Azhar Imran Mudassir
Beijing University of Technology, China, IEEE Senior Member

Dr. Azhar Imran is an Associate Professor at the Department of Computer Science, Beijing University of Technology, China, with over 13 years of international research and academic experience. He specializes in Artificial Intelligence, Machine Learning, Data Science, and Image Processing, with research interests spanning intelligent systems, computational intelligence, computer vision, pattern recognition, and data-driven modeling. His work focuses on developing robust, scalable, and interpretable AI solutions applicable across diverse domains. He has published more than 100 research articles in high-impact journals, including IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Image Processing, and Pattern Recognition, accumulating over 4000 citations. He has been invited as a keynote speaker at prominent international conferences, including ICBDDM-24, ICCTEC-25, CCET-25, CVIT-24, ICDSP-24, and CVIT-23, highlighting his global recognition in AI and machine vision. As a Senior Member of IEEE, he serves on the editorial boards of multiple high-impact journals, including IEEE Access, MDPI Cancers, Applied Sciences, Springer Visual Computer, Taylor & Francis Biomedical Imaging & Visualization, and Multimedia Tools & Applications, and has acted as a guest editor for IEEE Transactions on Industrial Informatics and IEEE Sensors Journal. His contributions have been recognized with several prestigious awards, including the Young Marconi Award, Prime Minister's Science and Technology Award, the Embassy Honored Award from the Pakistan Embassy in Beijing, and the Outstanding Researcher and Excellence in Teaching Awards from Beijing University of Technology. He is also recognized as a ScholarGPS top 1% scientist worldwide. Through his interdisciplinary research, Dr. Imran continues to advance intelligent, interpretable, and socially impactful AI systems, shaping the next generation of computational and vision technologies.
Speech Title:Hybrid Deep Learning and Computational Optics for Explainable Medical and Machine Vision Systems
Abstract:
The convergence of computational optics and machine vision is enabling a paradigm shift in intelligent imaging systems. In this keynote, I present our research contributions on hybrid deep learning frameworks that integrate image formation principles with data-driven representation learning to achieve robust, scalable, and interpretable visual understanding. Our work focuses on medical image analysis and ECG image-based multi-class diagnosis, where EfficientNet-based and convolutional architectures are systematically designed to handle complex classification tasks under real-world constraints. Key publications informing this keynote include “Automated Multi-Class ECG Diagnosis from Image Representations Using EfficientNet and Grad-CAM” and other recent work in medical image feature learning.
A distinguishing contribution of this research lies in embedding explainability into deep models through attention mechanisms and gradient-based visualization techniques, such as Grad-CAM, allowing transparent and clinically meaningful interpretation of predictions. To address challenges including noise, domain variability, and limited annotated data, we incorporate computational imaging priors alongside transfer learning and domain adaptation strategies, significantly improving model generalization and stability.
Extensive experimental evaluations demonstrate that the proposed hybrid frameworks consistently outperform conventional deep learning approaches in terms of accuracy, robustness, and interpretability across diverse datasets. Looking ahead, the keynote highlights emerging directions, including multimodal learning, transformer-based vision architectures, and explainable AI, aiming to establish trustworthy next-generation machine vision systems. These contributions underscore the critical role of unifying computational imaging and machine learning to advance applications in healthcare diagnostics, intelligent surveillance, and large-scale visual analytics.

TBA......