Hello! I am Mingze Chen, Ph.D. Candidate at the Urban Nature Design Research (UNDER) Lab, University of British Columbia (UBC), and an exchange student at the Senseable City Lab, Massachusetts Institute of Technology (MIT). My research focuses on the interdisciplinary application of big data and machine learning technologies in landscape and urban planning.
I hold a Master of Architecture in machine learning urbanism and worked as a research associate at The Bartlett Faculty of the Built Environment, University College London (UCL).
I have published over 30 peer-reviewed journal articles in leading international journals such as the Sustainable Cities and Society, Habitat International, Journal of Environmental Management, Urban Forestry & Urban Greening, and Cities, etc. I presented my work at major academic conferences, including ACSP, CELA, IFLA, and EDRA. I am also actively involved in teaching, serving as a sessional lecturer and adjunct fellow at institutions in Canada, the United States, and China.
I am the founder of Nature AI Lab, a research platform that integrates cutting-edge technologies with urban and nature studies to create data-driven solutions for a more resilient, equitable, and livable future—embodying our vision: Better Technologies, Better Nature, Better Life.
Contact: mze.chen@outlook.com
For more about my research and design, please visit my Google Scholar Profile, Design Portfolio, and LinkedIn.
Download my CV (updated Jan 2026)
My research interest focuses on Human-Nature-Urban Intelligence, including:
Theme I. AI-driven Human Behaviour Monitoring in Urban and Natural Environment
Developing multimodal AI and sensing frameworks (computer vision, large language models, and environmental sensors) to monitor behavioral dynamics (visitor volume, movement, and activity) in urban and natural spaces (green space, street space, public space, and protected areas).
Theme II. Regional-level Urban Vitality and Environmental Justice
Leveraging multi-source geospatial data (POIs, street-view imagery, social media, and smartphone GPS traces) to examine spatial equity, accessibility, perception, and vitality across urban systems.
Theme III. Climate Resilience, Thermal Comfort, and Wellbeing in Urban Landscapes
Exploring the intersection of environmental comfort, public health, and urban resilience by combining microclimate monitoring, machine learning, and geo-design approaches.
Theme IV. Computational Design, Mapping, and Visualization
Integrating Rhino–Grasshopper, AI diffusion models, web-based visualization (HTML/CSS/JS), and GIS workflows to support parametric design and mapping.
Collaborators
