Dildar Hussain

Dildar Hussain is an Assistant Professor in the Department of Artificial Intelligence Data Science at Sejong University, South Korea, where he has been serving since September 2022. His academic journey includes a Ph.D. in Biomedical Engineering from Kyung Hee University (2019), preceded by an MS in the same field (2014) and a BCS from Kohat University of Science and Technology, Pakistan (2010). Prior to his current position, he held significant roles including Postdoctoral Research Fellow at the Korea Institute for Advanced Study (KIAS), a subordinate institute of KAIST (2019-2022), Research and Development Engineer at YOZMA BMTech CO., LTD (2013-2019), and Research Associate at NUST Pakistan (2010-2012).

His teaching portfolio at Sejong University includes Computer Organization and Architecture, Introduction to Data Analysis, Advance Programming Usage, Computer Vision, Artificial Intelligence and Cyber Security, Basic Coding Based on Computational Thinking, and Introduction to Applied Image Processing.

• Artificial Intelligence and Machine Learning applications in healthcare
• Medical Imaging and DXA image analysis
• Computer Vision and Image Processing techniques
• Biomedical informatics and computer-aided diagnosis
• Automation solutions in healthcare settings
• Natural language processing applications

Hussain's research output shows a strong focus on applying deep learning techniques to medical imaging problems, particularly in osteoporosis detection using DXA imaging systems. His work spans multiple healthcare domains including cancer detection, diabetic retinopathy analysis, and embryo viability assessment. Recent publications (2023-2025) demonstrate expansion into diverse areas like text sentiment analysis, skin lesion diagnosis, and even materials science through computational studies of hydrogen storage materials.

• Kyung Hee University scholarship for Ph.D.

Hussain has advised numerous research projects and collaborated extensively across disciplines, with co-authors from institutions worldwide. His industry experience at YOZMA BMTech significantly contributed to the implementation of machine learning algorithms for DXA image analysis in osteoporosis diagnosis devices. Current research directions include advancing AI techniques for medical diagnostics and exploring applications of deep learning in diverse scientific domains beyond traditional healthcare settings.