About Me

I am a physicist interested in the theory of complex networks and statistical physics, as well as their applications to urban and social systems. The mathematical and computational methods I develop in my research draw on ideas from a range of disciplines including statistical physics, information theory, Bayesian inference, scientific computing, and machine learning.

Currently I am an Assistant Professor at the University of Hong Kong (HKU), jointly appointed in the Institute of Data Science and Department of Urban Planning and Design. I received my PhD in Physics at the University of Michigan in 2021 under the supervision of Mark Newman, and received undergraduate degrees in Physics and Mathematics at the University of Rochester in my hometown of Rochester, New York.

My main research interests are:

• Developing statistically principled unsupervised learning methods for noisy network data
• Improving the efficiency and interpretability of network model fitting and evaluation
• Modelling urban mobility networks and their impact on city prosperity and resilience
• Characterizing heterogeneity and correlations in urban spatial data with network methods