I am a postdoctoral researcher at UT Southwestern Medical Center working on multimodal foundational AI models for medicine and healthcare. My research spans knowledge distillation, efficient deep learning, brain foundation models, sample efficient learning, 3D pose estimation, and computer vision. I completed my Ph.D. (in CSE), M.S. (in CSE), M.Sc.(in ICT) and B.Sc. (in ICT) at Kyung Hee University, South Korea, supported by the Korean Presidential Scholarship, and Islamic University, Bangladesh, respectively. I am eager to contribute to cutting-edge AI research by leveraging my expertise in artificial intelligence, deep learning, knowledge distillation, preserving prior knowledge and representation, and efficient model design to address key challenges in healthcare and medicine by developing robust multimodal brain foundation models (BFMs), digital twin brain, computer vision, biometrics, causal learning, biomarker detection, and disease prediction. My goal is to develop innovative, scalable solutions through academic research by leveraging modern state-of-the-art AI technologies to enhance model performance in real-world medical scenarios to diagnose diseases by analyzing MRI, CT scan, ECG, EEG, and other multimodal medical data. My research directly tackles problems such as subject-invariant learning, segmentation under distribution shift, multi-modal action understanding, causal learning, active learning, catastrophic forgetting, task transfer, and computational efficiency.

Full list on Google Scholar.

Part-time Lecturer · Dept. of Artificial Intelligence, Kyung Hee University, South Korea · Sep 2025 – Mar 2026

Hands-on notebooks and guides bridging research concepts with practical implementation. All code is open-source and runs on Google Colab (free GPU tier).

More tutorials in progress. Star the GitHub repository to be notified of new releases.