Dr Yulun Zhou is an Assistant Professor in Urban Data Science based in the Faculty of Architecture at the University of Hong Kong. His work straddling urban science and artificial intelligence has been published in leading journals such as Nature Computational Science, Environmental Science & Technology, Geoscientific Model Development, Environment and Planning B: Urban Analytics and City Science, and Cartography and Geographic Information Science. He also serves as a reviewer for around ten journals such as Remote Sensing of Environment, International Journal of Geoinformation Science, Urban Studies, and Applied Network Science. Some of his recent work includes vector-based pedestrian navigation in cities (link), AI-driven climate-sensitive urban growth planning (link), data quality control of low-cost indoor air pollutant sensors (link), and urban vibrancy evaluation using multi-source spatial big data (link).
Yulun holds a PhD from the Chinese University of Hong Kong and a B.Eng. in Nuclear Science from the Institute of Modern Physics at Fudan University, Shanghai, China. He spent two of his PhD years in Cambridge, MA, working at the MIT Senseable City Laboratory and Harvard Healthy City Laboratory.
My research straddles urban science, spatial data science, and cognitive science. I study computational methods to support human decision-making in cities and promote AI-driven, human-centred urban planning and design. For the first time in history, urban sensing in cities has enabled passive observations of human behaviours in urban space at an unprecedented spatio-temporal scale. Through a combination of spatial data science, spatial information theory, statistics and computer optimizations, I try to uncover the strengths and weaknesses in human and machine intelligence by observing and analyzing human behaviours in cities. I approach these topics with various empirical methods — data mining of passively collected urban big datasets, statistical testing and modelling, machine learning, and optimization methods. My work is driven by the complementary goals of better understanding human learning and inferences in urban spaces and designing collaborative mechanisms and systems between AI and human decision-makers in urban planning and design.
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