Staff profile

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 2022/23 academic year I am lecturing the second year undergraduate course Data Science and Statistical Computing. This is an optional module on the BSc/MMath Mathematics degrees, and a core module on the BSc/MMath Mathematics and Statistics degrees.

I am also lecturing on the MSc in Scientific Computing and Data Analysis degree, the submodule on Classification in Core II A: Advanced Statistics and Machine Learning.

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.

This project recently concluded and was awarded "A - Outstanding" in the final grading by assessors.

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

• Statistics

• 0000: 01FAIRVASC consortium: Advisor to the FAIRVASC.eu consortium, a pan-European research network aiming to link vasculitis registries across Europe into a ‘single European dataset’, and thus open the door to new research into these challenging diseases. My role is advising on privacy preserving statistical methodology.
• 0000: 02Health 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.
• 0000: 03Academic Service:
  • Associate Editor, The R Journal
  • Committee member, Royal Statistical Society North-East Section
  • Committee member, Royal Statistical Society Statistical Computing and Machine Learning Section (2018 — 21)
  • Reviewer for numerous international statistics journals
• 0000: 99Invited 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.

Chapter in book

• Aslett, Louis J. M. (2022). Sampling from Complex Probability Distributions: A Monte Carlo Primer for Engineers. In Uncertainty in Engineering. 15-35.

Conference Paper

• Liley, J., Emerson, S.R., Mateen, B.A., Vallejos, C.A., Aslett, L.J.M. & Vollmer, S.J. (2021), Model updating after interventions paradoxically introduces bias, Proceedings of Machine Learning Research 130: The 24th International Conference on Artificial Intelligence and Statistics. Virtual, PMLR, 3916-3924.
• 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.

Edited book

• L.J.M. Aslett, F.P.A. Coolen & J. De Bock (2022). Uncertainty in Engineering - Introduction to Methods and Applications. SpringerBriefs in Statistics. Springer.

Journal Article

• Scott, Jennifer, Nic an Ríogh, Eithne, Al Nokhatha, Shamma, Cowhig, Cliona, Verrelli, Alyssa, Fitzgerald, Ted, White, Arthur, Walsh, Cathal, Aslett, Louis J. M., DeFreitas, Declan, Clarkson, Michael R., Holian, John, Griffin, Matthew D., Conlon, Niall, O’Meara, Yvonne, Casserly, Liam, Molloy, Eamonn, Power, Julie, Moran, Sarah M. & Little, Mark A. (2022). ANCA-associated vasculitis in Ireland: a multi-centre national cohort study. HRB Open Research 5: 80.
• Scott, J, Havyarimana, E, Navarro-Gallinad, A, White, A, Wyse, J, van Geffen, J, van Weele, M, Buettner, A, Wanigasekera, T, Walsh, C, Aslett, LJM, Kelleher, JD, Power, J, Ng, J, O’Sullivan, D, Hederman, L, Basu, N, Little, MA, Zgaga, L & RKD and UKIVAS groups (2022). The association between ambient UVB dose and ANCA-associated vasculitis relapse and onset. Arthritis Research & Therapy 24(1): 147.
• 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.
• Aslett, L. J. M., Nagapetyan, T. & Vollmer, S. J. (2017). Multilevel Monte Carlo for Reliability Theory. Reliability Engineering & System Safety 165: 188-196.
• 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., 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.