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Alessandro Corbetta is an Assistant Professor in the Department of Applied Physics at Eindhoven University of Technology. His research focuses on the interface of complex flowing matter and the application of machine learning to non-linear physical systems. He actively contributes to the understanding of pedestrian crowd flows through the 'Crowdflow' topical group, aiming to develop quantitative models to enhance pedestrian safety and efficiency in crowded environments. This includes collaboration with national and international facility managers, such as municipalities and festivals, and he has initiated real-life crowd tracking experiments. His recent work involves applying advanced machine learning and deep learning techniques to analyze complex non-linear systems, especially in fluid turbulence. Corbetta holds an MSc (cum laude) in Mathematical Engineering from the Polytechnic University of Turin, Italy, and completed his PhD at Eindhoven University of Technology in 2016. He has received the VENI grant from NWO for research on understanding and controlling human crowd flows and serves on the editorial board of the multidisciplinary journal Collective Dynamics.
Eindhoven University of Technology • Eindhoven, Netherlands
Engaged in teaching and research focused on complex flowing matter and machine learning applications in physical systems.
Eindhoven University of Technology • Eindhoven, Netherlands
Conducted research within the Department of Applied Physics, focusing on fluid dynamics and crowd flow behavior.
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