Jason Lu
Using Key Cycles in Transition Networks to Identify High-Information Partitions
Recent work has been done by Jiang et al. to identify key cycles in networks. This project will apply this work to the transition networks produced by applying time delay embedding and state space partitioning to time-series data, with the goal of identifying generating or high-information partitions. This project will research the relationship between key cycles in transition networks, and the possibly generating or high-information nature of the partitions from which the networks are produced.
Jason Lu
The University of Western Australia
Jason is passionate about using maths to explore and solve real world problems. Currently, his interests lie in complex and dynamical systems, and in particular, the modelling of complex networks. However, his interests are always evolving, so he’s always excited to learn new maths.
Jason spent his first three years at The University of Western Australia studying maths and electrical engineering; in 2024, he plans to undertake an honours year with the UWA Complex Systems Group.
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