Processing Systems (NeurIPS).
(2024). Multi-Sender Persuasion: A Computational Perspective. International Conference on Machine Learning (ICML).
(2024). Learning Thresholds with Latent Values and Censored Feedback. International Conference on Learning Representations (ICLR).
(2023). Sample Complexity of Forecast Aggregation. Advances in Neural Information Processing Systems (NeurIPS spotlight).
(2023). From Monopoly to Competition: Optimal Contests Prevail. Proceedings of the AAAI Conference on Artificial Intelligence.
(2022). Nash Convergence of Mean-Based Learning Algorithms in First Price Auctions. Proceedings of the ACM Web Conference (WWW).
(2022). How Many Representatives Do We Need? The Optimal Size of a Congress Voting on Binary Issues. Proceedings of the AAAI Conference on Artificial Intelligence.
(2020). Learning Utilities and Equilibria in Non-Truthful Auctions. Advances in Neural Information Processing Systems (NeurIPS).
(2020). A Game-Theoretic Analysis of the Empirical Revenue Maximization Algorithm with Endogenous Sampling. Advances in Neural Information Processing Systems (NeurIPS).
(2020). Private Data Manipulation in Optimal Sponsored Search Auction. Proceedings of The Web Conference (WWW).
๐Ÿ“ƒ Journal Publications
(2024). From Monopoly to Competition: When Do Optimal Contests Prevail?. Minor revision at Games and Economic Behavior.
๐Ÿ“ Notes
๐ŸŽ™๏ธ Recent & Upcoming Talks

Experience

  1. Postdoctoral Researcher

    Microsoft Research
    Hosted by Alex Slivkins
  2. Student Researcher

    Google
  3. Research Intern

    ByteDance
    Hosted by Yang Liu

Education

  1. PhD in Computer Science

    Harvard University
  2. BSc in EECS

    Peking University