The X-Trust CPS Lab focuses on the integration of cutting-edge technologies to advance the development and deployment of next-generation cyber-physical systems (CPS).  Key areas of exploration include:

Resilient Distributed Computing: Explore techniques for enhancing the reliability and fault-tolerance of distributed computing systems. Research focuses on designing adaptive systems that maintain performance and availability despite hardware or software failures due to normal failure and attacks.

Trustworthy and Collaborative Machine Learning: Examine methods to ensure the integrity and transparency of machine learning models used in CPS. This includes developing frameworks for fair, accountable, and explainable AI, as well as promoting collaboration between machine learning systems and human operators to improve decision-making processes.

Digital Twin Technology: Study the use of digital twins for real-time monitoring and simulation of physical systems. Research aims to optimize system performance, predict maintenance needs, and improve overall system design by leveraging virtual replicas of physical assets.

Large Language Modeling: Investigate the application of large language models to enhance human-computer interactions within CPS. This includes improving natural language understanding and generation capabilities to facilitate more intuitive and effective communication between users and systems.

The integration of multimodal data fusion with explainable AI (XAI) on resource-constrained devices creates a scalable, trustworthy and transparent framework for real-time environmental monitoring and decision-making. By combining data from sources such as sensors, satellites, and climate models, it delivers a comprehensive understanding of complex scenarios. XAI enhances the interpretability and accountability of AI insights, fostering trust in automated decisions and allowing stakeholders to verify outcomes transparently, even in critical, real-time contexts.

We aim to develop adaptive, secure, and efficient solutions for cyber-physical systems, addressing complex challenges while driving innovation in digital environments. These advancements empower edge devices to provide timely, accurate and explainable insights in areas like disaster response and climate adaptation, ensuring that trustworthy technologies can operate effectively, even under resource constraints.