Tao Lin
Tao Lin

PhD in Computer Science

Harvard University

About Me

Contact: tlin@g.harvard.edu or lintao@cuhk.edu.cn

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. I am especially interested in 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. I am looking for PhD students starting in Fall 2026; please see here for details.

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
Information Design With Large Language Models

Working paper
Learning to Play Multi-Follower Bayesian Stackelberg Games

Working paper
WOMAC: A Mechanism For Prediction Competitions

Working paper
Explainable Information Design

Working paper
Learning a Game by Paying the Agents

Working paper
📃 Publications
See here.
📝 Notes
🎙️ Recent & Upcoming Talks

Experience

  1. Assistant Professor

    The Chinese University of Hong Kong, Shenzhen
  2. Postdoctoral Researcher

    Microsoft
    Hosted by Alex Slivkins
  3. Student Researcher

    Google
  4. Research Intern

    ByteDance
    Hosted by Yang Liu

Education

  1. PhD in Computer Science

    Harvard University
  2. BSc in EECS

    Peking University
For Prospective Students

I am looking for PhD and MPhil students starting in Fall 2026. If you have:

  • strong interest in pursuing interdisciplinary research across Computer Science, Data Science, and Economics;
  • good mathematical background and coding skills;
  • (bonus) ample experience in prompting GenAI for research and judging whether their responses are correct;

then please consider applying to the MPhil-PhD Program in Data Science or Computer Science of CUHK-Shenzhen, SDS, and mention my name in your application. Feel free to contact me at lintao@cuhk.edu.cn