PROF. MD. SADIQ IQBAL
Chairman, Department of Computer Science & Engineering
Bangladesh University
Prof. Md. Sadiq Iqbal stands as a distinguished academic leader at the Department of Computer Science & Engineering, where his visionary guidance continues to inspire excellence in teaching, research, and innovation. Under his leadership, the department has cultivated a vibrant academic environment that emphasizes critical thinking, technological advancement, and a strong ethical foundation. Prof. Iqbal’s commitment to academic excellence is reflected in his dedication to nurturing the next generation of engineers not merely as technical experts, but as forward thinking leaders capable of addressing complex global challenges. His efforts have significantly contributed to shaping a curriculum that is both industry-relevant and research-driven, ensuring that students are well-equipped to thrive in an ever-evolving technological landscape.
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Guiding Future Technologists with Vision and Integrity.
About Me
Chairman, Educator & Innovator in Computer Science & Engineering
Prof. Md. Sadiq Iqbal is an accomplished academic and researcher with over 22 years of experience in the field of Computer Science and Engineering. He currently serves as the Chairman of the Department of Computer Science & Engineering and as a Professor at Bangladesh University (BU) in Dhaka. Iqbal’s academic credentials include a PhD from the Dhaka University of Engineering & Technology (DUET), which he is currently pursuing, a Master of Computer Science, and a Bachelor of Computer Engineering, both earned with Excellent results from the National Technical University of Ukraine. His research is broadly focused on cutting-edge technologies, including Machine Learning, Explainable AI (XAI), Network Security, Telemedicine, and Cloud Computing. He has an extensive publication record.
SI Research Areas
A Machine Learning Framework for Identifying Sources of AI-Generated Text
Statistics, Optimization and Information Computing Vol.13, May 2025, pp 2186–2204. International Academic Press
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Performance analysis of classical and quantum support vector machines for diagnosis of chronic kidney disease
Informatics and Health Accepted 31 August 2025
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Machine Learning-Enhanced SPR-Based Optical Biosensor for Cancer Cell Detection
Optics & Laser Technology (ELSEVIER) Volume 192, Part D, December 2025, 113796
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