Summary
Nhu-tai Do is a lecturer and postdoctoral-trained researcher with eight years of experience bridging academia and applied AI, currently teaching at Saigon University and previously holding research roles at Chonnam National University. He holds a PhD in Artificial Intelligent Convergence and has published on deep learning for medical image analysis, video-based emotion recognition, hand-gesture and face tracking, with notable challenge placements (KERC, AffWild2, EmotiW). His recent work focuses on knee bone tumor prognosis from MRI sequences and video understanding for assistive captioning, alongside an ongoing interest in 3D reconstruction from X-rays. An educator with a strong record mentoring competitive student teams, he combines rigorous research with practical teaching across programming, algorithms and databases. Beyond publications, he actively translates deep-learning solutions into medical and accessibility applications, reflecting a pragmatic drive to deploy research in real-world clinical and assistive contexts.
8 years of coding experience
4 years of employment as a software developer
Master of Engineering in Information Technology Management, Information Technology Management, GPA: 84.8 / 100.0 Thesis: 94.0 / 100.0, Master of Engineering in Information Technology Management, Information Technology Management, GPA: 84.8 / 100.0 Thesis: 94.0 / 100.0 at International University - VNU HCMC
Bachelor of Information Technology, Information Technology, GPA: 8.71 / 10.0 Thesis: 10.0 / 10.0, Bachelor of Information Technology, Information Technology, GPA: 8.71 / 10.0 Thesis: 10.0 / 10.0 at Ho Chi Minh City University of Foreign Languages - Information Technology, Vietnam
Doctor of Philosophy in Artificial Intelligent Convergence, Computer Vision, Deep Learning, Pattern Recognition, Medical Image Analysis, 4.5 / 4.5, Doctor of Philosophy in Artificial Intelligent Convergence, Computer Vision, Deep Learning, Pattern Recognition, Medical Image Analysis, 4.5 / 4.5 at Chonnam National University
English, Vietnamese