Scholar
Wenqi Shi
Google Scholar ID: 4qkrZTAAAAAJ
Assistant Professor, University of Texas Southwestern Medical Center
Citations & Impact
Citations
1,049
H-index
16
i10-index
22
Publications
20
Co-authors
17
list available
Publications
Resume (English only)
Academic Achievements
- Developed MedAgentGym (June 2025): an interactive gym-style platform for training LLM agents in code-based medical reasoning
- Awarded NVIDIA Academic Grant (March 2025) to support research on enhancing fundamental agent capabilities of LLMs in biomedicine
- Published multiple high-impact papers, including:
- — 'MedAgentGym: Training LLM Agents for Code-Based Medical Reasoning at Scale' (arXiv)
- — 'WorkForceAgent-R1: Incentivizing Reasoning Capability in LLM-based Web Agents via Reinforcement Learning' (arXiv)
- — 'Collab-rag: Boosting retrieval-augmented generation for complex question answering via white-box and black-box llm collaboration' (COLM'25)
- — 'MedAgentsBench: Benchmarking Thinking Models and Agent Frameworks for Complex Medical Reasoning' (arXiv)
- — 'Fairness artificial intelligence in clinical decision support: Mitigating effect of health disparity' (JBHI'24)
- — 'MedAssist: LLM-Empowered Medical Assistant for Assisting the Scrutinization and Comprehension of Electronic Health Records' (WWW'25)
- — 'Predicting pediatric patient rehabilitation outcomes after spinal deformity surgery with artificial intelligence' (Nature Communication Medicine)
- — Multiple papers on causal inference, clinical decision support, and EHR analysis in journals such as BMC Medical Informatics and Decision Making
- Contributed to MIMIR: A Customizable Agent Tuning Platform for Enhanced Scientific Applications (EMNLP'24)
Background
- Tenure-Track Assistant Professor in the Department of Health Data Science and Biostatistics at UT Southwestern Medical Center
- Faculty member of the Quantitative Biomedical Research Center at UTSW
- Research focuses on the intersection of artificial intelligence (AI) and healthcare, advancing fundamental algorithms and applied systems for precision and personalized medicine
- Special emphasis on pediatric healthcare, cancer, and rare diseases
- Current research directions include: large language models for translational medicine, agentic AI for biomedical discovery, and responsible AI practices to improve clinical research and practice
