Research. Innovation. Collaboration

Md Ariful Islam Mozumder, PhD

I am a dedicated postdoctoral researcher at the Smart Computing Laboratory (SCLab—https://ai-sclab.com/team/), Inje University, Republic of Korea, with a multidisciplinary research background spanning artificial intelligence (AI), healthcare, medical image analysis, computational pathology, computer vision, and intelligent decision-support systems. I also serve as a full-time research mentor for MS and PhD students across the Department of Computer Engineering and the Department of AI & Software Engineering at Inje University.

My primary research focuses on developing advanced and clinically meaningful AI solutions for healthcare, particularly for cancer diagnosis, classification, segmentation, prognosis, and survival prediction. My work has extensively explored weakly supervised and semi-supervised learning, multiple instance learning (MIL), vision transformers, foundation models, multimodal learning, explainable AI, and generative AI. A major part of my doctoral research centered on computational pathology using whole-slide images (WSIs), where I investigated AI-based approaches for cancer subtyping and prognosis across multiple cancer types.

Beyond healthcare, I have participated in several industry-oriented and multidisciplinary AI projects, including wearable sensor and time-series data analysis, real-time manufacturing anomaly detection, computer vision, and intelligent monitoring systems. These experiences have strengthened my interest in translating AI research beyond laboratory experiments into practical, deployable solutions for real-world environments.

Alongside my academic position, I work as an R&D consultant at HUMS LAB, an AI R&D company in Gimhae, Republic of Korea. In this role, I contribute to AI-driven research and development activities and help bridge the gap between academic innovation and industrial implementation.

I have authored and co-authored numerous peer-reviewed journal and international conference publications, particularly in AI-enabled healthcare and cancer research, and have received multiple Best Paper Awards at international conferences. I also actively contribute to the academic community through peer reviewing, conference activities, research collaborations, and student supervision.

International and interdisciplinary collaboration is an important part of my research vision. I am particularly interested in establishing collaborations with researchers and institutions working across AI, medicine, healthcare, biology, pharmaceutical sciences, engineering, and other multidisciplinary domains. My goal is to combine advanced AI methodologies with domain expertise to address meaningful scientific and societal challenges.

Beyond my own research, I am passionate about mentoring young researchers and international students, developing new research ideas, and helping students transform those ideas into high-quality publications and practical applications. My long-term research vision is to advance trustworthy, explainable, and translational AI that connects cutting-edge computational research with real clinical and industrial needs.