Dr. Sara A. Al-Emadi

Research Fellow

Dr. Sara A. Al-Emadi

Research Fellow

Educational Qualifications

PhD in Computer Science and Engineering

MSc in Computing (focus: Computer Engineering)

Entity

Qatar Computing Research Institute

Division

Qatar Center for Artificial Intelligence

Biography

Dr. Sara A. Al-Emadi is a Research Fellow at the Qatar Computing Research Institute (QCRI). Her research is focused on advancing robust generalisation in artificial intelligence (AI) to enable reliable deployment in complex, real-world environments. Her scholarly interests span computer vision, remote sensing, and artificial general intelligence (AGI), combining methodological development with practical application.

Dr. Al-Emadi’s research trajectory reflects a strong interdisciplinary orientation. She has led and contributed to projects involving unmanned aerial vehicle (UAV) technologies, anti-drone systems, network infrastructure, computer networks, and cybersecurity. This breadth of expertise enables her to approach research from both systems-level and algorithmic perspectives, fostering solutions that are technically rigorous and operationally viable.

Her professional trajectory uniquely combines academic scholarship with substantial industry leadership. Prior to her research career, she worked in the oil and gas sector as a Network Infrastructure Engineer and later as a Senior Engineer in IT Strategy and Enterprise Architecture. This experience informs her strategic approach to AI research, emphasising reliability, systems integration, and real-world deployment at scale.

Dr. Al-Emadi actively contributes to the research community through peer review and scholarly service. She serves as a reviewer for leading international conferences and journals in artificial intelligence and computer vision. In addition, she regularly presents her research at academic and professional venues, supporting knowledge dissemination and contributing to the advancement of the field.

Dr. Al-Emadi is a member of the Institute of Electrical and Electronics Engineers (IEEE) and the Association for Computing Machinery (ACM). Her recent scholarly contributions have been published in leading and highly selective venues, including the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) and the International Journal of Computer Vision (IJCV), reflecting the impact and rigour of her research.

PhD in Computer Science and Engineering

Hamad Bin Khalifa University

2025

MSc in Computing (focus: Computer Engineering)

Qatar University

2020

BSc in Computer Engineering

Qatar University

2018

  • Deep Learning
  • Reinforcement Learning
  • Machine Learning
  • Artificial Intelligence (AI)
  • Unmanned Aerial Vehicles technologies
  • Anti-drone Systems
  • Network Infrastructure
  • Computer Networks
  • Network Security.

Senior IT Engineer (ICT Strategy and Enterprise Architecture)

ICT Strategy and Enterprise Architecture, Qatar Petroleum.

Jul 2019 - Apr 2020

Part-time Research Assistant

Qatar University

Jan 2018 - Dec 2020

Network Infrastructure Engineer

Systems Engineer, Network Infrastructure, Qatar Petroleum.

Feb 2018 - Jul 2019

Al-Emadi, S. A., Yang, Y., & Ofli, F. (2025). Analysing satellite imagery classification under spatial domain shift across geographic regions. International Journal of Computer Vision. 

Al-Emadi, S. A., Yang, Y., & Ofli, F. (2025). Benchmarking object detectors under real-world distribution shifts in satellite imagery. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 8299-8309). IEEE.

Al-Emadi, S., Al-Ali, A., & Al-Ali, A. (2021). Audio-based drone detection and identification using deep learning techniques with dataset enhancement through generative adversarial networks. Sensors, 21(15), 4953.

Al-Emadi, S. A. (2021). DDI: Drones detection and identification using deep learning techniques [Master's thesis]. Qatar University.

Al-Emadi, S., & Al-Senaid, F. (2020). Drone detection approach based on radio-frequency using convolutional neural network. In 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies, ICIoT 2020 (pp. 29-34). IEEE.

Al-Emadi, S., & Al-Mohannadi, A. (2020). Towards enhancement of network communication architectures and routing protocols for FANETs: A survey. In 3rd International Conference on Advanced Communication Technologies and Networking, CommNet 2020. IEEE.

Al-Emadi, S., Al-Mohannadi, A., & Al-Senaid, F. (2020). Using deep learning techniques for network intrusion detection. In 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies, ICIoT 2020 (pp. 171-176). IEEE.

Zaza, A., Al-Emadi, S., & Kharroub, S. (2020). Modern QoS solutions in WSAN: An overview of energy aware routing protocols and applications. In 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies, ICIoT 2020 (pp. 581-589). IEEE.

Al-Emadi, S., Al-Ali, A., Mohammad, A., & Al-Ali, A. (2019). Audio based drone detection and identification using deep learning. In 2019 15th International Wireless Communications Mobile Computing Conference (IWCMC) (pp. 459-464). IEEE. 

  • May 2017, Dean's List 2017, College of Engineering, Qatar University, Doha, Qatar
  • Dec 2016, Grand Prize - Course Project Award, Department of CSE, College of Engineering, Qatar University, Doha, Qatar