Meet the team

Dr. Ismail El Sayad is an Associate Professor at the University of the Fraser Valley (UFV) and serves as the Research Chair for the School of Computing. With an extensive and distinguished background in computer science, computer engineering, data mining, machine learning, and interdisciplinary applications of artificial intelligence (AI), Dr. El Sayad has made significant contributions to both academia and industry. He holds an M.S. in Computer Engineering from Duisburg Essen University, Germany, and a Ph.D. in Computer Science from Lille1 University for Science and Technology, France.

Dr. El Sayad's research portfolio spans multiple domains, focusing on addressing real-world challenges through AI-driven solutions. His key areas of expertise include:

• Multimedia Retrieval and Semantic Learning: Advancing techniques in multimedia retrieval and representation, including semantic learning in image databases. His research focuses on extracting, processing, and analyzing visual data to develop intelligent and efficient data retrieval systems.
• Agriculture and Sustainability: Developing AI-powered models to optimize agricultural practices. His work leverages consequential life cycle analysis (LCA) and deep learning techniques to promote environmental sustainability and improve resource efficiency.
• Online Safety: Pioneering multi-modal AI systems to detect and mitigate toxicity in online conversations, thereby fostering safer and more inclusive digital spaces. His research integrates text, audio, and visual data into comprehensive frameworks for online safety.
• AI Safety and Ethics: Exploring the ethical implications of artificial intelligence and its safe deployment in society. His efforts focus on ensuring transparency, fairness, and accountability in AI systems while addressing biases and promoting equitable outcomes.
• Risk Assessment in Finance: Designing and comparing AI models for mortgage risk assessment, combining artificial intelligence with traditional financial methods to enhance decision-making processes.
• Industrial IoT and Data-Driven Solutions: Creating data-driven IoT solutions that focus on data integration and mining to improve operational efficiency and system performance through advanced machine learning approaches.

Dr. El Sayad’s work is deeply rooted in practical applications, facilitating impactful collaborations with both industry and academia. His contributions to research have earned widespread recognition, including publications in leading journals and conferences, as well as authorship of a book and a book chapter.

In addition to his research achievements, Dr. El Sayad is deeply committed to education and mentoring. His teaching philosophy emphasizes active learning, blended models, and game-based strategies, creating engaging learning experiences that inspire students to think critically and innovate.

Dr. El Sayad’s interdisciplinary approach bridges theoretical advancements with practical implementations, delivering sustainable and intelligent solutions to complex societal and industrial challenges. His ongoing efforts continue to push the boundaries of AI’s potential, showcasing its ability to create positive, real-world impacts.

1.Thu Nguyen, Simranjit Singh, Sadaf Faizi, Ismail El Sayad. “A Late Fusion Approach Using CSNNs for Multi-Modal Toxicity Detection in Online Media.” Proceedings
of the 49th IEEE International Conference on Computers, Software, and Applications
(COMPSAC 2025), IEEE, 2025, Toronto, Canada.

2. Ismail El Sayad. “AI and IoT-Driven Framework for Predictive Urban Agriculture: Bridging Technology and Sustainability.” Proceedings of the 2025 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), IEEE, 2025, Vancouver, Canada.

3. Ismail El Sayad, Mohammed Al Nakshabandi. “Multi-Modal Personalization for Toxicity Detection via Reinforcement-Learned Spiking Neural Fusion.” Proceedings of the IEEE 8th International Conference on Signal Processing and Machine Learning
(SPML 2025), IEEE, 2025, Hohhot, China .

4. Ismail El Sayad, Simranjit Singh, Sadaf Faizi, Thu Nguyen. “Multi-Modal CSNNs for Integrated Toxicity Detection Across Text, Audio, and Visual Modalities.” Proceedings of the 5th International Conference on Electrical, Computer and Energy Technologies (ICECET), IEEE, 2025, Paris, France.

5. Ismail El Sayad, Harjobanpreet Singh Sidhu, Montek Kundan, Alesandros Glaros, Stefania Pizzirani. “AI-Driven Optimization for Urban and Vertical Agriculture Planning: A Multi-Model Approach.” Proceedings of the 5th International Conference on Electrical, Communication and Computer Engineering (ICECCE), IEEE, 2024, KualaLumpur, Malaysia.