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 2023/24 academic year I am lecturing the second year undergraduate course Data Science and Statistical Computing during Michaelmas Term. This is an optional module on the BSc/MMath Mathematics degrees, and a core module on the BSc/MMath Mathematics and Statistics degrees.

I will be on research leave during Epiphany Term.

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

• 2013: 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 European Headquarters, 2013.
• 2000: 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.
• 2000: 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.
• 2000: 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

Chapter in book

• Aslett, L. J. (2022). Sampling from Complex Probability Distributions: A Monte Carlo Primer for Engineers. In L. J. Aslett, F. P. Coolen, & J. De Bock (Eds.), Uncertainty in Engineering (15-35). Springer. https://doi.org/10.1007/978-3-030-83640-5_2

Conference Paper

• Liley, J., Emerson, S., Mateen, B., Vallejos, C., Aslett, L., & Vollmer, S. (in press). Model updating after interventions paradoxically introduces bias. In Proceedings of The 24th International Conference on Artificial Intelligence and Statistics (3916-3924)
• Esperança, P., Aslett, L., & Holmes, C. (2017). Encrypted accelerated least squares regression. In A. Singh, & J. Zhu (Eds.), Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, PMLR 54 (334-343)
• Wilson, S., Mai, T., Cogan, P., Bhattacharya, A., Robles-Sánchez, O., Aslett, L., …Roetzer, G. (2014). Using Storm for scaleable sequential statistical inference. In M. Gilli, G. González-Rodríguez, & A. Nieto-Reyes (Eds.), Proceedings of COMPSTAT 2014: 21st International Conference on Computational Statistics (hosting the 5th IASC World Conference): Geneva, Switzerland, August 19–22, 2014 (103-109)

Conference Proceeding

• Coolen, F., Coolen-Maturi, T., Aslett, L., & Walter, G. (2016). Imprecise system reliability using the survival signature. In R. Bris, V. Snášel, C. D. Khanh, & P. Dao (Eds.), Proceedings of the 1st International Conference on Applied Mathematics in Engineering and Reliability (207-214)

Edited book

• Aslett, L., Coolen, F., & De Bock, J. (Eds.). (2022). Uncertainty in Engineering - Introduction to Methods and Applications. Springer Verlag. https://doi.org/10.1007/978-3-030-83640-5

Journal Article

• Aslett, L. J. M., & Christ, R. R. (2024). kalis: a modern implementation of the Li & Stephens model for local ancestry inference in R. BMC Bioinformatics, 25(1), Article 86. https://doi.org/10.1186/s12859-024-05688-8
• Gisslander, K., Rutherford, M., Aslett, L., Basu, N., Dradin, F., Hederman, L., …Mohammad, A. J. (2023). Data quality and patient characteristics in European ANCA-associated vasculitis registries: data retrieval by federated querying. Annals of the Rheumatic Diseases, https://doi.org/10.1136/ard-2023-224571
• Scott, J., Nic an Ríogh, E., Al Nokhatha, S., Cowhig, C., Verrelli, A., Fitzgerald, T., …Little, M. A. (2022). ANCA-associated vasculitis in Ireland: a multi-centre national cohort study. HRB Open Research, 5, https://doi.org/10.12688/hrbopenres.13651.1
• Scott, J., Havyarimana, E., Navarro-Gallinad, A., White, A., Wyse, J., van Geffen, J., …and UKIVAS groups, R. (2022). The association between ambient UVB dose and ANCA-associated vasculitis relapse and onset. Arthritis Research and Therapy, 24(1), Article 147. https://doi.org/10.1186/s13075-022-02834-6
• Rogers, D. J., Aslett, L. J., & Troffaes, M. C. (2021). Modelling of modular battery systems under cell capacity variation and degradation. Applied Energy, 43, Article 116360. https://doi.org/10.1016/j.apenergy.2020.116360
• Huang, X., Aslett, L. J., & Coolen, F. P. (2019). Reliability analysis of general phased mission systems with a new survival signature. Reliability Engineering & System Safety, 189, 416-422. https://doi.org/10.1016/j.ress.2019.04.019
• Willetts, M., Hollowell, S., Aslett, L., Holmes, 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), Article 7961. https://doi.org/10.1038/s41598-018-26174-1
• Aslett, L., Nagapetyan, T., & Vollmer, S. (2017). Multilevel Monte Carlo for Reliability Theory. Reliability Engineering & System Safety, 165, 188-196. https://doi.org/10.1016/j.ress.2017.03.003
• Walter, G., Aslett, L., & Coolen, F. (2017). Bayesian nonparametric system reliability using sets of priors. International Journal of Approximate Reasoning: Uncertainty in Intelligent Systems, 80(1), 67-88. https://doi.org/10.1016/j.ijar.2016.08.005
• Aslett, L., Coolen, F., & Wilson, S. (2015). Bayesian inference for reliability of systems and networks using the survival signature. Risk Analysis, 35(9), 1640-1651. https://doi.org/10.1111/risa.12228