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Durham University

Department of Mathematical Sciences

Academic Staff

Louis Aslett, BA (Mod), PhD Trinity College Dublin

Personal web page

Assistant Professor, Statistics in the Department of Mathematical Sciences
Telephone: +44 (0) 191 33 43067
Room number: CM212

(email at

Research Groups

  • Probability & Statistics: Statistics
  • Probability and Statistics

Research Interests

  • Cryptography and Privacy in Statistics
  • Reliability Theory
  • Bayesian Statistics
  • MCMC
  • Computational Statistics and High Performance Computing

Indicators of Esteem

  • Health Programme Fellow, The Alan Turing Institute, London (2018-20):

    Partially seconded to The Alan Turing Institute, the UK national institute for data science and artificial intellegence to lead the SPARRA project.
    Currently co-lead of the Analytics Workstream in the DECOVID project, a collaboration between Turing, University Hospitals Birmingham and University College London Hospitals, responding to the Covid-19 pandemic.

  • Invited talks:
    • Van Dantzig National Seminar, Netherlands, 2019.
    • Conference on Applied Statistics Ireland, Trinity College Dublin, 2019.
    • Bayesian Statistics in the Big Data Era, Centre International de Rencontres Mathématiques, Marseille, France, 2018.
    • Isaac Newton Institute, University of Cambridge. Scalable Statistical Inference Workshop, 2017.
    • 3rd UCL Workshop on the Theory of Big Data, 2017.
    • Google Inc., European Headquarters, 2013.


Journal Article

Conference Paper

  • Esperança, P. M., Aslett, L. J. M. & Holmes, C. C. (2017), Encrypted accelerated least squares regression, in Singh, Aarti & Zhu, Jerry eds, Proceedings of Machine Learning Research 54: The 20th International Conference on Artificial Intelligence and Statistics. Fort Lauderdale, Florida, PMLR, Fort Lauderdale, FL, USA, 334-343.
  • Wilson, S. P., Mai, T., Cogan, P., Bhattacharya, A., Robles-Sánchez, O., Aslett, L. J. M., Ó Ríordáin, S. & Roetzer, G. (2014), Using Storm for scaleable sequential statistical inference, in Gilli, Manfred, González-Rodríguez, Gil & Nieto-Reyes, Alicia eds, 21st International Conference on Computational Statistics (COMPSTAT 2014). Geneva, Switzerland, International Association for Statistical Computing, Geneva, 103-109.


Current Research

My current primary research interest is at the interface between cryptography and statistics, with the focus on privacy preserving statistical analyses. My personal interest is on the statistics side of this fusion, developing novel statistical methodology which is amenable to use in the constrained environment of encrypted computation made possible by recent developments in homomorphic encryption.

My other main strand of research is in reliability theory, where interest is in the structural reliability of engineered systems, usually taken from a Bayesian perspective. I also have research interests in computational acceleration of Hidden Markov Models (HMMs) as used in genetics which result in intractable inference as population sizes grow. Threaded through all these research interests is a particular interest in modern massively parallel computing architectures such as GPUs and the development of statistical methodology which is amenable to implementation in such environments.

Current Teaching

In the 2019/20 academic year I am teaching on Core II A: Advanced Statistics and Machine Learning, part of the MSc in Scientific Computing and Data Analysis.


SPARRA (Scottish Patients At Risk of Re-admission and Admission)

I am a Health Programme Fellow at the Alan Turing Institute, leading on the SPARRA project for NHS Scotland. SPARRA is a model constructed on the entire Scottish population using centralised NHS data in order to predict those patients who require early primary care intervention to reduce the risk of emergency hospital admission.

This work is funded by a grant from the AI for science and government (ASG) research programme, as well as funding from Public Health Scotland.

Atom Bank KTP

Myself and Camila Caiado are running the Durham part of a Knowledge Transfer Partnership between Atom Bank, Newcastle University and Durham University. The project is exploring the use of encrypted statistical methods in mortgage book modelling.