Research Groups - School of Engineering

Safe and Sustainable Transportation Group

The Safe and Sustainable Transport Research Group focuses on developing intelligent, data-driven solutions to enhance the safety, efficiency, and environmental sustainability of modern transportation systems. The group integrates artificial intelligence, Internet of Things (IoT), and advanced optimization techniques to address critical challenges in traffic management, vehicular networks, and transportation infrastructure.

Core research areas include intelligent transportation systems, real-time traffic prediction, route optimization, and anomaly detection in vehicular environments. The group also investigates cooperative and connected mobility frameworks, leveraging distributed learning and edge intelligence to improve system resilience and reduce congestion. A strong emphasis is placed on reducing emissions and energy consumption, contributing to cleaner and more sustainable mobility ecosystems.

Goals

• Safer Mobility Systems: Design and deploy AI-driven solutions for accident prevention, anomaly detection, and secure vehicular communication, improving safety across transportation networks.
• Sustainable Transport Solutions: Develop models and optimization frameworks that reduce congestion, fuel consumption, and emissions, supporting the transition to environmentally sustainable mobility.
• Industry and Government Collaboration: Engage with transportation authorities, municipalities, and industry partners to implement scalable smart mobility solutions and inform policy development.

SDG Alignment

The group research agenda directly supports:

• SDG 11: Sustainable Cities and Communities by enabling efficient, safe, and accessible urban transportation systems

• SDG 13: Climate Action through reduction of transport-related emissions via intelligent optimization and planning

• SDG 9: Industry, Innovation and Infrastructure by advancing smart transportation infrastructure and connected mobility technologies

Team Leader

Sustainable Medical AI and Digital Health Group

The Sustainable Medical AI and Digital Health Research Group focuses on developing intelligent, scalable, and responsible AI solutions to improve healthcare delivery and public health outcomes. The group integrates machine learning, deep learning, and IoT-enabled sensing technologies to enable early disease detection, real-time patient monitoring, and data-driven clinical decision support.

A central emphasis is placed on sustainability in healthcare systems—designing efficient AI models, enabling remote and accessible care, and reducing the operational and environmental costs of healthcare delivery. The group also advances privacy-preserving and trustworthy AI frameworks, including federated learning and secure data infrastructures, to ensure safe and ethical deployment in real-world medical settings.

Goals

• AI-Driven Healthcare Innovation: Develop robust and interpretable AI models for diagnostics, prognosis, and patient monitoring, with demonstrated clinical and public health relevance.
• Scalable and Accessible Digital Health: Design technologies that expand access to healthcare through remote monitoring, edge intelligence, and low-resource deployment settings.
• Trustworthy and Sustainable AI Systems: Advance methods that ensure privacy, fairness, and energy-efficient computation in medical AI, supporting long-term sustainability and regulatory compliance.

SDG Alignment

• SDG 3: Good Health and Well-Being by enabling AI-driven early detection, improving access to digital healthcare solutions, and strengthening real-time health risk monitoring

• SDG 9: Industry, Innovation and Infrastructure through the development of advanced digital health technologies

• SDG 6: Clean Water and Sanitation via data-driven environmental and health monitoring systems

External Research Grants

• “Towards High-Resolution 3D Reconstruction in Volume Electron Microscopy.” Principal Investigator: Haythem El Messiry (2025–2028)
• “Identifying Prodromal and Preclinical Markers for Predicting Diagnosis and Progression in Dementia” Principal Investigator: Firuz Kamalov (2025–2027)

Team Leader