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.

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.

• A. Kirkley, Inference of dynamic hypergraph representations in temporal interaction data. Physical Review E 109, 054306 (2024). https://journals.aps.org/pre/abstract/10.1103/PhysRevE.109.054306
• A. Kirkley, Identifying hubs in directed networks. Physical Review E 109, 034310 (2024). https://journals.aps.org/pre/abstract/10.1103/PhysRevE.109.034310
• T. P. Peixoto and A. Kirkley, Implicit models, latent compression, intrinsic biases, and cheap lunches in community detection. Physical Review E 108, 024309 (2023). https://journals.aps.org/pre/abstract/10.1103/PhysRevE.108.024309
• A. Kirkley, A. Rojas, M. Rosvall, and J-G. Young, Compressing network populations with modal networks reveals structural diversity. Communications Physics 6, 148 (2023). https://www.nature.com/articles/s42005-023-01270-5
• A. Kirkley, Spatial regionalization based on optimal information compression. Communications Physics 5, 249 (2022). https://www.nature.com/articles/s42005-022-01029-4
• A. Kirkley, G. T. Cantwell, and M. E. J. Newman, Belief propagation for networks with loops. Science Advances 7, eabf1211 (2021). https://www.science.org/doi/10.1126/sciadv.abf1211

Network Science, Statistical Physics, Complex Systems, Statistical Inference, Urban Science