About me
I am a Ph.D. candidate from the Institute for Health Informatics at University of Minnesota, working with Prof. Rui Zhang. Before that, I received my MBBS degree (equivalent to U.S. MD) from Fudan University in 2021.
Research Interests
My research focuses on medical informatics, with a particular emphasis on developing and applying Natural Language Processing (NLP) techniques for healthcare and biomedical data. I am passionate about extracting meaningful insights from large volumes of clinical and biomedical text to support evidence generation, enable knowledge discovery, and improve health outcomes.
🤝 Open to collaboration opportunities in healthcare AI, clinical informatics, and biomedical NLP
I am broadly interested in both academic and industry research opportunities related to NLP, clinical informatics, and data-driven healthcare applications.
Research Highlights
🔬 Clinical NLP & Information Extraction
Developing advanced natural language processing systems to automatically extract structured information from unstructured clinical notes, enabling better clinical decision support and patient care analytics.
📊 Biomedical Knowledge Discovery
Creating innovative methods for automated knowledge extraction from biomedical literature and electronic health records, facilitating faster identification of disease mechanisms, treatment relationships, and clinical insights.
🏥 Real-World Evidence Generation
Building AI systems that transform messy, real-world clinical data into actionable evidence for healthcare improvements, regulatory decisions, and clinical research.
News
Aug 2025 One paper accepted by EMNLP 2025.
June 2025 One paper accepted by Neurology Clinical Practice.
June 2025 One paper accepted by AMIA 2025.
May 2025 One paper accepted by ACL 2025.
Jan 2025 One paper accepted by NAACL 2025.
Selected Skills & Tools
Programming & NLP: Python, PyTorch, Hugging Face Transformers, spaCy, BERT, GPT, Clinical BERT
Healthcare Data: Electronic Health Records (EHR), MIMIC, Clinical Text Mining, Medical Ontologies (UMLS, SNOMED)
Research Methods: Deep Learning, Transfer Learning, Few-shot Learning, Multi-task Learning, Model Evaluation
Feel free to reach out via LinkedIn or email if you’d like to discuss research collaborations or opportunities!