Scientific Profile
I am a research fellow at the Qatar Computing Research Institute, Hamad Bin Khalifa University. I completed my PhD at the College of Science and Engineering, HBKU, where my doctoral research focused on advancing the robustness and reliability of artificial intelligence systems for deployment in complex, real-world environments, with a specific emphasis on domain generalisation.
I completed my MSc at Qatar University, where my thesis focused on anti-drone technologies, employing deep learning to detect and identify drones through their acoustic signatures.
My professional trajectory reflects an interdisciplinary orientation. Prior to pursuing research, I worked in the oil and gas sector, first as a network infrastructure engineer, and subsequently as a senior engineer in IT strategy and enterprise architecture. This experience continues to inform my approach to AI research, with an emphasis on reliability, systems integration, and deployment at scale.
I am an active member of the research community, serving as a reviewer for leading AI and computer vision venues, presenting at academic conferences, and publishing in venues such as CVPR and IJCV.
Active projects
RWGAI (Real World Generalisation in Artificial Intelligence)
Research
I am interested in improving the robustness and generalisation of machine learning models for real-world applications. My research focuses on developing solutions, datasets, and benchmarks for challenges like domain generalisation, to enhance the reliability and applicability of models across diverse conditions and environments.
Publications
2025
Benchmarking Object Detectors under Real-World Distribution Shifts in Satellite Imagery
Sara Al-Emadi, Yin Yang, Ferda Ofli
CVPR 2025
Project page/Paper/arXiv
Introducing the RWDS benchmark to evaluate object detection robustness in satellite imagery under real-world distribution shifts.
2021
MSc Thesis – 2021
2020
Towards enhancement of network communication architectures and routing protocols for FANETs: A survey
Sara Al-Emadi, Aisha Al-Mohannadi
IEEE CommNet 2020
Paper
This paper surveys the latest network communication architectures and routing protocols for Flying Ad hoc Networks (FANETs) and proposes a clustering-based architecture and new routing mechanism to address existing limitations.
Drone detection approach based on radio-frequency using convolutional neural network
Sara Al-Emadi, Felwa Al-Senaid
IEEE ICIoT 2020
Paper/Github
This paper presents a deep learning-based drone detection solution using Radio Frequency (RF) signals, outperforming existing methods.
Modern QoS Solutions in WSAN: An Overview of Energy Aware Routing Protocols and Applications
Ahmad Zaza, Sara Al-Emadi, Suleiman Kharroub
IEEE ICIoT 2020
Paper
This paper reviews state-of-the-art QoS-enhancing approaches, protocols, and applications in WSAN, highlighting their shortcomings and suggesting improvements.
Using deep learning techniques for network intrusion detection
Sara Al-Emadi, Aisha Al-Mohannadi, Felwa Al-Senaid
IEEE ICIoT 2020
Paper
The paper proposes a deep learning-based network intrusion detection system using CNN and RNN, comparing their performance to identify the best model for detecting attacks.
2019
Audio Based Drone Detection and Identification using Deep Learning
Sara Al-Emadi, Abdulla Al-Ali, Amr Mohammed, Abdulaziz Al-Ali
IEEE IWCMC 2019
Project page/Paper/Github
This study proposes a deep learning solution to detect and identify drones using acoustic signatures and introduces the first drone audio dataset.
Sara Al-Emadi 