Alexandre Lombard is an Associate Professor in Computer Science at the Université de Technologie de Belfort-Montbéliard (UTBM), France, and a member of the CIAD laboratory (Connaissance et Intelligence Artificielle Distribuées).

He holds a PhD in Computer Science, awarded in 2017, with doctoral work focused on cooperative regulation of conflict areas between autonomous vehicles. Since then, his academic and research activities have centered on intelligent transportation systems, artificial intelligence for autonomous and connected vehicles, and the modelling of complex interactions between humans and cyber-physical systems.

His teaching activities for the UTBM focuses on algorithmic and programmation, databases and web technologies. He is also the founder of the course on Generative Artificial Intelligence at the UTBM, and the supervisor of the Artificial Intelligence section of the engineering degree in computer science. From 2023 to 2026, he was in charge of the pedagogical management of internships for the computer science department.

His main research interests include intelligent transportation systems, connected and autonomous vehicles, multi-agent and cooperative systems, reinforcement learning for decision-making, simulation of dynamic environments, and privacy-aware approaches for mobility data and driver-behavior analysis.

Alexandre Lombard has contributed to several notable research efforts in these areas. His work includes studies on cooperative vehicle coordination, trajectory optimization for pedestrian crossings, and the use of deep reinforcement learning to improve the safety and efficiency of autonomous vehicles in interaction with pedestrians. He has also been involved in research related to privacy-preserving methods for connected and autonomous vehicles, including driver-behavior profiling and secure data analysis frameworks.

Among his notable participations, he was involved in the VASIS research dynamic (Véhicule Autonome Socialement Intelligent et Sûr), including the co-supervision of doctoral work dedicated to deep reinforcement learning for safe and efficient pedestrian crossings. He is also involved in the X-Hub project, an autonomous logistics platform dedicated to the industrialization and large-scale deployment of eco-friendly, autonomous, and connected tow-robots for warehouses and logistics platforms, contributing to research at the intersection of autonomous driving, robotics, connectivity, and intelligent logistics systems.

His publications and collaborations reflect a strong commitment to bridging artificial intelligence, simulation, human-centered vehicle interaction, cyber-physical systems, and next-generation mobility and logistics solutions.