Huang, X. (CSE) – Scalable and Verifiable Reasoning for Medical Foundation Models

This PhD research focuses on developing reliable medical foundation models capable of reasoning across textual, visual, and interactive clinical information. The work investigates three complementary directions: improving medical reasoning through test-time scaling, training multimodal medical models with verifiable rewards, and synthesizing high-quality visual question-answering data from biomedical literature using generator-verifier frameworks. Building on these efforts, the proposed research will extend medical language and multimodal models toward agentic systems that can gather evidence, use external tools, integrate multimodal information, and verify decisions over sequential interactions. Overall, this research aims to improve the reliability, efficiency, and transparency of medical AI reasoning while supporting reproducible and human-supervised applications in healthcare.

Event Host: Xiaoke Huang, Ph.D. Student, Computer Science & Engineering

Advisor: Yuyin Zhou

Zoom: https://ucsc.zoom.us/j/8855787311

Passcode: 197379