Ting-Wei Li

📍 Champaign, IL, USA. 📧 twli AT illinois DOT edu.

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I’m Ting-Wei Li, a second-year Ph.D. student at the Siebel School of Computing and Data Science @ University of Illinois Urbana-Champaign. I am a member of iDEA-iSAIL Joint Laboratory and my supervisor is Prof. Hanghang Tong. I previously interned at Amazon (Prime Video, Summer 2025) and AT&T (AT&T Labs, Fall 2023).

My research interest lies in data-centric machine learning and graph machine learning. I like to explore and advance the entire data lifecycle, including data attribution, data selection and data generation. For applications, I am particually interested in recommendation systems and LLM-based agentic systems.

Before joining UIUC, I received my master’s degree from ECE @ University of Michigan, Ann Arbor and bachelor’s degree from EE @ National Taiwan University. I also worked with Prof. Jiaqi Ma and Prof. Qiaozhu Mei.

News

Jan 27, 2026 One survey on Agentic AI & Reasoning has been released on Arxiv.
Jan 27, 2026 One survey on Data Attribution has been released on SSRN.
Jan 26, 2026 Three papers has been accepted to ICLR 2026.
Jan 15, 2026 I will join Meta as a Research Intern this summer. See you all in Sunnyvale!
Jan 13, 2026 Two papers has been accepted to WWW 2026.
Sep 19, 2025 One paper has been accepted to NeurIPS 2025.
Jan 03, 2025 I will join Amazon as an Applied Scientist Intern this summer. See you all in Seattle!
Sep 21, 2024 One paper has been accepted to NeurIPS D&B Track 2024 (Spotlight).
Apr 30, 2024 I will join IDEA Lab@UIUC as a Ph.D. student and be supervised by Prof. Hanghang Tong this fall!
Sep 30, 2023 One paper has been accepted to NeurIPS 2023.

Selected Publications (*Equal Contribution)

  1. SSRN
    A Survey of Data Attribution: Methods, Applications, and Evaluation in the Era of Generative AI
    Junwei Deng*, Yuzheng Hu*, Pingbang Hu*, Ting-Wei Li*, and 7 more authors
    2025
    PDF
  2. Arxiv
    Agentic Reasoning for Large Language Models
    Tianxin Wei*, Ting-Wei Li*, Zhining Liu*, Xuying Ning, and 7 more authors
    arXiv preprint arXiv:2601.12538, 2026
    arXiv Project
  3. ICLR
    Graph Homophily Booster: Rethinking the Role of Discrete Features on Heterophilic Graphs
    Ruizhong Qiu*, Ting-Wei Li*, Gaotang Li, and Hanghang Tong
    In International Conference on Learning Representations , 2026
    PDF
  4. ICLR
    Continual Low-Rank Adapters for LLM-based Generative Recommender Systems
    Hyunsik Yoo, Ting-Wei Li, SeongKu Kang, Zhining Liu, and 3 more authors
    In International Conference on Learning Representations , 2026
    arXiv
  5. NeurIPS
    Graph Data Selection for Domain Adaptation: A Model-Free Approach
    Ting-Wei Li, Ruizhong Qiu, and Hanghang Tong
    Advances in Neural Information Processing Systems, 2025
    PDF Poster
  6. NeurIPS
    dattri: A Library for Efficient Data Attribution
    Junwei Deng*, Ting-Wei Li*, Shiyuan Zhang, Shixuan Liu, and 6 more authors
    Advances in Neural Information Processing Systems, 2024
    PDF Code
  7. Arxiv
    Efficient Ensembles Improve Training Data Attribution
    Junwei Deng*, Ting-Wei Li*, Shichang Zhang, and Jiaqi Ma
    arXiv preprint arXiv:2405.17293, 2024
    arXiv
  8. NeurIPS
    A Metadata-Driven Approach to Understand Graph Neural Networks
    Ting-Wei Li, Qiaozhu Mei, and Jiaqi Ma
    Advances in Neural Information Processing Systems, 2023
    PDF Code Poster