Lab

Research Interests

AI for Health Clinical Informatics Personalized Medicine Large Language Models Agentic AI Tool-Augmented Learning Retrieval-Augmented Learning Reasoning Data-Centric AI
  • Tool-Augmented LLM Agents for Clinical Research and Biomedical Discovery
    Developing LLM agents that perform structured question interpretation, retrieval-augmented planning, governed tool selection, code synthesis, sandboxed execution, and self-verification with clinician checkpoints. Building agentic training environments with reinforcement learning so agents practice safe tool use, recover from execution errors, and improve from human and environmental feedback.
  • Reasoning-Aware Retrieval-Augmented LLMs for Evidence-Based Medicine
    Unifying reasoning and retrieval so that LLM outputs are auditable and decision-ready. Formalizing reasoning-aware retrieval where agents decompose clinical questions, plan evidence needs, retrieve and ground each step, and verify intermediate claims through self-play and collaborative supervision. Developing domain-tuned biomedical retrievers and EHR-aware augmentation that supply trustworthy context with citations, rationales, and uncertainty signals.
  • Responsible AI for Transparent and Trustworthy Clinical Models
    Ensuring that AI systems for clinical decision support are trustworthy, explainable, and fair. Addressing challenges of data quality, interpretability, and bias in medical AI through fairness-aware modeling, knowledge-infused data generation, and interpretable attention mechanisms to establish accountable and equitable AI deployment in healthcare.
  • AI-Powered Precision Medicine for Personalized Patient Care
    Advancing AI methods to support precision medicine by combining clinical data with causal inference techniques to tailor predictions and treatments to each patient. Developing models for individualized outcome prediction, personalized causal graph learning, and NLP techniques to extract social and behavioral determinants of health from clinical notes.

Students

  • Junhui Mi, Ph.D. Student in Biostatistics, UTSW
  • Yi Jiang, Ph.D. Student in Biomedical Engineering, UTSW (co-advised w/ Dr. Yang Xie)
  • Jinrui Fang, Ph.D. Student, UT Austin (co-advised w/ Drs. Ying Ding & Yang Xie)

Openings

I am actively looking for Ph.D. students and Postdoctoral researchers to join my group. I am open to discussing research opportunities with motivated students who have strong backgrounds in machine learning and/or health informatics. Please reach out only if you are ready to devote time and effort in a research project. If interested, please email me at wenqi.shi@utsouthwestern.edu with your CV and a brief description of your research interests.