Master's student at UT Austin
I work on reliable AI for health.
I am a master's student at The University of Texas at Austin. I conduct research at the AI Health Lab with Prof. Ying Ding, and at Dell Medical School's Department of Internal Medicine with Dr. W. Michael Brode.
In summer 2026, I was a research intern in Mayo Clinic's Department of Artificial Intelligence and Informatics. My work develops adaptive clinical agents, controllable virtual patients, and evidence-grounded AI systems for healthcare.
I am currently seeking Ph.D. opportunities.
I study how LLM-based agents reason, learn from feedback, and support healthcare decisions while remaining useful, interpretable, and trustworthy.
My work spans clinical-agent learning, affective computing, evidence appraisal, patient simulation, and evidence-grounded clinical decision support. I pair model development with careful ablation, error analysis, and human-in-the-loop evaluation.
Before joining UT Austin, I earned a Bachelor of Engineering in Vehicle Engineering from Tongji University. That training continues to shape the systems-oriented way I approach clinical informatics and AI for healthcare research.
Current work on clinical agents that learn from feedback, controllable virtual patients, and rigorous evaluation of how LLMs use clinical evidence.
A framework for studying how clinical agents revise diagnostic skills after an encounter using source-traceable guideline feedback and stage-wise skill fusion.
Clinical agents · Learning from feedback · Skill fusion
An emotion-directed patient simulator with a closed-loop multi-agent architecture for generating dynamic, interpretable responses in palliative care communication.
Patient simulation · Multi-agent systems · Emotional modeling
An emotion-steering framework that uses prompting and contrastive activation-vector injection to control fear, sadness, and anger in multi-turn clinical dialogues.
Affective computing · Activation steering · Clinical fidelity
A differential-evidence mapping framework that reveals substantial disagreement in clinical evidence appraisal across eight LLMs, even when they reach the same diagnosis.
Clinical reasoning · Evidence appraisal · Robust evaluation
A multi-corpus clinical support system integrating expert guidance, PubMed, ClinicalTrials.gov, and systematic reviews with traceable evidence synthesis.
Retrieval-augmented generation · Clinical NLP · Evidence provenance
AMIA 2026
Y. Wu, T. Du, J. Fang, C. Zhang, S. Admane, Y. Ding
IEEE ICHI 2027 · Submitted
Y. Wu, P. Li, J. Fang, C. Tao, Y. Ding
This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.
IEEE ICHI 2027 · Submitted
Y. Wu, R. Hosseinpour, M. Bareither, T. Du, C. Pratt, Y. Ding
This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.
ACL Rolling Review · Oct 2026
J. Xu, Y. Xiong, Y. Wu, J. Fang, E. Kang, Y. Han, P. Wang, P. Li, Y. Ding
ML4H 2026 · Submitted
J. Fang, R. Chen, Y. Wu, J. Xu, X. Yang, J. Yu, W. Shi, Y. Ding, Y. Zhang
DAIH @ COLM 2026
Y. Wu, P. DiGiacomo, Y. Ding, W. Brode
NeurIPS 2026 Workshop
X. Yang, Z. Sha, J. Li, J. Yu, Y. Sun, M. Zhao, J. Fang, X. Guo, Y. Wu, X. Hu, Y. Luo, Q. Liu, Z. Wang
ACTS 2027 · Abstract submitted
W. M. Brode, Y. Wu, P. DiGiacomo, J. Quilantan Jr., A. Raghav, M. Verduzco-Gutierrez, J. J. Li, S. Saiyed, Y. Ding
Jan 2026-present
AI Health Lab, UT Austin
Clinical agents learning from feedback, emotion-aware virtual patients, and rigorous evaluation of clinical evidence appraisal.
May-Aug 2026
Mayo Clinic · Jacksonville, Florida
Post-training LLMs for cardiovascular risk prediction and agentic target-trial emulation.
Oct 2025–Jun 2026
Dell Medical School, UT Austin
Evidence-grounded clinical decision support for Long COVID.
2025–2027
The University of Texas at Austin
GPA: 4.0 / 4.0
2020–2025
Tongji University
GPA: 3.97 / 4.0
I welcome conversations about clinical informatics, AI for healthcare, clinical collaborations, and Ph.D. opportunities.