Heba Kurdi | Nature-inspired computing | Best Academic Researcher Award

Published on by Math Scientist

Prof. Heba Kurdi | Multi-View Data | Best Academic Researcher Award

Full Professor | King Saud University | Saudi Arabia

Prof. Heba Kurdi is a leading researcher in distributed artificial intelligence and cloud computing at King Saud University, Saudi Arabia. A Stanford-ranked top 2% scientist (2022–2024), she has produced over 120 research publications and holds eight patents in AI-driven systems, multi-agent technologies, and computational optimization. Her work spans distributed AI, swarm intelligence, and smart system design, with applications in robotics, cloud infrastructures, and cyber-physical systems. Prof. Kurdi has led more than 15 funded projects and consulted on national-level research initiatives with institutions such as KACST and Aramco. As an MIT Fellow and IEEE Senior Member, she continues to advance the integration of bio-inspired algorithms and AI-driven models to enhance system efficiency, scalability, and intelligence in both industrial and academic settings.

Profiles: Orcid | Google Scholar 

Featured Publications

Al-Rayis, E., & Kurdi, H. (2013). Performance analysis of load balancing architectures in cloud computing. 2013 European Modelling Symposium, 520–524. Citation count: 44.

Aljalaud, F., Kurdi, H., & Youcef-Toumi, K. (2023). Bio-inspired multi-UAV path planning heuristics: A review. Mathematics, 11(10), 2356. Citation count: 41.

Kurdi, H., Almulifi, A., Al-Megren, S., & Youcef-Toumi, K. (2021). A balanced evacuation algorithm for facilities with multiple exits. European Journal of Operational Research, 289(1), 285–296. Citation count: 39.

Kurdi, H., Alkhaider, S., & Alfaifi, N. (2014). Development and evaluation of a web-based question answering system for Arabic language. Computer Science & Information Technology (CS & IT), 4(2), 187–202. Citation count: 38.

Althnian, A., Aloboud, N., Alkharashi, N., Alduwaish, F., Alrshoud, M., & Kurdi, H. (2020). Face gender recognition in the wild: An extensive performance comparison of deep-learned, hand-crafted, and fused features with deep and traditional models. Applied Sciences, 11(1), 89. Citation count: 36.