About Me

I am a physicist working on network science and statistical physics for understanding complex systems.

Currently I am an Assistant Professor at the University of Hong Kong (HKU), hosted by the School of Computing and Data Science and jointly appointed with the 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.

My research is centered around the design, optimization, and analysis of principled methods for inference and unsupervised learning with network data. I sometimes also explore applications to spatial and/or time series data, often through the lens of networks. The goal of these methods is to enable theoretically meaningful, conceptually consistent summaries and comparisons of complex datasets while ensuring robustness to statistical fluctuations and scalability to large systems. I strongly subscribe to Occam’s Razor, leading me to prefer simple models as well as Bayesian and information theoretic approaches to inference and learning.

In my research I develop new mathematical and computational methods that draw on ideas from a range of disciplines including information theory, statistical physics, Bayesian inference, spatial analysis, scientific computing, and data mining. I believe interdisciplinary thinking and research is essential for broadening the increasingly narrow scope of scientific research (despite the challenges it encounters in dissemination and evaluation). I am therefore happy to collaborate with researchers across different fields that are interested in using networks in their research.