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Durham University

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Staff Profile

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

Personal web page

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

(email at louis.aslett@durham.ac.uk)

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 2020/21 academic year I am lecturing on Core II A: Advanced Statistics and Machine Learning, part of the MSc in Scientific Computing and Data Analysis.

Grants

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

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.

Reproducible machine learning in health data science: supporting trustworthy clinical insights, Co-Investigator

The clinical actions supported by machine learning methods can greatly differ depending on how models are built according to many factors related to both internal and external study validity. This project aims to develop reporting guidelines, scientific methods, and training material for reproducible machine learning in health data science to support trustworthy clinical inference before routine use in public health and clinical practice. Funded by HDR UK.

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.

Research Groups

Department of Mathematical Sciences

  • Probability & Statistics: Statistics
  • Probability and Statistics

Research Interests

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

Publications

Journal Article

  • Rogers, Daniel J., Aslett, Louis J. M. & Troffaes, Matthias C. M. (2021). Modelling of modular battery systems under cell capacity variation and degradation. Applied Energy 283: 116360.
  • Huang, Xianzhen, Aslett, Louis J.M. & Coolen, Frank P.A. (2019). Reliability analysis of general phased mission systems with a new survival signature. Reliability Engineering & System Safety 189: 416-422.
  • Willetts, M., Hollowell, S., Aslett, L.J.M, Holmes, C.C. & Doherty, A. (2018). Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK Biobank participants. Scientific Reports 8(1): 7961.
  • Walter, G., Aslett, L.J.M. & Coolen, F.P.A. (2017). Bayesian nonparametric system reliability using sets of priors. International Journal of Approximate Reasoning 80(1): 67-88.
  • Aslett, L. J. M., Nagapetyan, T. & Vollmer, S. J. (2017). Multilevel Monte Carlo for Reliability Theory. Reliability Engineering & System Safety 165: 188-196.
  • Aslett, L.J.M., Coolen, F.P.A. & Wilson, S.P. (2015). Bayesian inference for reliability of systems and networks using the survival signature. Risk Analysis 35(9): 1640-1651.

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.

Software

  • Aslett, L. J. M. (2015). HomomorphicEncryption - An R package for fully homomorphic encryption.

Supervises