Saqib Qamar | Artificial Intelligence | Best Researcher Award

Published on by Engineering Scientist

 Dr. Saqib Qamar |Artificial Intelligence
| Best Researcher Award

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.

📊 Publication Top Notes:

📘 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 💡

Mohd Usama | Machine Learning | Best Researcher Award

Published on by Engineering Scientist

Assist. Prof. Dr. MohdUsama|MachineLearning
|Best Researcher Award

Postdoctoral Researcher at Umea University, Sweden Sweden.

Dr. Mohd Usama is a Postdoctoral Researcher at the Department of Diagnostics and Intervention, Umea University, Sweden. He holds a Ph.D. in Computer Science from Huazhong University of Science and Technology, China, focusing on deep learning for disease prediction and sentiment analysis. His research bridges artificial intelligence and medical imaging, particularly using GANs for domain adaptation and plaque detection in ultrasound imagery. With a solid teaching and research background across reputed institutions in India, he has significantly contributed to developing AI-based clinical decision support systems. His scholarly work, practical innovation, and interdisciplinary expertise make him highly suitable for the Best Researcher Award, exemplifying excellence in research, innovation, and educational service in the domains of biomedical engineering and artificial intelligence.



🌍 Professional Profile:


Scopus


Orcid


🏆 Suitability for the Best Researcher Award


 






































Dr. Mohd Usama exemplifies the qualities of a top-tier researcher through his impactful contributions to AI-driven medical imaging and clinical decision support systems. Currently a Postdoctoral Researcher at Umea University, Sweden, his work on generative adversarial networks for ultrasound-based atherosclerosis risk assessment addresses critical challenges in healthcare diagnostics. His strong academic foundation, interdisciplinary approach, and global research collaborations demonstrate exceptional innovation and dedication. Dr. Usama’s ability to translate deep learning research into real-world clinical applications, alongside a consistent record of teaching, publishing, and mentoring, positions him as a leader in his field. His scientific rigor, creativity, and societal impact make him a highly deserving candidate for the Best Researcher Award.






































🎓 Education 


Dr. Usama earned his Ph.D. in Computer Science from Huazhong University of Science and Technology, China (2016–2020), with a dissertation on “Recurrent Deep Learning for Text Processing with Application to Disease Prediction and Sentiment Analysis,” supervised by Prof. Min Chen. He completed his Master’s in Computer Science and Applications (71.78%, First Division) from Aligarh Muslim University (2013–2016), focusing on cloud-based electric vehicle charging management. His undergraduate degree is a B.Sc. (Hons) in Statistics (71.07%, First Division), also from Aligarh Muslim University (2009–2012), with a thesis on students’ opinions on the Ombudsman Bill in India. His academic journey reflects a blend of statistical foundations, computing applications, and interdisciplinary insights, crucial for innovative AI research in biomedical domains.


🏢 Work Experience 


Dr. Mohd Usama has served as a Postdoctoral Researcher at Umea University, Sweden (Dec 2022–Present), contributing to AI-powered clinical decision support systems and generative models for carotid ultrasound imaging. Previously, he worked as an Assistant Professor at the University of Petroleum and Energy Studies (2022), Kalasalingam Academy of Research and Education (2021–2022), and Madanapalle Institute of Technology and Science (2020–2021). He taught various courses including Deep Learning, Algorithms, Programming, and Information Security. His work spans both academia and research, with a deep engagement in curriculum development and applied machine learning. His experience in medical imaging research and teaching demonstrates a strong integration of theoretical and practical knowledge, making him a well-rounded and impactful scholar.


🏅 Awards and Honors 


Dr. Mohd Usama has been recognized for his innovative interdisciplinary research contributions at the intersection of artificial intelligence and healthcare. He received prestigious academic scholarships for his doctoral studies in China and earned consistent recognition throughout his academic career. He has been invited to deliver expert lectures and guest talks on AI, deep learning, and statistical computing at various institutions. His role in international collaborative projects on ultrasound imaging and disease prediction further demonstrates his global impact. As a frequent reviewer for reputed journals and contributor to academic forums, he maintains high standards of scholarly excellence. These achievements, coupled with his dedication to knowledge dissemination and impactful research, position him as a strong candidate for the Best Researcher Award.


🔬 Research Focus 
































































Dr. Mohd Usama’s research lies at the convergence of artificial intelligence, deep learning, and medical imaging. His work primarily involves the use of generative adversarial networks (GANs) to address domain adaptation, noise reduction, and feature interpolation in carotid ultrasound images. He develops AI-powered clinical decision support systems to enhance subclinical atherosclerosis risk prediction and ultrasound diagnostics. His doctoral research explored recurrent deep learning for text analysis in healthcare applications. He is also keenly interested in disease modeling, natural language processing, and sentiment analysis within clinical contexts. Dr. Usama’s work emphasizes real-world application of machine learning in healthcare, contributing to early diagnosis and precision medicine through robust, data-driven solutions, reinforcing his value as a research innovator.




























































































📊 Publication Top Notes:


    

  1. 

