سارة العمادي

باحث مشارك

البريد الإلكتروني

salemadi@hbku.edu.qa

سارة العمادي

باحث مشارك

المؤهلات العلمية

MSc in Computing (focus: Computer Engineering)

BSc in Computer Engineering

الكيان

معهد قطر لبحوث الحوسبة

القسم

تحليل البيانات

السيرة الذاتية

Sara Al-Emadi is a Research Associate at the Qatar Computing Research Institute, part of Hamad Bin Khalifa University. Her research explores generalization in artificial intelligence for real-world applications, computer vision and AI.

Her past work spans diverse domains, including unmanned aerial vehicle (UAV) technologies, anti-drone systems, network infrastructure, computer networks, and cybersecurity.

Sara previously worked in the oil and gas industry as a Network Infrastructure Engineer and later advanced to a Senior Engineer role in IT Strategy and Enterprise Architecture.

She holds a Master of Science in Computing and a Bachelor of Science in Computer Engineering, both from Qatar University. Currently, she is pursuing a PhD in Computer Science and Engineering at Hamad Bin Khalifa University.

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-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-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-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-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