Bio

He is currently a postdoctoral fellow in the Nima Lab, where he focuses on pathology data analysis for biomarker quantification in whole slide images (WSIs), leveraging foundation models alongside customized deep learning architectures.

Prior to joining the Nima Lab, he earned a Ph.D. in Computer Science and Engineering from Seoul National University, South Korea and accumulated over a decade of industry experience working with radiology imaging. His expertise spans a wide range of modalities, including 3D MRI, CT, 2D ultrasound, mammograms, and X-rays.

He has played a key role in developing innovative AI-based computer-aided diagnostic tools for industrial applications, successfully translating research concepts into real-world products that enhance diagnostic efficiency and accuracy.

Throughout his career, he has collaborated extensively with radiologists, designing robust AI models and integrating them into user-friendly platforms via customized APIs. These tools enable qualitative evaluation of AI performance beyond numerical metrics, empowering clinicians to make more informed and confident diagnostic decisions.

Interests

Multimodal learning, Medical Image Analysis, Active Learning

Play

I enjoy staying updated with current events, exploring new places, and savoring my favorite foods. I also enjoy baking, cooking, and sharing meals with others. Being in natural surroundings brings me joy and peace.