Eliseo Ferrante

Eliseo Ferrante is a Visiting Professor of Computer Science at New York University Abu Dhabi and a tenured Assistant Professor at Vrije Universiteit Amsterdam. He holds a PhD in Applied Sciences from Université Libre de Bruxelles and dual MSc degrees in Computer Science and Artificial Intelligence from Politecnico di Milano and the University of Illinois at Chicago. His academic work spans robotics, artificial intelligence, and complex systems, with a focus on swarm robotics and statistical physics.

• MSc, Computer Science and Artificial Intelligence – Politecnico di Milano
• MSc, Computer Science and Artificial Intelligence – University of Illinois at Chicago
• PhD, Applied Sciences – Université Libre de Bruxelles

His research interests include swarm robotics, complex systems, machine learning, artificial intelligence, non-equilibrium statistical physics, and evolutionary and theoretical biology. Ferrante’s work bridges fundamental and applied research, aiming to develop scalable, controllable swarm systems that operate without centralized control or man-made infrastructure. He has contributed to over 80 publications across robotics, physics, and computer science.

The 15 most recent articles reflect a consistent focus on decentralized control, emergent behavior in swarms, bio-inspired algorithms, and the application of statistical physics to robotics. Topics include UAV swarms, task allocation, energy efficiency, and human-swarm interaction, demonstrating both theoretical depth and real-world applicability.

Eliseo Ferrante actively contributes to the academic community through editorial roles and conference organization, including serving as a co-organizer for IROS 2024 in Abu Dhabi. He has taught core computer science courses such as Data Structures, Machine Learning, Operating Systems, and the Capstone Project, mentoring students in research and development. His teaching spans from algorithm design to advanced machine learning and system-level programming.

Ferrante leads research on large-scale robotic swarms, focusing on achieving a balance between scalability and controllability. His lab explores autonomous navigation in GPS-denied environments, swarm intelligence, and robust coordination in dynamic settings. He collaborates across disciplines, integrating insights from biology and physics into engineering solutions for real-world challenges.