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Yuantong Li (李沅桐)

Senior Research Scientist
Meta
Building AI Systems for Human Behavior and Decision Making

E-mail: liyuantong93 [@] gmail [DOT] com

[Google Scholar] [Github] [LinkedIn]

About me

2024 - Now, Senior Research Scientist, Meta.

2019 - 2024, Ph.D. in Statistics at UCLA.

Biography

I am a Senior Research Scientist at Meta AI, where I develop large-scale AI systems for personalization and human behavior modeling. My research lies at the intersection of sequential decision making, causal inference, optimization, and machine learning. I am particularly interested in understanding and optimizing human decisions under uncertainty, with applications in recommender systems, AI agents, and market design. Previously, I received my Ph.D. in Statistics from UCLA, where I worked on statistical learning, multi-agent decision making, and matching markets. More broadly, I aim to build intelligent systems that can reason about human behavior and make adaptive decisions at scale.

Research Interests

  • sequential decision making

  • causal inference

  • personalization and recommendation

  • AI economics and market design

  • foundation models for human behavior

Research Vision

I believe the next generation of AI systems will move beyond static prediction and become adaptive decision-making systems that understand and influence human behaior. My long-term goal is to build AI systems that can:

  • model human preferences

  • reason under uncertainty

  • make seqeuntial decisions

  • and coordinate among multiple agents and economics incentives

My mission is to buld intelligent systems that understand, predict, and optimize human decisions under uncertainty.

Experiences

  • Meta, Senior Research Scientist, 2024   Now

    • recommendation foundation Models

    • cold start modeling

    • user behavior modeling

    • large-scale personalization

  • Amazon Search, Applied Scientist Intern, 2023

    • Multi-task LLM

    • Search CTR optimization

    • Behavior foundation model

  • Amazon AWS AI Lab, Applied Scientist Intern, 2022

    • LLM generation

    • fairness

Selected Publications

Recommendation Systems

  • MBD: A Model-Based Debiasing Framework Across User, Content, and Model Dimensions
    Yuantong Li, Lei Yuan, Zhihao Zheng, Weimiao Wu, Songbin Liu, Jeong Min Lee, Ali Selman Aydin, Shaofeng Deng, Junbo Chen, Xinyi Zhang, Hongjing Xia, Sam Fieldman, Matthew Kosko, Wei Fu, Du Zhang, Peiyu Yang, Albert Jin Chung, Xianlei Qiu, Miao Yu, Zhongwei Teng, Hao Chen, Sunny Baek, Hui Tang, Yang Lv, Renze Wang, Qifan Wang, Zhan Li, Tiantian Xu, Peng Wu, Ji Liu.

Sequential Decision Making

Statistical Learning

AI Applications