ML4CPS – Machine Learning for Cyber-Physical Systems

7th ML4CPS Conference, March 21-22, 2024, Berlin

The 7th ML4CPS offers a platform for exchange between researchers and users from various fields. The conference will be held in English on March 21 and 22, 2024 at the Fraunhofer Forum in Berlin. It will be hosted by the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB and the Helmut Schmidt University (HSU).

Submissions requested (deadline expired)

The contributions may relate to the following topics, but are not limited to them:

  • Large Language Models for CPS: Large language models possess the capability to facilitate human-machine interaction. Their capacity to interpret and generate text unlocks novel opportunities for intelligent automation, contextual comprehension, and seamless integration of various data sources, ultimately enhancing the overall performance and functionality of cyber-physical systems.
  • Multimodal Learning: These models possess the capability to facilitate the integration of multiple diverse sensors. Multimodal machine learning models can effectively combine sensor measurements, visual data, contextual information, and various other types of inputs. By using these models, cyber-physical systems can enhance their perception, especially in complex real-world scenarios, leading to improved overall performance.
  • Robust Machine Learning: Those techniques play a vital role since they reduce effects of noisy or adversarial data. By incorporating robustness measures, machine learning models can demonstrate improved resilience, enhanced generalization capabilities, and reliable performance when being used for cyber-physical systems.
  • Integrating domain knowledge in neural networks: This is a crucial aspect for building robust and high-performing neural networks. There are several ways to incorporate prior knowledge into the neural network, like designing the network architecture, incorporating additional data from simulations, or imposing constraints on the loss function. All approaches lead to improved network performance and adaptability in cyber-physical systems.

The publications of the ML4CPS Conference 2024

The publications of the 7th ML4CPS - Machine Learning for Cyber-Physical Systems Conference can be found online on the library page of Helmut Schmidt University.

Papers are chosen on a peer-review basis and accepted papers are published with a unique DOI number. Papers with commercial character will not be taken into consideration. Submissions are limited to eight pages of content, including all figures and tables; additional pages with references are permitted. Submissions exceeding 8 pages are also possible after consultation with the organizing committee.

  • Please use the following template for your submission.
  • Paper Submission will be handled via easychair.
  • All questions related to paper submissions should be emailed to:



General Chairs:

  • Oliver Niggemann, Helmut-Schmidt-Universität Hamburg
  • Jürgen Beyerer, Fraunhofer IOSB/Karlsruher Institut für Technologie (KIT)

Program Chairs:

  • Alexander Diedrich, Helmut-Schmidt-Universität Hamburg
  • Alexander Windmann, Helmut-Schmidt-Universität Hamburg
  • Christian Kühnert, Fraunhofer IOSB


International Program Committee:

  • Volker Lohweg, HS-OWL, DE
  • Alexander Fay, HSU , DE
  • Ingo Pill, Silicon Austria Labs, AT
  • Patrick Rodler, Alpen-Adria-Universität Klagenfurt, AT
  • Roni Stern, Ben-Gurion-Universität, Israel
  • Alexander Maier, HS Bielefeld
  • Kaja Balzereit, HS Bielefeld
  • Idel Montalvo, IngeniousWare GmbH
  • Martin Wagner, Technologiezentrum Wasser