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Scalable Model-Based Clustering with Sequential Monte Carlo<\/em> <\/em>(AISTATS 2026). In online clustering problems, there is often a large amount of uncertainty over possible cluster assignments that cannot be resolved until more data are observed. This difficulty is compounded when clusters follow complex distributions, as is the case with text data. Sequential Monte Carlo (SMC) methods give a natural way of representing and updating this uncertainty over time, but have prohibitive memory requirements for large-scale problems. We propose a novel SMC algorithm that decomposes clustering problems into approximately independent subproblems, allowing a more compact representation of the algorithm state. Our approach is motivated by the knowledge base construction problem, and we show that our method is able to accurately and efficiently solve clustering problems in this setting and others where traditional SMC struggles.<\/p>\n\n\n\n
![Poster<\/a><\/div>\n<\/div>\n\n\n\n\n\n\n\nDiffusion Generative Modelling for Divide-and-Conquer MCMC<\/em> (preprint). Divide-and-conquer MCMC is a strategy for parallelising Markov Chain Monte Carlo sampling by running independent samplers on disjoint subsets of a dataset and merging their output. An ongoing challenge in the literature is to efficiently perform this merging without imposing distributional assumptions on the posteriors. We propose using diffusion generative modelling to fit density approximations to the subposterior distributions. This approach outperforms existing methods on challenging merging problems, while its computational cost scales more efficiently to high dimensional problems than existing density estimation approaches.<\/p>\n\n\n\n\narXiv<\/a><\/div>\n\n\n\nGitHub<\/a><\/div>\n<\/div>\n\n\n\n<\/p>\n\n\n\n\n\n\n\n<\/p>\n\n\n\nMRes<\/h3>\n\n\n\n<\/p>\n\n\n\nResearch proposal<\/strong>: Diffusion-Based Deep Generative Models for Assessing Safety in Autonomous Vehicles<\/em>. An introduction to deep generative models in the context of generating scenarios to test autonomous vehicle safety in simulators, with a particular focus on diffusion-based models.<\/p>\n\n\n\n\nPresentation<\/a><\/div>\n<\/div>\n\n\n\n\n\n\n\nStochastic Dynamic Optimisation<\/em>. An introduction to the properties, solution methods, and applications of Markov decision processes and stochastic games.<\/p>\n\n\n\n\nReport<\/a><\/div>\n\n\n\nPresentation<\/a><\/div>\n<\/div>\n\n\n\n\n\n\n\nThe Particle Filter<\/em>. An introduction to particle filtering and particle MCMC, with applications to epidemic modelling.<\/p>\n\n\n\n\nReport<\/a><\/div>\n<\/div>\n\n\n\n<\/p>\n\n\n\n\n\n\n\n<\/p>\n\n\n\nUndergraduate<\/h3>\n\n\n\n<\/p>\n\n\n\nMaster’s project<\/strong>: Statistics and Data Science for Text Data<\/em> (2021). An introduction to the field of natural language processing with a particular focus on language modelling. Poster and presentation focus on word embeddings.<\/p>\n\n\n\n\nReport<\/a><\/div>\n\n\n\nPoster<\/a><\/div>\n\n\n\nPresentation<\/a><\/div>\n<\/div>\n\n\n\n\n\n\n\nSTOR-i internship project<\/strong>: Approximate posterior sampling via stochastic optimisation<\/em> (2019). An overview of how stochastic gradient Markov chain Monte Carlo algorithms can be used for computationally efficient Bayesian inference.<\/p>\n\n\n\n\nPoster<\/a><\/div>\n\n\n\nPresentation<\/a><\/div>\n<\/div>\n\n\n\n<\/p>\n","protected":false},"excerpt":{"rendered":"PhD Scalable Model-Based Clustering with Sequential Monte Carlo (AISTATS 2026). In online clustering problems, there is often a large amount…<\/p>\n","protected":false},"author":43,"featured_media":0,"parent":0,"menu_order":1,"comment_status":"closed","ping_status":"closed","template":"","meta":{"slim_seo":{"title":"Research - Connie Trojan","description":"PhD Scalable Model-Based Clustering with Sequential Monte Carlo (AISTATS 2026). In online clustering problems, there is often a large amount of uncertainty over"},"footnotes":""},"class_list":["post-159","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/www.lancaster.ac.uk\/stor-i-student-sites\/connie-trojan\/wp-json\/wp\/v2\/pages\/159","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.lancaster.ac.uk\/stor-i-student-sites\/connie-trojan\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.lancaster.ac.uk\/stor-i-student-sites\/connie-trojan\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.lancaster.ac.uk\/stor-i-student-sites\/connie-trojan\/wp-json\/wp\/v2\/users\/43"}],"replies":[{"embeddable":true,"href":"https:\/\/www.lancaster.ac.uk\/stor-i-student-sites\/connie-trojan\/wp-json\/wp\/v2\/comments?post=159"}],"version-history":[{"count":13,"href":"https:\/\/www.lancaster.ac.uk\/stor-i-student-sites\/connie-trojan\/wp-json\/wp\/v2\/pages\/159\/revisions"}],"predecessor-version":[{"id":522,"href":"https:\/\/www.lancaster.ac.uk\/stor-i-student-sites\/connie-trojan\/wp-json\/wp\/v2\/pages\/159\/revisions\/522"}],"wp:attachment":[{"href":"https:\/\/www.lancaster.ac.uk\/stor-i-student-sites\/connie-trojan\/wp-json\/wp\/v2\/media?parent=159"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}](\"https:\/\/virtual.aistats.org\/media\/PosterPDFs\/AISTATS%202026\/13750.png?t=1777552283.2516727\"){kind=link}