I’m a Postdoctoral Fellow at Queen’s University’s Centre for Neuroscience Studies & Ingenuity Labs Research Institute, where I develop AI pipelines for epilepsy detection and presurgical decision-making using arterial spin labeling MRI and foundational models for neurological disease prediction. I have also been a Postdoctoral Fellow in ECE & Ingenuity Labs at Queen’s since Apr 2023, working on hierarchical time-series representation learning and multi-domain EEG modeling for cognitive-load classification. From Sept 2021–Mar 2023, I was at the KNU-LG Convergence Research Center in South Korea, building Transformer-based ICU outcome predictors and AI clinical-decision support systems. I earned my PhD (with Best Thesis Award) in Electronic & Electrical Engineering from Kyungpook National University in August 2021, where I focused on low-shot, long-tailed learning for medical imaging and time-series prediction.

Profile Summary

• Conduct research in medical artificial intelligence, computer vision, foundation models, long-tailed distribution learning, multimodal learning, and time-series prediction, focusing on advancing technical expertise in artificial intelligence and contributing actively to the research community.

• Authored over 38 publications, including journal articles, conferences, and patents, featured in venues like NeurIPS, IEEE Transactions, and Elsevier, accumulating 830+ citations, with an h-index of 16.

• Contributed to securing research funding and grants, supporting the expansion and development of innovative projects.

• Co-supervised 12 researchers (4 PhD and 8 MSc students), providing mentorship to support their academic and professional development.

Research Interests

• Deep Learning & Foundation Models: CNNs, Transformers, self-supervised & contrastive methods
• Medical & Biomedical AI: MRI, EEG, time-series forecasting, anomaly detection
• Long-Tailed & Low-Shot Learning: Imbalance regularization, meta-loss methods
• Multimodal Fusion: Vision, kinematics, clinical signals, text
• Computer Vision & Imaging: Hippocampal sclerosis measurement, arterial spin labeling asymmetry
• Applied Machine Learning: Clinical decision support, ICU outcome prediction, generative adversarial frameworks