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 Lecturing

In the 2025/26 academic year I lecture the first half of the new second year undergraduate module ‘Data Science and Statistical Modelling’ during Epiphany 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 also lecture the UK national Academy for PhD Training in Statistics module ‘Statistical Machine Learning’.

Grants

PINCODE (Pooling INferences and Combining Distributions Exactly), Co-Investigator, local-PI

PINCODE is developing a general framework for fusion methods based on the stochastic simulation of coalescing Markov processes with the property that their common coalesced value comes from the combined posterior distribution.  This includes careful attention to computational efficiency, software implementation, and construction of privacy aware methods within this framework.  Overall PI is Gareth Roberts FRS, funded by EPSRC.

OCEAN (On intelligenCE And Networks), funded investigator

OCEAN gathers researchers from diverse backgrounds to provide the foundation for the next generation of machine learning algorithms, where multiple agents engage over a network, operate over lengthy stretches of time in a shared environment, and engage in interactions that may be either collaborative or competitive.  Overall PIs Eric Moulines, Michael I. Jordan, Christian Robert and Gareth Roberts FRS, funded by ERC Synergy.

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

• 2024: Statistics and Computing: Associate Editor of Statistics and Computing.
• 2021: The R Journal: Associate Editor of The R Journal.
• 2020: FAIRVASC 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.
• 2018: Royal Statistical Society North-East Section: Committee member (2018–)
  Secretary (2024–)
• 2018: Health Programme Fellow, The Alan Turing Institute, London: Partially seconded to The Alan Turing Institute, the UK national institute for data science and artificial intellegence to lead the SPARRA project.
  Also 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.
• 2018: Royal Statistical Society Statistical Computing and Machine Learning Section: Committee member
• 2013: Invited talks:
  • Bayescomp, Singapore, 2025, invited session speaker.
  • UK Government Department of Health and Social Care, 2022.
  • European Institute for Innovation through Health Data, 2021, invited panelist.
  • 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.

Chapter in book

• Sampling from Complex Probability Distributions: A Monte Carlo Primer for Engineers
  Aslett, L. J. M. (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

• Model updating after interventions paradoxically introduces bias
  Liley, J., Emerson, S., Mateen, B., Vallejos, C., Aslett, L., & Vollmer, S. (2021, December). Model updating after interventions paradoxically introduces bias. Presented at The 24th International Conference on Artificial Intelligence and Statistics, Virtual
• Encrypted accelerated least squares regression
  Esperança, P., Aslett, L., & Holmes, C. (2017, December). Encrypted accelerated least squares regression. Presented at The 20th International Conference on Artificial Intelligence and Statistics, Fort Lauderdale, Florida
• Imprecise system reliability using the survival signature
  Coolen, F., Coolen-Maturi, T., Aslett, L., & Walter, G. (2016, May). Imprecise system reliability using the survival signature. Presented at 1st International Conference on Applied Mathematics in Engineering and Reliability, Ho Chi Minh City, Vietnam
• Using Storm for scaleable sequential statistical inference.
  Wilson, S., Mai, T., Cogan, P., Bhattacharya, A., Robles-Sánchez, O., Aslett, L., Ó Ríordáin, S., & Roetzer, G. (2014, December). Using Storm for scaleable sequential statistical inference. Presented at 21st International Conference on Computational Statistics (COMPSTAT 2014), Geneva, Switzerland

Edited book

• Uncertainty in Engineering - Introduction to Methods and Applications
  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

