In this issue of the TSS Newsletter,
we are pleased to invite you to a workshop addressing key challenges for TSS: “Trustworthy
Autonomy and Collaboration in Smart Systems”, and to share selected
results from TSS-supported environments.
As noted in our 2025 Annual Report,
MDU is undergoing organisational changes, which will also affect TSS. I can
already share that there will be a change in leadership, as I will retire at
the end of May. The details are currently being discussed, with a clear
ambition to further strengthen the efficiency, impact, and value
creation of TSS. More information, including the new leadership, will be
presented in the next newsletter.
I would like to thank everyone who has
contributed to and supported TSS—it has been a pleasure working with you!
I hope to see you at MDU in Västerås
on May 21.
/Hans Hansson, for a short while
longer leader of TSS
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WORKSHOP: Trustworthy Autonomy and Collaboration in Smart Systems
This workshop brings together researchers and industry representatives to explore howtrust, collaboration,and adaptabilitycan be designed into AI-driven systems operating in complex, real-world environments.Drawing on ongoing research at Mälardalen University within the Trusted Smart Systems (TSS) profile area,the workshop highlights how autonomy must be combined withverifiability, resilience, and human-centricdesignto enable dependable systems in safety-critical and industrial contexts.
At Mälardalen University, the Heterogeneous Systems – Hardware–Software Co-Design (HERO) research group is tackling this challenge head-on. Their work focuses on making complex systems predictable before they reach the real world.
New research shows that truly covert systems must balance conflicting worst-case scenarios—because in the real world, uncertainty is the real adversary.
How can autonomous machines collaborate without compromising safety? Researchers at Mälardalen University show how reinforcement learning combined with formal safety contracts can coordinate fleets of machines—ensuring efficiency while preventing unsafe behaviour.
