Imperial College London


Faculty of Natural SciencesDepartment of Mathematics

Research Associate







Electrical EngineeringSouth Kensington Campus





Paul Expert is a postdoctoral researcher at the EPSRC Centre for Mathematics of Precision Healthcare (CMPH), working with Prof. Mauricio Barahona. His current research revolves around developing Laplacian based methods in graph signal processing, machine learning and persistent homology.

He studied Physics and then Statistics at the University of Geneva and received his PhD in Physics from Imperial College London in the Complexity and Networks Group under the supervision of Prof. Kim Christensen. His thesis work made use of statistical mechanics and network theoretical approaches to study various systems from the human brain to human society. As a post doctoral researcher with Prof. Federico Turkheimer in the Center for Neuroimaging Sciences at King's College London, he continued to develop novel analysis methods using network and more generally topological approaches to characterize human brain function in health, disease and altered states.

Selected Publications

Journal Articles

Expert P, de Nigris S, Takaguchi T, et al., 2017, Graph spectral characterization of the XY model on complex networks, Physical Review E, Vol:96, ISSN:2470-0045

Turkheimer FE, Leech R, Expert P, et al., 2015, The brain's code and its canonical computational motifs. From sensory cortex to the default mode network: A multi-scale model of brain function in health and disease, Neuroscience and Biobehavioral Reviews, Vol:55, ISSN:0149-7634, Pages:211-222

Petri G, Expert P, Turkheimer F, et al., 2014, Homological scaffolds of brain functional networks, Journal of the Royal Society Interface, Vol:11, ISSN:1742-5689

Lord L-D, Allen P, Expert P, et al., 2011, Characterization of the anterior cingulate's role in the at-risk mental state using graph theory, Neuroimage, Vol:56, ISSN:1053-8119, Pages:1531-1539

Expert P, Evans TS, Blondel VD, et al., 2011, Uncovering space-independent communities in spatial networks, Proceedings of the National Academy of Sciences of the United States of America, Vol:108, ISSN:0027-8424, Pages:7663-7668

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