Tao Lin
Tao Lin

PhD in Computer Science

Harvard University

About Me

I am a postdoctoral researcher at Microsoft Research (New England), in the Economics and Computation group, hosted by Alex Slivkins.

I obtained my PhD in Computer Science from Harvard University in 2025 (advised by Yiling Chen) and BSc from Peking University in 2020 (advised by Xiaotie Deng). My research spans economics, machine learning, and theoretical computer science, focusing on mechanism design and information design for learning-based decision-makers, with applications to, e.g., advertising auctions and recommender systems. From 2023 to 2024, I interned at ByteDance and Google. I received the Siebel Scholar Award in 2025.

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
  • Algorithmic Game Theory
  • Mechanism Design
  • Machine Learning
  • Theoretical Computer Science
Education
  • PhD in Computer Science

    Harvard University

  • BSc in EECS

    Peking University

🗞️ News
📚 My Research

My research direction is learning-based incentive design, an interdiscplinary topic in economics, machine learning, and theoretical computer science. I study mechanism design and information design problems with learning-based decision-makers. Example directions include:

  • Learning agents: I investigate how the learning behavior of boundedly rational agents (modeled by, e.g., reinforcement learning) affects the outcome of games, compared to the outcome predicted by the traditional rational-agent-based economic theory.
  • Learning principals: I also study how the principals (designers of mechanisms and information structures) can achieve optimal design goals by learning unknown parameters about agents and environments from repeated interactions. Such learning problems involving dynamic and strategic data sources, departing from traditional machine learning paradigms, requiring new methodologies that I aim to develop.

My research is often motivated by the interplay between economic incentives and machine learning algorithms in real-world AI systems, such as advertising auctions and recommender systems.

📑 Featured Projects

Information Design With Large Language Models

Generalized Principal-Agent Problem with a Learning Agent

📃 Working Papers
(2025). Learning to Play Multi-Follower Bayesian Stackelberg Games. Working paper.
(2025). Information Design With Large Language Models. Working paper.
(2025). WOMAC: A Mechanism For Prediction Competitions. Working paper.
(2025). Explainable Information Design. Working paper.
(2025). Learning to Coordinate Bidders in Non-Truthful Auctions. Working paper.
(2025). Learning a Game by Paying the Agents. Working paper.
📃 Conference Publications
(2025). A Unified Approach to Submodular Maximization Under Noise. Advances in Neural Information Processing Systems (NeurIPS).
(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.
📃 Journal Publications
(2025). From Monopoly to Competition: When Do Optimal Contests Prevail?. Games and Economic Behavior.
📝 Notes
🎙️ Recent & Upcoming Talks

Experience

  1. Postdoctoral Researcher

    Microsoft
    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