• Statistical disaggregation—A Monte Carlo approach for imputation under constraints
  Hu, S., Dai, H., Meng, F., Aslett, L., Pollock, M., & Roberts, G. O. (online). Statistical disaggregation—A Monte Carlo approach for imputation under constraints. Scandinavian Journal of Statistics, https://doi.org/10.1111/sjos.12790
• Ethical considerations of use of hold-out sets in clinical prediction model management
  Chislett, L., Aslett, L. J. M., Davies, A. R., Vallejos, C. A., & Liley, J. (online). Ethical considerations of use of hold-out sets in clinical prediction model management. AI and Ethics, https://doi.org/10.1007/s43681-024-00561-z
• Holdout Sets for Safe Predictive Model Updating
  Haidar-Wehbe, S., Emerson, S. R., Aslett, L. J., & Liley, J. (2025). Holdout Sets for Safe Predictive Model Updating. Annals of Applied Statistics, 19(2), 1190-1213. https://doi.org/10.1214/24-AOAS1982
• Differential behaviour of a risk score for emergency hospital admission by demographics in Scotland—A retrospective study
  Thoma, I., Rogers, S., Ireland, J., Porteous, R., Borland, K., Vallejos, C. A., Aslett, L. J. M., & Liley, J. (2024). Differential behaviour of a risk score for emergency hospital admission by demographics in Scotland—A retrospective study. PLoS Digital Health, 3(12), Article e0000675. https://doi.org/10.1371/journal.pdig.0000675
• Data-driven subclassification of ANCA-associated vasculitis: model-based clustering of a federated international cohort.
  Gisslander, K., White, A., Aslett, L., Hrušková, Z., Lamprecht, P., Musiał, J., Nazeer, J., Ng, J., O'Sullivan, D., Puéchal, X., Rutherford, M., Segelmark, M., Terrier, B., Tesař, V., Tesi, M., Vaglio, A., Wójcik, K., Little, M. A., & Mohammad, A. J. (2024). Data-driven subclassification of ANCA-associated vasculitis: model-based clustering of a federated international cohort. The Lancet Rheumatology, 6(11), e762-e770. https://doi.org/10.1016/S2665-9913%2824%2900187-5
• Computable phenotype for real-world, data-driven retrospective identification of relapse in ANCA-associated vasculitis
  Scott, J., White, A., Walsh, C., Aslett, L., Rutherford, M. A., Ng, J., Judge, C., Sebastian, K., O’Brien, S., Kelleher, J., Power, J., Conlon, N., Moran, S. M., Luqmani, R. A., Merkel, P. A., Tesar, V., Hruskova, Z., & Little, M. A. (2024). Computable phenotype for real-world, data-driven retrospective identification of relapse in ANCA-associated vasculitis. RMD Open, 10(2), Article e003962. https://doi.org/10.1136/rmdopen-2023-003962
• kalis: a modern implementation of the Li & Stephens model for local ancestry inference in R
  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
• Development and assessment of a machine learning tool for predicting emergency admission in Scotland
  Liley, J., Bohner, G., Emerson, S., Mateen, B., Borland, K., Carr, D., Heald, S., Oduro, S., Ireland, J., Moffat, K., Porteous, R., Riddell, S., Cunningham, N., Holmes, C., Payne, K., Vollmer, S., Vallejos, C., & Aslett, L. (2024). Development and assessment of a machine learning tool for predicting emergency admission in Scotland. Nature, 7, Article 277. https://doi.org/10.1038/s41746-024-01250-1
• Data quality and patient characteristics in European ANCA-associated vasculitis registries: data retrieval by federated querying
  Gisslander, K., Rutherford, M., Aslett, L., Basu, N., Dradin, F., Hederman, L., Hruskova, Z., Kardaoui, H., Lamprecht, P., Lichołai, S., Musial, J., O’Sullivan, D., Puechal, X., Scott, J., Segelmark, M., Straka, R., Terrier, B., Tesar, V., Tesi, M., Vaglio, A., …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, 83(1), 112-120. https://doi.org/10.1136/ard-2023-224571
• ANCA-associated vasculitis in Ireland: a multi-centre national cohort study
  Scott, J., Nic an Ríogh, E., Al Nokhatha, S., Cowhig, C., Verrelli, A., Fitzgerald, T., White, A., Walsh, C., Aslett, L. J., DeFreitas, D., Clarkson, M. R., Holian, J., Griffin, M. D., Conlon, N., O’Meara, Y., Casserly, L., Molloy, E., Power, J., Moran, S. M., & 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
• The association between ambient UVB dose and ANCA-associated vasculitis relapse and onset
  Scott, J., Havyarimana, E., Navarro-Gallinad, A., White, A., Wyse, J., van Geffen, J., van Weele, M., Buettner, A., Wanigasekera, T., Walsh, C., Aslett, L., Kelleher, J., Power, J., Ng, J., O’Sullivan, D., Hederman, L., Basu, N., Little, M., Zgaga, L., & 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
• Modelling of modular battery systems under cell capacity variation and degradation
  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
• Reliability analysis of general phased mission systems with a new survival signature
  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
• Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK Biobank participants
  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
• Multilevel Monte Carlo for Reliability Theory
  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
• Bayesian nonparametric system reliability using sets of priors
  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
• Bayesian inference for reliability of systems and networks using the survival signature
  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