Research Team

About

Stasis Mukwenya is a PhD researcher in Civil Engineering at Ariel University, specializing in urban geospatial data science, semantic interoperability, and human-centric urban digital twins. His research examines how urban domain models that are built and run separately, for energy, climate, mobility, and urban form, can share meaning rather than only data, and predict more accurately as a result.

His doctoral research develops ontologies for cognitive urban digital twins, shared semantic structures through which independently built models exchange typed, physically grounded information at inference time without retraining. The work introduces measures of how complete such a shared language is, separating what a twin can name from what it can relate, and tests them on pedestrian flow prediction under both routine and extreme urban conditions, where models that rely only on historical trends are least reliable.

Stasis contributes to the METROPOLIS Urban Digital Twin project at the Architectural Artificial Intelligence Research Lab, a platform on which planners run simulations against a shared model of a city, test proposed developments as separate scenarios, and compare the outcomes before any decision is committed. He works on the infrastructure beneath this: how city data is stored and organised, how simulation models are supplied with what they need, and how new data sources and models are added without the system being rebuilt.

Before beginning his doctoral studies, Stasis earned a BSc in Electronics and Telecommunications Engineering and an MSc in Mechatronics and Artificial Intelligence. His broader interests include knowledge representation, physics-informed machine learning, cross-domain reasoning, inclusive mobility, and responsible urban artificial intelligence.

Projects