Abstract:
Digital twins combine real-world data with digital representations of physical systems, but building useful digital twins involves more than simply collecting and displaying data. This talk presents my work on developing a real-time digital twin of a smart building, covering the journey from privacy-preserving sensor design and real-time event detection through to data processing and visualisation, and ultimately how these systems are evaluated with the people who use them.
Using the Cambridge Computer Laboratory as a testbed, I will discuss the systems developed to collect and process data from hundreds of sensors, detect events in real time, and provide a range of visualisations designed for different users and tasks. As building technical solutions becomes increasingly accessible, the question of what constitutes a good or fit-for-purpose solution becomes increasingly important. My PhD explores this question through a series of end-user studies, examining how different interfaces and visual representations perform across users and tasks, and how these findings can be used to guide the design of digital twins around the people who use them.
Bio:
Justas Brazauskas is a technical HCI researcher and machine learning engineer specialising in real-time systems, sensing, and interactive visualisation. He recently completed a PhD in Computer Science at the University of Cambridge. His work spans HCI and systems research at UCLIC and the Cambridge Computer Laboratory, as well as machine learning engineering at Roku.
Online meeting link:
