About Me

I am a physicist working on developing new mathematical and computational tools for network science and statistical physics to gain a better quantitative understanding of 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.

Most of my research is motivated by the fact that bad methodological choices will often completely undermine the validity and reproducibility of scientific analyses. This is a particular problem for network science, as it is a relatively young field with no standard accepted set of tools (for, e.g. community detection, sparsification, etc). To develop a solid toolkit for network science, our methods must be:

• Founded solely in rigorous modeling and/or scientific principles
• Robust to the structural and dynamical heterogeneity found within a particular application
• Completely free (to the extent that it is possible) from any tunable parameters that permit the overfitting of statistical fluctuations and/or enable human aesthetic and scientific confirmation biases through cherry-picking
• Scalable to large networks

Such methods will enable theoretically meaningful, conceptually consistent summaries and comparisons of complex systems while ensuring robustness to statistical fluctuations and flexibility for large systems.

In my research I primarily work on the design, optimization, and analysis of principled methods for inference and unsupervised learning with network data, with the goal of contributing to a network science toolkit that follows the above principles. I sometimes also explore applications to spatial and/or time series data, often through the lens of networks. 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 work.

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.