Digital Twin
Connected models of buildings and infrastructure that stay in sync with the physical world, for monitoring, simulation, and better engineering decisions.
- real-time data
- simulation
- sensing & IoT
I study how buildings, bridges, and cities can be sensed, simulated, and understood, connecting real structures to models that learn from them.
Research across digital twin, AI in construction, and parametric modeling.
Connected models of buildings and infrastructure that stay in sync with the physical world, for monitoring, simulation, and better engineering decisions.
Machine learning and generative methods applied to how we design and build: prediction, optimization, and learning from project data.
Rule-driven geometry and computational design, encoding engineering logic so that models, drawings, and analyses can generate themselves.
Cloud platforms and web-based 3D tools that bring computational methods into everyday engineering and construction workflows.
I'm a researcher focused on computational approaches to construction and the built environment, with a background in engineering.
My work centers on digital twin, AI in construction, and parametric modeling. I build systems that connect engineering logic, geometry, and data.
I previously led research & development at OpenBrIM, building cloud-based parametric modeling systems used on large-scale infrastructure projects.
I care about making engineering knowledge programmable, and helping the construction industry work in a more model-driven, data-informed way.

Between engineering and data: digital twin models, AI, and parametric systems for the construction and infrastructure world.