Mingchen Li
Portrait of Mingchen Li

Mingchen Li

Ph.D. Student

Computer Science, University of Massachusetts Amherst, U.S.

mingchenli@umass.edu

I am a Ph.D. student in Computer Science at the University of Massachusetts Amherst. My research focuses on large language models, agentic retrieval, reasoning, and AI for biomedicine.

My primary research centers on agentic retrieval, reinforcement learning, and large language model pretraining for specialized domains, including high-stakes medical settings such as cancer, opioid overdose, and diagnosis prediction, as well as information retrieval, retrieval-augmented generation, and graph-based RAG frameworks. More broadly, I am interested in building agentic AI systems that can search, plan, retrieve evidence, and solve complex tasks reliably in high-stakes environments. A central goal of my work is to develop systems that are not only effective, but also more robust, interpretable, and reliable in open-ended settings. I am also interested in extending these methods to the medical domain, where accurate evidence gathering and reasoning are especially important.

My recent work spans several interconnected directions:

  • Developing and studying large language model systems for multi-step reasoning, complex problem solving, and embodied AI.
  • Improving agentic retrieval pipelines so models can retrieve the right evidence at the right time.
  • Building tool-using LLM agents that are more robust, interpretable, and effective in open-ended environments.

Recent Preprints

  • Efficient and Effective Internal Memory Retrieval.

    Mingchen Li, Jiatan Huang, Zonghai Yao, Hong Yu

    Year: 2026

    • Selected Publications

    For the latest and complete publication list, please also visit my Google Scholar profile.

    1. Cancerllm: A large language model in cancer domain

      Mingchen Li, Zaifu Zhan, Jiatan Huang, Jeremy Yeung, Kai Ding, Anne Blaes, Steven Johnson, Hongfang Liu, Hua Xu , Rui Zhang

      NPJ Digital Medicine

      Year: 2026

    2. Benchmarking retrieval-augmented large language models in biomedical nlp: Application, robustness, and self-awareness

      Mingchen Li, Zaifu Zhan, Han Yang, Yongkang Xiao, Jiatan Huang, Rui Zhang

      Science Advance

      Year: 2026

    3. Biomedrag: A retrieval augmented large language model for biomedicine

      Mingchen Li, Halil Kilicoglu, Hua Xu, Rui Zhang

      Journal of Biomedical Informatics (JBI) 162, 104769

      Year: 2025

    4. RAMIE: retrieval-augmented multi-task information extraction with large language models on dietary supplements

      Zaifu Zhan, Shuang Zhou, Mingchen Li, Rui Zhang

      Journal of the American Medical Informatics Association (JAMIA) 32 (3), 545-554

      Year: 2025

    5. Large language model synergy for ensemble learning in medical question answering: design and evaluation study

      Han Yang, Mingchen Li, Huixue Zhou, Yongkang Xiao, Qian Fang, Shuang Zhou, Rui Zhang

      Journal of Medical Internet Research 27, e70080

      Year: 2025

    6. RT: a Retrieving and Chain-of-Thought framework for few-shot medical named entity recognition

      Mingchen Li, Huixue Zhou, Han Yang, Rui Zhang

      Journal of the American Medical Informatics Association (JAMIA) 31 (9), 1929 ...

      Year: 2024

    7. LEAP: LLM instruction-example adaptive prompting framework for biomedical relation extraction

      Huixue Zhou, Mingchen Li, Yongkang Xiao, Han Yang, Rui Zhang

      Journal of the American Medical Informatics Association (JAMIA) 31 (9), 2010 ...

      Year: 2024

    8. Fuselinker: Leveraging Llm's Pre-Trained Text Embeddings and Domain Knowledge to Enhance Gnn-Based Link Prediction on Biomedical Knowledge Graphs

      Yongkang Xiao, Shuang Zhang, Huixue Zhou, Mingchen Li, Han Yang, Rui Zhang

      Journal of Biomedical Informatics (JBI)

      Year: 2024

    9. A condensed transition graph framework for zero-shot link prediction with large language models

      Mingchen Li, Chen Ling, Rui Zhang, Liang Zhao

      ICDM 2024

      Year: 2024

    10. Understand the Dynamic World: An End-to-End Knowledge Informed Framework for Open Domain Entity State Tracking

      Mingchen Li, Lifu Huang

      SIGIR 2023

      Year: 2023

    11. A Review of Reinforcement Learning for Natural Language Processing, and Applications in Healthcare

      Ying Liu, Haozhu Wang, Huixue Zhou, Mingchen Li, Yu Hou, Shuang Zhou, Fang Wang, Rama Hoetzlein, Rui Zhang

      Journal of the American Medical Informatics Association (JAMIA)

      Year: 2023

    12. Semantic Structure based Query Graph Prediction for Question Answering over Knowledge Graph

      Mingchen Li, Shihao Ji

      COLING 2022

      Year: 2022

    13. A Hierarchical N-Gram Framework for Zero-Shot Link Prediction

      Mingchen Li, Junfan Chen, Samuel Mensah, Nikolaos Aletras, Xiulong Yang, Yang Ye

      EMNLP 2022 (Finding track)

      Year: 2022

    Experience

    • PhD Student, Computer Science, UMass Amherst. 09.2024 - Present
    • Researcher, University of Minnesota Twin Cities. 01.2023 - 08.2024
    • Teaching Assistant, Georgia State University, Deep Learning (CSC 8850). 09.2021 - 12.2021
    • Research Assistant, Georgia State University. 01.2021 - 05.2021

    Service

    • Program Committee: ACL 2023, IEEE ICHI 2023, EMNLP Industry Track 2023
    • Reviewer: EMNLP 2022, EMNLP 2023, NAACL 2024, Artificial Intelligence in Medicine, Engineering Applications of Artificial Intelligence, Expert Systems with Applications, Information Processing & Management, International Journal of Medical Informatics, Npj Health Systems, Journal of Artificial Intelligence Research
    • Other Service: Central Functions Committee in SCD 2022, Student Volunteer at EMNLP 2022

    Contact

    Google Scholar: scholar profile

    GitHub: ToneLi

    Email: mingchenli@umass.edu