Events

This thesis explores iterative methods for solving elliptic partial differential equations (PDEs), which are used in computational fluid dynamics (CFD) to model a wide range of physical phenomena. The primary application of interest here is self-gravity, modeled by Poisson’s equation. Although many numerical approaches exist, including direct matrix inversion, FFT-based methods, and classical iterative methods such as Jacobi and Gauss-Seidel, these approaches involve tradeoffs in computational cost, scalability, implementation complexity, and adaptability to changing boundary conditions and problem configurations. Therefore, we introduce SORh, a simple and efficient relaxation method derived from a hyperbolic reformulation of Poisson’s equation. SORh generalizes classical successive […]

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 […]

Modern autonomous-driving models increasingly rely on learned representations and generated reasoning to interpret complex scenes and produce predictions or actions. However, it remains unclear what information these models encode, how that information is exposed through common interpretation methods, and whether their stated reasoning meaningfully influences their behavior. This research investigates these questions across motion-forecasting and vision-language-action models. Event Host: Manasi Pawar, Ph.D. Student, Computer Science & Engineering  Advisor: Leilani Gilpin Zoom: https://ucsc.zoom.us/j/92653015420?pwd=xEyOcy5ZuTcHN04La9w2KP1cmVY1ao.1 Passcode: 309299