    Usama, M., Nyman, E., Näslund, U., & Grönlund, C. (2025).
    A domain adaptation model for carotid ultrasound: Image harmonization, noise reduction, and impact on cardiovascular risk markers.
    Computers in Biology and Medicine.
    https://doi.org/10.1016/j.compbiomed.2025.110030
    

    2. 

  2. 

    Usama, M., & Grönlund, C. (2023).
    Carotid Ultrasound Image Denoising Using Low-to-High Image Quality Domain Adaptation.
    The Medical Technology Days (MTD), 2023, Stockholm.
    

    3. 

  3. 

    Singh, A. P., Kumar, S., Kumar, A., & Usama, M. (2022).
    Machine Learning based Intrusion Detection System for Minority Attacks Classification.
    2022 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES).
    https://doi.org/10.1109/cises54857.2022.9844381
    

    4. 

  4. 

    Ahmad, B., Usama, M., Ahmad, T., Khatoon, S., & Alam, C. M. (2022).
    An ensemble model of convolution and recurrent neural network for skin disease classification.
    International Journal of Imaging Systems and Technology, 32(1), 15–24.
    https://doi.org/10.1002/ima.22661
    

    5. 

  5. 

    Ahmad, B., Usama, M., Huang, C. M., Hwang, K., Hossain, M. S., & Muhammad, G. (2020).
    Discriminative Feature Learning for Skin Disease Classification Using Deep Convolutional Neural Network.
    IEEE Access, 8, 39098–39110.
    https://doi.org/10.1109/ACCESS.2020.2975198
    

    6. 

  6. 

    Qamar, S., Jin, H., Zheng, R., Ahmad, P., & Usama, M. (2020).
    A variant form of 3D-UNet for infant brain segmentation.
    Future Generation Computer Systems, 108, 618–628.
    https://doi.org/10.1016/j.future.2019.11.021
    

    7. 

  7. 

    Usama, M., Ahmad, B., Song, E., Hossain, M. S., Alrashoud, M., & Muhammad, G. (2020).
    Attention-based sentiment analysis using convolutional and recurrent neural network.
    Future Generation Computer Systems, 106, 336–347.
    https://doi.org/10.1016/j.future.2020.07.022
    

    8. 

  8. 

    Usama, M., Ahmad, B., Xiao, W., Hossain, M. S., & Muhammad, G. (2020).
    Self-attention based recurrent convolutional neural network for disease prediction using healthcare data.
    Computer Methods and Programs in Biomedicine, 187, 105191.
    https://doi.org/10.1016/j.cmpb.2019.105191
    

    9. 

  9. 

    Ahmad, P., Jin, H., Qamar, S., Zheng, R., Jiang, W., Ahmad, B., & Usama, M. (2019).
    3D Dense Dilated Hierarchical Architecture for Brain Tumor Segmentation.
    Proceedings of the 2019 4th International Conference on Big Data and Computing (ICBDC).
    https://doi.org/10.1145/3335484.3335516
    

    10. 

  10. 

    Ahmad, B., Usama, M., Lu, J., Xiao, W., Wan, J., & Yang, J. (2019).
    Deep Convolutional Neural Network Using Triplet Loss to Distinguish the Identical Twins.
    2019 IEEE Globecom Workshops (GC Wkshps).
    https://doi.org/10.1109/GCWkshps45667.2019.9024704
    

    11. 

  11. 

    Usama, M., Xiao, W., Ahmad, B., Wan, J., Hassan, M. M., & Alelaiwi, A. (2019).
    Deep Learning Based Weighted Feature Fusion Approach for Sentiment Analysis.
    IEEE Access, 7, 140361–140373.
    https://doi.org/10.1109/ACCESS.2019.2940051
    

    12. 

  12. 

    Usama, M., Ahmad, B., Yang, J., Qamar, S., Ahmad, P., Zhang, Y., Lv, J., & Guna, J. (2019).
    Equipping recurrent neural network with CNN-style attention mechanisms for sentiment analysis of network reviews.
    Computer Communications, 149, 111–121.
    https://doi.org/10.1016/j.comcom.2019.08.002
    

    13. 

  13. 

    Hao, Y., Usama, M., Yang, J., Hossain, M. S., & Ghoneim, A. (2019).
    Recurrent convolutional neural network based multimodal disease risk prediction.
    Future Generation Computer Systems, 98, 296–304.
    https://doi.org/10.1016/j.future.2018.09.031
    

    14.