Dr. Sajid Javed
Dr. sajid javed Assistant Professor Computer Science

Contact Information
sajid.javed@ku.ac.ae (02) 312 4353


Dr. Sajid is an assistant professor of computer vision at Khalifa University of Science and Technology, UAE. He is affiliated with Khalifa University Centre of Autonomous Robotics Systems (KUCARS), and the University of Warwick, United Kingdom. Dr. Sajid received his Ph.D. degree in Computer Science and Engineering from the Kyungpook National University, Republic of Korea, and his B.Sc. (Hons) degree in Computer Science from the University of Hertfordshire, United Kingdom. Previously, he has worked as a research fellow at the University of Warwick, University Hospitals Coventry and Warwickshire, United Kingdom, and KUCARS, UAE.

He leads the computer vision group in KUCARS and has extensive experience working on computer vision, machine learning, and artificial intelligence research projects. He has published many high-quality and high-impact factor papers in leading computer vision journals and conferences (e.g., Transactions on Pattern Analysis and Machine Intelligence, Transactions on Image Processing, Transactions on Cybernetics, Medical Image Analysis). His current research interests span the field of computer vision and computational pathology. This includes visual tracking from video sequences, multi-object tracking, object detection, background-foreground modeling, background subtraction, video object segmentation, histology image classification, tissue phenotyping, nucleus detection, and nucleus classification from routine Hematoxylin and Eosin Whole Slide Images. He is also a member of IEEE and ACM.

  • PhD. in Computer Science and Engineering, Republic of Korea
  • B.Sc. (Hons) in Computer Science, U.K.

  • Advanced Computer Vision Paradigms (ECCE630)
  • Artificial Intelligence (COSC604)
  • Deep Learning System Design (ECCE635)
  • Introduction to Human Computer Interfaces (HSEG601)
  • Operating Systems (ECCE354)

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Affiliated Centers, Groups & Labs

Research Interests
  • Computer Vision, Machine Learning, Artificial Interest, Medical Imaging, Computational Pathology, Cancer Imaging
  • Visual Tracking, Object Detection, Video Segmentation, Underwater Video Tracking, Underwater Image Analysis, Cancer Diagnosis, Tissue Classification, Histology Image Analysis

Research Projects

Unveiling the power of computer vision for the underwater environment: In this project, our main objectives are three folds: 1) Since a large-scale underwater video analysis dataset is missing and this is because of the expensive cost of underwater image acquisition, therefore, our first objective is to propose a new publicly available large-scale underwater dataset with tens of thousands of annotations. 2) Existing vision algorithms show performance degradation in the presence of underwater challenges our second objective will be an image enhancement framework. 3) We will build novel vision algorithms for generic object detection, segmentation, classification, and tracking using noise-free high-quality sequences.

Research Staff and Graduate Students:

Dr. Fayaz Ali Postdoctoral Research Fellow
Mehnaz Ummar Research Associate
Basit Alawode Ph.D. candidate
Mehnaz Umar M.Sc. candidate
Omar Ibrahim Belal Combined Master & PhD Student
Moshira Ali Abdala Ph.D. candidate
Abderrahmene Boudiaf Ph.D. candidate
Muaz Khalifa Alradi Ph.D. candidate
Additional Info

Dr. Sajid is currently working on three different projects including Artificial Intelligence for Oceans Surveillance, Detecting Cancerous footprints from the histopathological landscape, and the flagship competition/project Muhammad Bin Zayed International Robotics Challenge-2023. Dr. Javed is supervising is also supervising several PhD., Ms.c., and Postdocs candidates in those projects. Dr. Javed is also the area chair of the Asian Conference on Computer Vision (ACCV-2022) and the Internation Conference on Robotics and Automation (ICRA) 2023.


I always look for good PhD students/research associates/postdocs to join projects related to computer vision and machine learning problems. Interested applicants with a strong track record of publications in computer vision venues are encouraged to contact me.