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HKU-IDS Scholar

(Chairman, HKU IDS Seminar Committee)

Assistant Professor
HKU Musketeers Foundation Institute of Data Science and
Department of Urban Planning and Design, HKU (852) 3910 2336
P307I, Graduate House, HKU
Department of Urban Planning and Design
About Me

Alec is a physicist interested in the theory of complex networks, statistical physics, as well as their applications to urban and social systems. The mathematical and computational methods he develops in his research draw on ideas from a range of disciplines including statistical physics, information theory, Bayesian inference, scientific computing, and machine learning. He received his PhD in Physics at the University of Michigan in 2021 under the supervision of Mark Newman and joined HKU as an Assistant Professor in 2022. His main research interests lie in developing principled unsupervised learning methods for noisy network data and improving the efficiency and interpretability of statistical inference methods for networks. He also adapts and applies these techniques to uncover new insights about the structure and dynamics of urban mobility as well as the underlying topology of geographical data.

Current Research Project

Alec is currently working on a number of projects related to the theory of complex networks, statistical inference, and their applications to urban systems. He is developing principled, flexible, and interpretable unsupervised learning methods for network data in a variety of forms (temporal and multilayer networks, hypergraphs, etc), which are robust to overfitting and do rely on the ad hoc heuristics commonly used in modern machine learning methodology. These methods typically require the formulation of new learning objectives as well as novel sampling and optimization techniques to reveal the patterns of interest and distinguish statistically meaningful structure from noise. Alec is also developing improved computational methods to enable the scaling of inference methods to large networks as well as robust, interpretable comparisons of network structures based on fundamental principles. Finally, he is applying these methods to understand the structure and dynamics of mobility networks as well as spatially embedded phenomena such as socioeconomic inequality and segregation.

Selected Publications
Research Interests
Network Science, Statistical Physics, Complex Systems, Statistical Inference, Urban Science 
Past Seminar
Invited Presentations
  • Constructing hypergraphs from temporal data’’. NetSci. Quebec City, Canada. June 2024. 
  • From Hubs to Hypergraphs: Nonparametric Inference for Network Data with the MDL Principle”. Network Science Institute, Northeastern University. May 2024.
  • Nonparametric Inference for Network Data with the Minimum Description Length Principle”. Department of Mathematics and Statistics, University of Vermont. April 2024. 
  • Compressing network populations with modal networks reveals structural diversity”. NetSci. Vienna, Austria. July 2023. 
  • Improved algorithms for statistical inference with complex network data: Loopy graphical models and parameter-free regionalization”. Department of Physics, Hong Kong University of Science and Technology. February 2023. 
  • Complex Network Inference: Efficient Algorithms and Insights for Urban Spatial Segregation”. Department of Physics and Astronomy, University of Rochester. November 2022.