Seoul, South Korea
Graduated with MMath degree from the University of Oxford. Mainly interested in brdging the gap between theoretical and clinical neuroscience through machine learning, mathematics, and neuroimaging.
- Managed the AI engine codebase of DeepBrain and designed an efficient product pipeline. - Spearheaded the short and long-term product planning of VUNO-Med DeepBrain, including research objectives and writing grant applications, in collaboration with VUNO's software engineering team and domestic & global marketing teams. - Delivered presentations introducing VUNO-Med DeepBrain software, including a showcase featuring VUNO-Med DeepBrain and its future direction at RSNA 2023.
- Improved T1 MRI intracranial volume segmentation model for more accurate atrophy quantification - Introduced the multi-task multiple instance learning architecture for WMH segmentation and Fazekas scale prediction - Organized a research seminar series to establish a collaborative environment in VUNO’s R&D Center.
SNU AI Institute Summer Internship @ Clinical Cognitive Neuroscience Center (PI: Jun Soo Kwon) Project title: A novel MMN classification method using machine learning and the signature transform
- Implemented a theoretical framework to evaluate performance how much a reinforcement model imitates human behaviour. - Developed an algorithm to categorize reinforcement learning models with respect to Jensen-Shannon Divergence of their policy distributions.
- Conducted in silico experiments to determine the difference in dynamics of various neuronal models. - Proved the Depolarizing After Potential (DAP) model exhibits shorter opening in potassium channel than the original Hodgkin-Huxley model.