Yan Li (李岩)

Yan Li (李岩)

MSc student in Machine Learning at MBZUAI, working on causal representation learning.

About

Hello! I am Yan Li (李岩), an M.Sc. student in Machine Learning at the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), advised by Kun Zhang.
I mainly work with Guangyi Chen and Yunlong Deng in the MBZUAI Causality Group.

From July 2025 to December 2025, I will be a visiting student in the Department of Philosophy at Carnegie Mellon University (CMU), working with Peter Spirtes and Kun Zhang, and collaborating with the CMU CLeaR Group.

Previously, I obtained a B.Eng. in Computer Science and Technology from Tsinghua University in June 2024, where I worked at the Research Institute of Human–Computer Interaction and Media Integration with Chun Yu, mainly on HCI and context-aware systems.

Broadly, I am interested in the mathematical and causal foundations of machine learning, and in building robust and interpretable models.

Research interests

  • Causal representation learning
  • Domain adaptation, domain generalization, and out-of-distribution robustness
  • Multimodal and vision–language models

News

  • 2025.12 — I will attend NeurIPS 2025 in San Diego and present two posters.
    Feel free to talk to me at the poster sessions.
  • 2025.09 — “CausalVerse: Benchmarking Causal Representation Learning with Configurable High-Fidelity Simulations” accepted to NeurIPS 2025 (spotlight).
  • 2025.09 — “Towards Self-Refinement of Vision-Language Models with Triangular Consistency” accepted to NeurIPS 2025 (poster).
  • 2025.04 — “A General Representation-Based Approach to Multi-Source Domain Adaptation” presented at ICML 2025 (poster).
  • 2025.07–2025.12 — Visiting student at Carnegie Mellon University, Department of Philosophy.
  • 2024.10 — Started the M.Sc. in Machine Learning at MBZUAI, advised by Kun Zhang.
  • 2024.06 — Graduated from Tsinghua University (B.Eng. in Computer Science and Technology).
  • 2023.06–2023.07 — Research intern at Mian Bi Intelligent Technology Co., Ltd., working on pre-training data preparation for large language models.
  • 2022 — Third Prize, 40th Challenge Cup of Tsinghua University.
  • 2020 — Second-Class Scholarship for Freshmen, Tsinghua University.

Publications

*Equal contribution is indicated by * after author names.

Research experience

CMU CLeaR Group & MBZUAI Causality Group

Advisors: Kun Zhang, Peter Spirtes

Evaluating causal representation learning methods · Jul. 2025 – present

  • Build on CausalVerse to systematically evaluate different causal representation learning (CRL) methods.
  • Study when methods achieve extremely high R² scores and test principles such as minimal interventions or minimal changes.
  • Aim to provide a comprehensive benchmark of method performance on images and videos, and to derive new methods and principles from empirical observations (e.g., for iMSDA-like methods, smaller variation dimension can work better under suitable hyperparameters).

Continuous learning with temporal data · Oct. 2025 – present

  • Study continual learning for time-series representations inspired by how humans learn hierarchical latent variables.
  • Explore models that first learn high-level latent structure and then refine lower-level factors, enabling better adaptation of model components over time.
  • Participate in project discussions, simulations, and experiments.

Causality Group, MBZUAI

Causal representation learning benchmark (CausalVerse) · Feb. 2025 – Jun. 2025

  • Developed a benchmark for causal representation learning using high-fidelity simulated visual data with ground-truth causal processes.
  • Dataset includes around 200k images and 3M video frames across 24 sub-scenes in four domains: static image generation, physical simulations, robotic manipulation, and traffic analysis.
  • Responsible for static image generation of physical processes, implementing and testing image-related baselines, and contributing to writing.

Framework to use data bias in domain adaptation · Jul. 2024 – May 2025

  • Proposed a framework that leverages data bias to complement invariant representations in domain adaptation.
  • Used domain biases to re-weight experts trained on source domains and showed that invariant representations can help identify beneficial biases.
  • Responsible for algorithm design, theoretical analysis, implementation, and paper writing.

Universal domain adaptation · Nov. 2024 – Jan. 2025

  • Worked on a general domain adaptation framework that learns compact latent representations capturing task-relevant distribution shifts.
  • Developed a non-parametric approach that handles different types of distribution shifts in a unified setting.
  • Participated in theory development, experiments, and writing.

Self-refinement of vision–language models · Jul. 2024 – Nov. 2024

  • Studied the self-refinement capabilities of vision–language models (VLMs) and how they can generate high-quality supervised data without external labels.
  • Contributed to the theoretical analysis and writing.

Research Institute of Human–Computer Interaction and Media Integration, Tsinghua University

Advisor: Chun Yu · Oct. 2021 – Oct. 2023

During my undergraduate years, I worked on human–computer interaction (HCI), aiming to better connect humans and machines. After several projects and completing coursework for a statistics minor, I became more interested in the mathematical mechanisms of AI and shifted my focus toward machine learning and causality.

Representative work and projects:

  • THUPat campus app
    • Designed and developed a campus mobile application, which won Third Prize in the 40th Challenge Cup of Tsinghua University.
  • User-centric context-aware area discovery
    • Participated in designing user experiments, validating the method, and writing the paper “Revealing Personalized Positions in Indoor Spaces: A User-Centric Approach to Context-Aware Area Discovery.”

Industry experience

Mian Bi Intelligent Technology Co., Ltd. · Jun. 2023 – Jul. 2023
Supervisor: Jie Cai, Algorithm Engineer

  • Worked on data preparation for pre-training large language models, including data-cleaning algorithms and large-scale batch cleaning.
  • Participated in discussions on classical text LLMs, data pipelines, and preliminary design.

Education

  • M.Sc. in Machine Learning, Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) · Oct. 2024 – Jun. 2026
    Supervisor: Kun Zhang

  • Visiting student, Department of Philosophy, Carnegie Mellon University (CMU) · Jul. 2025 – Dec. 2025
    Visiting tutors: Peter Spirtes, Kun Zhang

  • B.Eng. in Computer Science and Technology, Tsinghua University · Sept. 2020 – Jun. 2024

Honors and awards

  • MBZUAI Conference Travel Scholarship, 2025
  • Tsinghua University Academic Progress Scholarship, 2023
  • Social Practice Excellence Scholarship of Tsinghua University, 2022
  • Third Prize, 40th Challenge Cup of Tsinghua University, 2022
  • Second-Class Scholarship for Freshmen, Tsinghua University, 2020

Service

  • Reviewer, ICLR

Contact

  • Email: lyan012010 (at) gmail (dot) com