I am a physician–scientist working at the intersection of medical imaging, machine learning, and clinical decision support. My work focuses on developing AI systems that are interpretable, uncertainty-aware, and aligned with real clinical workflows—spanning 3D imaging analysis, multimodal modeling, and structured information extraction.

I currently work in academic medical research, collaborating with radiologists, data scientists, and clinicians to design end-to-end pipelines that translate technical advances into meaningful clinical value. This includes building imaging segmentation and feature extraction pipelines, developing large-language-model systems for structured reporting, and incorporating causal reasoning and predictive modeling to support evidence-based decision-making.

Beyond imaging AI, my interests include longitudinal risk modeling, population-level analytics, digital health tools, and the integration of equity principles into clinical informatics. I am committed to translational impact—bridging technology, medicine, and global health to make advanced AI capabilities accessible across diverse healthcare settings.

Research Philosophy: AI in healthcare should be clinically grounded, transparent, and designed for real-world use. I strive to build systems that complement clinician expertise, enhance decision-making, and improve patient care in a safe and responsible way.