Patrick Rubin-Delanchy
Professor of Statistics, School of Mathematics, University of Bristol


Contact details

Office GA.13 (Virtual office
Fry Building, School of Mathematics
University of Bristol
University Walk, Bristol, BS8 1TW

My research is about developing methods to discover hidden structure (e.g. clusters, trees, manifolds) in complex data (e.g. large dynamic networks, point processes, high-dimensional data).

Interests include: clustering, anomaly detection, manifold estimation, topological data analysis, dimension reduction, high-dimensional statistics, (graph) embedding, dynamic networks, spectral methods, bias/variance trade-offs, statistical/computational trade-offs, model selection, nonparametric statistics, exploratory data analysis, representation learning and machine learning.

Applications in biosciences, healthcare, (cyber-)security, societal resilience, environmental protection and more. For example, unfolded spectral embedding is used for anti-corruption.

Current and former post-docs and PhD students (group photo):


Two papers at NeurIPS 2023; one selected for spotlight presentation:
I am associate editor for the Journal of the Royal Statistical Society, Series B (Statistical Methodology) (June 2023)

Our paper: Hannah Sansford, Alexander Modell, Nick Whiteley, PRD. "Implications of sparsity and high triangle density for graph representation learning"" was among the 32 selected for oral presentation at AISTATS 2023, of ~2000 submissions. (April 2023)

We have won an EPSRC Programme Grant on Network Stochastic Processes and Time Series (NeST), a multi-million pound award, between Imperial, Oxford, Bath, LSE, York and Bristol [press release].
This 6-year programme (2022-2028) will study large dynamic networks, with applications in medicine, transportation, cybersecurity, the environment, finance, biology and economics. (Nov 2022)

Research opportunities

Multiple PhD and postdoctoral research positions are to be opened for NeST. Please contact me if you want to discuss these or other NeST research/industrial collaboration opportunities.

More generally, I'm always happy to hear from students interested in doing research, e.g. a PhD. Fundamentally, you'll need to enjoy doing maths — everything else you can learn.