Dr Patrick Rubin-Delanchy

Summary

At a high level, my research is about finding statistical patterns, underlying structure, and generally making sense of big datasets.

I'm of the view that:

• being able to extract probabilistic belief from modern large-scale and complex data sources will be of significant societal benefit.
• solutions are at the intersection of mathematics, statistics and computer science.

 A lot of my research is driven by cyber-security applications, and so I am particularly interested in:

• Networks, time series, and point processes
• Machine-learning
• Anomaly detection

Biography

Patrick Rubin-Delanchy obtained a PhD in Statistics at Imperial College London in 2008, supervised by Professor Andrew Walden. He was awarded a Heilbronn fellowship in Data Science at the University of Bristol (2012) and then the University of Oxford (2016). Since July 2017, he has been an assistant (now associate) professor in Statistics at the University of Bristol.

Teaching

Introduction to Mathematical Cyber-Security and Anomaly Detection.

Keywords

• Statistics
• Machine-Learning
• Clustering
• Graphs
• Anomaly Detection

Memberships

Organisations

School of Mathematics

Research themes

• Scientific Computing
• Bayesian Modelling and Analysis
• Nonparametric Regression
• Statistical Signal Processing
• Time Series

Links

• Homepage

Recent publications

• Price-Williams, M, Heard, N & Rubin-Delanchy, P, 2019, ‘Detecting weak dependence in computer network traffic patterns by using higher criticism’. Journal of the Royal Statistical Society. Series C: Applied Statistics, vol 68., pp. 641-655
• Heard, NA & Rubin-Delanchy, P, 2018, ‘Choosing between methods of combining _p-values’. Biometrika, vol 105., pp. 239-246
• Rubin-Delanchy, P, Heard, N & Lawson, D, 2018, ‘Meta-Analysis of Mid-p-Values: Some New Results based on the Convex Order’. Journal of the American Statistical Association.
• Rubin-Delanchy, P, Priebe, C & Tang, M, 2017, ‘The generalised random dot product graph’. arXiv.
• Rubin-Delanchy, P, Priebe, C & Tang, M, 2017, ‘Consistency of adjacency spectral embedding for the mixed membership stochastic blockmodel’. arXiv.
• Griffié, J, Shlomovich, L, Williamson, D, Shannon, M, Aaron, J, Khuon, S, Burn, G, Boelen, L, Peters, R, Cope, A, Cohen, E, Owen, D & Rubin-Delanchy, P, 2017, ‘3D Bayesian cluster analysis of super-resolution data reveals LAT recruitment to the T cell synapse’. Scientific Reports, vol 7.
• Rubin-Delanchy, P, Adams, N & Heard, N, 2016, ‘Disassortativity of computer networks’.
• Rubin-Delanchy, P, Lawson, DJ & Heard, NA, 2016, ‘Anomaly detection for cyber security applications’. in: Dynamic Networks and Cyber-Security. World Scientific Publishing Co., pp. 137-156
• Heard, N & Rubin-Delanchy, P, 2016, ‘Network-wide anomaly detection via the Dirichlet process’.
• Griffié, J, Shannon, M, Bromley, C, Boelen, L, Burn, G, Williamson, D, Heard, N, Cope, A, Owen, D & Rubin-Delanchy, P, 2016, ‘A Bayesian cluster analysis method for single-molecule localization microscopy data’. Nature Protocols, vol 11., pp. 2499?2514

View complete publications list in the University of Bristol publications system