Assistant Professor at Sohar University, India.
Dr. Saqib Qamar is a dedicated Assistant Professor at Sohar University, Oman, specializing in computer science with expertise in medical image analysis and deep learning. With a Ph.D. from Huazhong University of Science and Technology, China, and postdoctoral research from Sweden’s top institutes—KTH and Umea University—he has demonstrated strong research capabilities through high-quality publications and international collaboration. His academic career spans teaching, curriculum development, and industry experience. Known for his work ethic, innovation, and research productivity, Dr. Qamar has a profound commitment to student success and scientific excellence. His scholarly contributions and interdisciplinary engagement make him a compelling candidate for the Best Researcher Award.
🌍 Professional Profile:
ORCID
Google Scholar
🏆 Suitability for the Best Researcher Award :
Dr. Saqib Qamar’s academic journey reflects consistent excellence and impactful contributions to computer science, particularly in medical image analysis using AI. With experience in global research environments and a record of peer-reviewed publications, he bridges theory with real-world applications. His Ph.D. work on 3D CNNs for brain MRI segmentation was both innovative and practically relevant. As a postdoctoral fellow at KTH and Umea University, he engaged in collaborative, cutting-edge research. He also actively mentors students and contributes to academic discourse. His awards, research leadership, and ongoing projects demonstrate a trajectory of influence, making him highly suitable for the Best Researcher Award.
🎓 Education :
Dr. Saqib Qamar earned his Ph.D. in Computer Science from Huazhong University of Science and Technology, China (2015–2019), with a dissertation focused on 3D CNN models for brain MRI segmentation. His doctoral research integrated deep learning with medical imaging and received the HUST Excellence Award. He completed his Master’s in Computer Science at Aligarh Muslim University, India (2010–2013), where he studied AI, programming, and software engineering, and was awarded a Merit Cum Means scholarship. His academic foundation was laid through a B.Sc. (Hons.) in Statistics from the same university (2007–2010), where he focused on probability, statistics, and linear algebra, graduating with First Division.
🏢 Work Experience :
Dr. Saqib Qamar is currently an Assistant Professor at Sohar University, Oman (2025–present), where he teaches and conducts research in AI and medical image computing. He was previously a postdoctoral fellow at KTH Royal Institute of Technology (2024–2025) and Umea University (2022–2024), Sweden, focusing on machine learning and deep learning applications. At Umea, he also taught deep learning courses. Earlier, he served as Assistant Professor at Madanapalle Institute of Technology and Science, India (2019–2021), where he taught programming and ML subjects. Prior to academia, he worked as a Database Developer at nServices, Delhi (2013–2015), specializing in Oracle-based data systems and programming.
🏅Awards and Honors
Dr. Saqib Qamar has been recognized for his academic performance and research excellence throughout his career. He received the HUST Excellence Award during his Ph.D. at Huazhong University of Science and Technology, acknowledging his exceptional work in medical image segmentation using deep learning. He was also awarded the Merit Cum Means Scholarship by the Government of India during his Master’s studies. His postdoctoral research in Sweden was supported through prestigious international fellowships, and he has contributed to multiple international projects. Additionally, he has received appreciation for teaching excellence and academic service in both India and Oman. His record of honors reflects his dedication to advancing AI and medical informatics research globally.
🔬 Research Focus :
Dr. Saqib Qamar’s research focuses on medical image analysis, deep learning, and 3D convolutional neural networks. His doctoral work centered on brain MRI segmentation, proposing efficient and parallelized CNN architectures. He has expanded his expertise during postdoctoral stints in Sweden, exploring advanced AI techniques in healthcare imaging, neural cell recognition, and explainable AI. His work integrates datasets from MRI and CT scans with machine learning algorithms to enhance diagnostic capabilities. He is also interested in real-time data processing, parallel computing, and interpretable AI models. With an aim to bridge clinical needs with computational innovation, Dr. Qamar’s research contributes significantly to the domains of health informatics and intelligent medical systems.
📘 Techniques of data mining in healthcare: a review
🗓️ Year: 2015 | ✍️ P Ahmad, S Qamar, SQA Rizvi | 📖 International Journal of Computer Applications | 🔢 Cited by: 174 📊
🧠 A variant form of 3D-UNet for infant brain segmentation
🗓️ Year: 2020 | ✍️ S Qamar, H Jin, R Zheng, P Ahmad, M Usama | 📰 Future Generation Computer Systems | 🔢 Cited by: 132 🧬
🦴 CT-based automatic spine segmentation using patch-based deep learning
🗓️ Year: 2023 | ✍️ SF Qadri, H Lin, L Shen, M Ahmad, S Qadri, S Khan, M Khan, SS Zareen, S Qamar | 🧾 International Journal of Intelligent Systems | 🔢 Cited by: 77 🧠
🧠 Context aware 3D UNet for brain tumor segmentation
🗓️ Year: 2020 | ✍️ P Ahmad, S Qamar, L Shen, A Saeed | 📘 MICCAI Brainlesion Workshop | 🔢 Cited by: 57 🧪
🧬 MH UNet: A multi-scale hierarchical based architecture for medical image segmentation
🗓️ Year: 2021 | ✍️ P Ahmad, H Jin, R Alroobaea, S Qamar, R Zheng, F Alnajjar, F Aboudi | 📰 IEEE Access | 🔢 Cited by: 51 🔬
🧴 Dense encoder-decoder–based architecture for skin lesion segmentation
🗓️ Year: 2021 | ✍️ S Qamar, P Ahmad, L Shen | 🧠 Cognitive Computation | 🔢 Cited by: 50 🧪
🧠 HI-Net: Hyperdense Inception 3D UNet for Brain Tumor Segmentation
🗓️ Year: 2021 | ✍️ S Qamar, P Ahmad, L Shen | 📘 Brainlesion: Glioma, MS, Stroke and TBI Workshop | 🔢 Cited by: 46 💡