The Yaliraki group is interested in the emergent properties of self-assembling systems in confined environments. Examples from biology include the mechanisms of fibril and viral capsid formations. Another area of interest is the electronic properties of molecular scale junctions. A unifying theme of our work is how geometry and topology affect the dynamics of systems at different scales. Emphasis is on coarse-graining and system reduction approaches.
et al., Data-driven unsupervised clustering of online learner behaviour ￼, Npj Science of Learning, ISSN:2056-7936
et al., 2019, From free text to clusters of content in health records: An unsupervised graph partitioning approach, Applied Network Science, Vol:4, ISSN:2364-8228
Zhang H, Salazar JD, Yaliraki SN, 2018, Proteins across scales through graph partitioning: application to the major peanut allergen Ara h 1, Journal of Complex Networks, Vol:6, ISSN:2051-1310, Pages:679-692
Hodges M, Barahona M, Yaliraki S, 2018, Allostery and cooperativity in multimeric proteins: bond-to-bond propensities in ATCase, Scientific Reports, Vol:8, ISSN:2045-2322
et al., From Text to Topics in Healthcare Records: An Unsupervised Graph Partitioning Methodology, 2018 KDD Conference Proceedings - MLMH: Machine Learning for Medicine and Healthcare