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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 Kirkley is an Assistant Professor jointly appointed in the Institute of Data Science and Department of Urban Planning and Design at HKU. He obtained his PhD in physics at the University of Michigan and did his undergraduate studies at the University of Rochester. His research focuses on the theory of complex networks and the statistical physics of urban systems, with specific interests involving the characterization of structure in networks with metadata, the development of analysis methods and algorithms for statistical inference with network data, the structure and dynamics of human mobility, and the spatial manifestation of socioeconomic inequality. His research involves a balance of mathematical theory, computer simulation, and analysis of empirical data. His overarching goal is to develop physics-inspired mathematical and computational methods to aid in the understanding and modeling of complex networks and urban systems.

Current Research Project

Alec is currently working on a number of projects related to both the theory of complex networks and the statistical physics of urban systems. Within network theory, he is developing information theoretic methods for extracting large-scale patterns in network data from the perspective of data compression. Along this direction, he is using the Minimum Description Length (MDL) principle to develop principled, scalable, parameter-free statistical inference algorithms for identifying partitions of networks and their metadata. These projects will address a number of shortcomings of existing analysis methods for complex networks and provide domain experts with a set of efficient, easy-to-use tools for a range of applications. Within the urban realm, Alec is working on quantifying the impact of urban network structure (in particular of street and human mobility networks) on city-level outcomes such as accessibility, congestion, and prosperity. He is using tools from statistical physics, network science, and information theory to study how the many small-scale interactions facilitated by these networks can give rise to these complex large-scale phenomena, as well as how we can use this insight to design interventions to improve these urban networks. He is also applying the tools he has developed for complex network inference to urban systems, where these techniques can uncover new insights about spatial segregation and administrative redistricting among other problems.

Selected Publications
Research Interests
Network science; Complex systems; Urban Science; Statistical physics.
Past Seminar
Invited Presentations
  • Talk on “Regionalization through optimal information compression on spatial networks.” NetSci. Shanghai, China. July 2022.
  • Talk on “The Paradox of Interdisciplinary Collaboration” (with Shihui Feng). NetSci. Indiana University, USA. July 2021.
  • Talk on “Multimodal Community Structure in Networks.” NetSci. Indiana University, USA. July 2021.
  • Talk on “Probabilistic Models on Networks with Loops.” NetSci. Rome, Italy. September 2020.