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Dr. Nauman Ullah Gilal
Research Associate
Educational Qualifications
PhD in Computer Science and Engineering
M.Eng in Information and Communication Engineering
Entity
Qatar Computing Research Institute
Division
Arabic Language Technologies
Biography
Dr. Nauman Gilal is a Research Associate in the Arabic Language Technologies group at Qatar Computing Research Institute (QCRI) of Hamad Bin Khalifa University. He earned his PhD in Computer Science and Engineering from Hamad Bin Khalifa University, computer vision with a focus on learning from noisy labels and imbalanced data. His doctoral research applied interdisciplinary knowledge to real-world problems across diverse domains, including food computing, and medical imaging (skin cancer and pathology).
At QCRI, he engages in developing AI-powered tools for pathology diagnostics and education, using large multimodal models (MLLMs) to analyze whole slide images (WSIs). His work includes benchmarking and fine-tuning vision language models for healthcare applications and building web-based platforms for real-time image analysis and query capabilities. His research interests focus on multimodal deep learning, medical imaging, and AI-driven tools aimed at advancing healthcare diagnostics and education.
PhD in Computer Science and Engineering
Hamad Bin Khalifa University
2024
M.Eng in Information and Communication Engineering
Harbin Institute of Technology, China
2020
BS in Computer Science
University of Sindh, Pakistan
2015
- Large Multimodal Models (LMMs)
- Learning from noisy label (LNL)
- Computer vision
- Histopathology
Research Associate
Arabic Language Technology, Hamad Bin Khalifa University
2024 - Present
Teacher Assistance
College of Science and Engineering, Hamad Bin Khalifa University
2020 - 2023
Gilal, N. U., Al-Thelaya, K., Al-Saeed, J. K., Abdallah, M., Schneider, J., She, J., & Agus, M. (2024). Evaluating machine learning technologies for food computing from a data set perspective. Multimedia Tools and Applications, 83(11), 32041–32068.
Gilal, N. U., Ahmed, S. A. M., Schneider, J., Househ, M., & Agus, M. (2023). Mobile dermatoscopy: Class imbalance management based on blurring augmentation, iterative refining, and cost-weighted recall loss. Journal of Image and Graphics, 11(2).
Gilal, N. U., Qaraqe, M., Schneider, J., & Agus, M. (2024). Autocleandeepfood: Auto-cleaning and data balancing transfer learning for regional gastronomy food computing. The Visual Computer. Advance online publication.
Majeed, F., Gilal, N. U., Al-Thelaya, K., Yang, Y., Agus, M., & Schneider, J. (2024). MV-Soccer: Motion-vector augmented instance segmentation for soccer player tracking. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 3245–3255).
Al-Thelaya, K., Agus, M., Gilal, N. U., Yang, Y., Pintore, G., Gobbetti, E., & Schneider, J. (2021). InShaDe: Invariant shape descriptors for visual 2D and 3D cellular and nuclear shape analysis and classification. Computers & Graphics, 98, 105–125.
Complete Publication Listing(s): Google Scholar
- Best Student Research Paper Award at the International Conference on Innovation and Technological Advances for Sustainability (ITAS) 2023
- Chinese Government Scholarship awarded by China Scholarship Council (2018-2020)