Marco Caccamo

Marco Caccamo is a Professor at the Technical University of Munich (TUM), holding the Chair of Cyber-Physical Systems in Production Engineering within the Faculty of Mechanical Engineering. He is also a Principal Investigator and Professor at the Department of Computer Science, with courtesy appointments in Electrical and Computer Engineering, Coordinated Science Lab (CSL), and Aerospace Engineering at the University of Illinois at Urbana-Champaign (UIUC). His research spans Embedded Systems, Real-Time Systems, and Cyber-Physical Systems (CPS), focusing on resource management, reinforcement learning architectures, and 6D pose recognition for robotics.

• University of Pisa (B.Sc., 1997)
• Scuola Superiore Sant'Anna (Ph.D., 2002)

Research highlights include predictable resource management on heterogeneous platforms, security frameworks for AI-based controllers, and UAV testbed development. His work integrates deep learning and real-time constraints in industrial applications like avionics, farming, and automotive systems.

His 15 most recent publications emphasize cache optimization, memory bandwidth regulation, and reinforcement learning for CPS, with a focus on multi-core processors and DNN inference. Awards include the IEEE Fellow (2018), Alexander von Humboldt Professorship (2018), and multiple Best Paper Awards at RTSS, RTNS, and RTAS.

• NSF CAREER Award (2003)
• IEEE Fellow (2018)
• Alexander von Humboldt Professorship (2018)
• Best Paper Awards (RTSS 2024, RTNS 2023, ECRTS 2019)

He has advised numerous Ph.D. students and postdocs, with a track record in UAV development and industrial collaborations. His lab, the Real-Time and Embedded System Laboratory, focuses on real-time OS and predictable computing.