Omar Osama Mohamed Shalash

Omar Osama Mohamed Shalash is an Assistant Professor at the Arab Academy for Science, Technology, and Maritime Transport (AASTMT) in Egypt, where he serves in the Intelligent Systems Department of the College of Artificial Intelligence. He earned his Ph.D. in Biomedical Engineering from the University of Strathclyde, UK, where his doctoral research centered on the development of an autonomous robotic system for unicompartmental knee arthroplasty. He holds both a Master's and Bachelor's degree in Computer Engineering from AASTMT, where he graduated with distinction.

Shalash's 2025 polygraph study presents a multimodal dataset with physiological data (heart rate, galvanic skin response, and body temperature) along with demographic data, collected from 49 subjects. The research presents a polygraph-based lie detection system utilizing multimodal sensor fusion, with a Random Forest algorithm achieving 97% accuracy, outperforming Logistic Regression, Support Vector Machine, and k-Nearest Neighbor approaches. His work addresses critical gaps in deception detection, particularly the lack of adequate datasets and the need for improved accuracy in automated lie detection systems. The research team also released the Polygraph-Based Truth Detection System Dataset on Mendeley Data in 2025.

Shalash has made significant contributions to the fields of robotics, artificial intelligence, and digital twin technologies, authoring numerous publications in high-impact, peer-reviewed journals with topics ranging from swarm robotics to neural network applications in industrial diagnostics. He serves as a member of the advisory board of Robotics: Integration, Manufacturing and Control (RIMC) journal. His polygraph research represents a collaborative effort with frequent co-authors Ahmed Métwalli, Mohamed Hany Sallam (both from AASTMT), and Esraa Khatab from Heriot-Watt University Dubai.

Beyond polygraph research, Shalash's broader research portfolio spans multiple domains including autonomous robotic systems for healthcare (particularly robotic-assisted intravenous injection), AI-driven technologies for breast cancer detection using thermography and digital twin models, cloud-based battery management systems for electric vehicles, and machine learning applications in hydroponic farming. He consistently bridges the gap between academia and industry, working with government and private institutions to apply cutting-edge AI and robotics technologies to solve real-world challenges in healthcare, renewable energy, and precision agriculture.