Marco Caccamo

is Humboldt professor in the TUM School of Engineering and Design; Technical University of Munich (TUM).
He leads the Chair of Cyber-Physical Systems in Production Engineering.

Marco Caccamo graduated (summa cum laude) in Computer Engineering at University of Pisa on July 1997. He received a Ph.D. in Computer Engineering from Scuola Superiore Sant'Anna (SSSUP) in January 2002. Since 2002 he has been the Principal Investigator at the "Real Time and Embedded System Laboratory" and professor in the Department of Computer Science, with courtesy appointments in the Department of Electrical and Computer Engineering, Coordinated Science Lab (CSL), and Department of Aerospace Engineering at the University of Illinois at Urbana-Champaign (UIUC). Caccamo received visiting professorships at ETH, Zurich and TUM Munich as TÜV Süd Stiftung visiting professor and August-Wilhelm Scheer guest professor. He has chaired Real-Time Systems Symposium and Real-Time and Embedded Technology and Applications Symposium, the two IEEE flagship conferences on Real-Time Systems. He also served as General Chair of Cyber Physical Systems Week. In 2003, he was awarded an NSF CAREER Award. Since 2018, he is a recipient of the Alexander von Humboldt Professorship at TUM and he is IEEE Fellow. In broad terms, his research interests are centered on the area of embedded systems. He has worked in close collaboration with avionics, farming, and automotive industries developing innovative software architectures and toolkits for the design automation of embedded digital controllers, and low-level resource management solutions for real-time operating systems running on multicore architectures. More recently, he has begun to investigate real-time, security, and robustness problems in the software architecture of unmanned aerial vehicles (UAVs). See article about this work and take a look at our UAV testbeds and web-site).

Honors & Awards

  • Outstanding Technical Achievement and Leadership Award from the IEEE Technical Committee on Real-Time Systems, 2023
  • Best Paper Award (RTNS 2023)
  • Outstanding Paper (ECRTS 2019)
  • IEEE Fellow, "For contributions to the theory and applications of hard real-time multicore computing", 2018
  • Alexander von Humboldt Professorship, 2018 (TUM)
  • Paper of the month and Editor's pick of the year 2016, IEEE Transactions on Computers
  • Best Presentation Award (RTAS 2016)
  • Engineering Council Outstanding Advising Award (Spring 2015)
  • Best Student Paper Award (RTAS 2013)
  • Ranked as excellent teacher by students of CS598MC (Fall 2007, Spring 2012, Fall 2014)
  • IEEE Tech. Committee on RT Systems Service Award (for serving as General Chair of CPSWeek 2011)
  • Best Paper Award (RTCSA 2008)
  • Best Student Paper Award (RTSS 2004)
  • NSF CAREER Award (2003)

Professional Activities

Teaching

  • MW2400, IN2107 (Advanced Seminar on Safe Cyber-Physical Systems)
  • MW2411 (Concepts and Software Design for Cyber-Physical Systems)
  • MW2419  (Simplex: Fault-Tolerant Control Strategy for Real-Time Cyber-Physical Systems - Laboratory)
  • MW2426 (Cyber-Physical Systems Lab: Autonomous Applications)

Patents

  • U.S. Patent No. 15/639,666, Title: “Scratchpad-Based Operating System for Multi-Core Embedded Systems”. Issue date: May 2020.

Research Highlights

    Brief (but incomplete) summary of research highlights for year 2023:
  • Design and implement novel resource management policies for embedded real-time systems running on high-performance heterogeneous platforms
  • Develop new reinforcement learning architectures for CPS
  • Design architectures for sandboxing controllers in CPS
  • Develop synthetic training paradigms for 6D pose recognition and policy learning in robotic manipulation
  • Please refer to Annual Report 2023 for details.
    Brief (but incomplete) summary of research highlights for year 2022:
  • Develop new reinforcement learning architectures for CPS
  • Design and implement novel resource management policies for embedded real-time systems running on high-performance heterogeneous platforms
  • Design architectures for sandboxing controllers in CPS
  • Develop a 6D pose recognition framework for robotic manipulation
  • Please refer to Annual Report 2022 for details.
    Brief (but incomplete) summary of research highlights for year 2021:
  • Develop new reinforcement learning strategies for CPS, path planning, and control
  • Design and implement novel resource management policies for embedded real-time systems running on high-performance heterogeneous platforms
  • Design architectures for sandboxing controllers in CPS
  • Please refer to Annual Report 2021 for details.
    Brief (but incomplete) summary of research highlights for year 2020:
  • Reinforcement Learning for Cyber-Physical Systems
  • Application of 6D Pose Estimation to create novel deep learning approaches for high precision manufacturing
  • Predictable and high-performance resource management of CPS on heterogeneous platforms
  • Please refer to Annual Report 2020 for details.
    Brief (but incomplete) summary of research highlights for year 2019:
  • Sandboxing Controllers for Stochastic Cyber-Physical Systems
  • Real-Time Scratchpad-Centric OS with Three-Phase Execution Model
  • Segment Streaming for the Three-Phase Execution Model
  • Please refer to Annual Report 2019 for details.
    Brief (but incomplete) summary of research highlights for year 2018:
  • On the Predictability of Heterogeneous SoC Multicore Platforms
  • Preserving Physical Safety under Cyber Attacks
  • Please refer to Annual Report 2018 for details.

Current Students/Postdocs

  • Ayoosh Bansal (Ph.D.)
  • Mirco Theile (Ph.D.)
  • Denis Hoornaert (Ph.D.)
  • Hongpeng Cao (Ph.D.)
  • Binqi Sun (Ph.D.)
  • Daniele Bernardini (Ph.D.)
  • Raphael Trumpp (Ph.D.)
  • Lukas Dirnberger (Ph.D.)
  • Andrea Bastoni (Postdoctoral researcher)
  • Alexander Züpke (Postdoctoral researcher)
  • Harald Bayerlein (Postdoctoral researcher)

Former Students

  • Bingzhuo Zhong (Ph.D.'23)
  • Or Dantsker (Ph.D.'21)
  • Rohan Tabish (Ph.D.'21)
  • Jayati Singh (MS'21)
  • Fardin Abdi(Ph.D.'19, Senior machine learning engineer @ Uber)
  • Renato Mancuso (Ph.D'17, Assistant Professor Boston University)
  • Suraj Venkat (MS'17)
  • Andrew Louis (MS'17, Embedded Software Engineer at Bell)
  • Stanley Bak (Ph.D.'13, Assistant Professor Stony Brook University)
  • Roman Dudko (MS'12, Google)
  • Bach Duy Bui (Ph.D.'11, SW Engineer at Yahoo!, UIUC Research Center)
  • Rodolfo Pellizzoni (Ph.D.'10, Associate Professor Univ. of Waterloo)
  • Olugbemiga Adekunle (MS'10, Instructor at Blue Ridge Community College, VA)
  • Deepti K. Chivukula (MS'10, Associate, Firm Risk Management @ Morgan Stanley)
  • Chin F. Cheah (MS'07, Citadel Investment Group)
  • Sathish Gopalakrishnan  (Ph.D.'05, Associate Professor UBC)
  • Spencer Hoke (MS'05, Garmin)
  • Deepu C. Thomas (MS'04, Microsoft)
  • Simone Giannecchini (MS'03, Geosolutions, Italy)

Contact Me

  • Office: Building 1, Second Floor, Chair of Cyber-Physical Systems in Production Engineering, Faculty of Mechanical Engineering, Boltzmannstr. 15, 85748 Garching, Germany
  • Phone: +49(89)289 - 55170
  • Email: mcaccamo "at" tum.de
Marco Caccamo