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Louis Aslett is an Associate Professor in the Department of Mathematical Sciences at Durham University.

I have 3 primary areas of methodological research interest. The first 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 and multiparty computation methods such as homomorphic secret sharing.

My second main strand of research is in reliability theory, where interest is in the structural reliability of engineered systems, usually taken from a Bayesian perspective. Finally, I am interested in development of statistical methodology which is amenable to implementation in modern massively parallel computing architectures such as GPUs which are prevalent today — specifically this includes accelerating Bayesian modelling and genetic ancestry problems.

On the applied side of reseach I am engaged in large scale machine learning modelling with NHS Scotland via a Health Programme Fellowship at the Alan Turing Institute, the UK's national institute for data science and AI. I am project lead for SPARRA (Scottish Patients At Risk of Re-admission and Admission), which is developing the next generation of a model designed to aid GPs in prioritising primary care interventions to reduce the risk of emergency hospital admissions. This work utilises various Electronic Health Records (EHRs) for roughly 3.6 million people in Scotland (roughly 80% population coverage).

From 2013 to 2017 I was a postdoc on the EPSRC funded i-like project in Chris Holmes’ group at the University of Oxford, and Junior Research Fellow at Corpus Christi College.

I completed my PhD in 2013, entitled “MCMC for Inference on Phase-type and Masked System Lifetime Models” at Trinity College Dublin with supervisor Simon Wilson.

Before entering research I was Founder and Technical Director of 6 Internet Limited, a server hosting and application development specialist and hold a first-class BA (Hons) in Mathematics and a PhD in Mathematical Statistics from Trinity College Dublin.

Software Projects

RStudio AMIs — I am maintaining Amazon Machine Images which make deploying an RStudio Server into the Amazon EC2 service very fast and easy.

{kalis} R Package 📦 — A high performance implementation of the Li & Stephens model for local ancestry inference (local referring to a region of the genome). For a set of N phased haplotypes, kalis computes the posterior marginal probability of each haplotype copying every other haplotype by running N hidden Markov models in parallel. Extensive use is made of low level threading and CPU vector instructions. See Aslett and Christ (2024) for a peer reviewed paper on this implementation.

{mlmc} R Package 📦 — An implementation of Multi-level Monte Carlo for R. This package builds on the original GPL-2 Matlab and C++ implementations by Mike Giles (see https://people.maths.ox.ac.uk/~gilesm/mlmc/) to provide a full MLMC driver and example level samplers. Multi-core parallel sampling of levels is provided built-in. The package is available on CRAN.

{fhe} R Package 📦 (formerly {HomomorphicEncryption}) — An R package enabling statistics and machine learning researchers to exploit homomorphic encryption from R, with all computation performed in optimised C++ code. This package is not on CRAN and had to be renamed when a conflicting package was uploaded with the original name.

{PhaseType} R Package 📦 — An R package for inference on Phase-type distributions. At present, the inferential methods are fully Bayesian with two approaches provided, which are high-speed C implementations of MCMC algorithms. The package is available on CRAN.

{ReliabilityTheory} R Package 📦 — A toolkit for structural reliability theory. This includes methods of system reliability analysis based on structure functions, system signature and survival signatures. The package is available on CRAN.

{eider} R Package 📦 — Designed for extracting machine learning features from tabular data, in particular health records, in a declarative manner. Features are specified as JSON objects which contain all the necessary information required to perform a given calculation. The package is available on CRAN.

{EncryptedStats} R Package 📦 — An R package implementing traditional and novel statistical machine learning methods in a manner amenable to computation on homomorphically encrypted data. This is designed primarily for use with the {fhe} package above, but the methods can be run both encrypted and unencrypted.

Current Grants/Funding

PINCODE (Pooling INference and COmbining Distributions Exactly) (Co-I, local PI) — Funding: EPSRC
When data is stored on a disjoint collection of servers, it can be challenging to efficiently fit Bayesian models to find the whole data posterior without simply pooling the data in a central location. This arises both in big data settings where data may not realistically fit on a single server, or in privacy contexts where data cannot be shared for governance or regulatory reasons. This grant will first develop 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. These methods will be developed to operate within the constraints of homomorphic secret sharing and homomorphic encryption methods to also address aspects of data privacy. My involvement is predominantly on the computational, privacy and applications side. The grant is joint with Gareth Roberts FRS, Murray Pollock, and Hongsheng Dai.

OCEAN (On intelligenCE And Networks) (funded investigator) — Funding: ERC Synergy (backstopped by UKRI)
Major advances in machine learning have focused on a centralized paradigm with data aggregated at a central location for training. This is flawed for many real-world use cases. In particular, centralised learning risks exposing user privacy and makes inefficient use of network resources. OCEAN will develop statistical and algorithmic foundations for systems involving multiple incentive-driven learning and decision-making agents. My role is as part of the UK team developing privacy preserving statistical methods in this broader project. This grant is with Eric Moulines, Michael I. Jordan, Christian Robert, and Gareth Roberts FRS.

SPARRA (Scottish Patients At Risk of Re-admission and Admission) (PI) — Funding: multiple grants including AI for Science and Government; Alan Turing Institute internal; NHS Scotland ISD; EPSRC; The Health Foundation
I hold a health programme fellowship at the Alan Turing Institute, leading a project for NHS Scotland to update SPARRA. SPARRA is a model constructed on 3.6 million Scottish patients 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 with Co-I Catalina Vallejos, postdoc James Liley and PhD student Sam Emerson.

Atom Bank KTP (Co-I) — Funding: Innovate UK
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.

Reproducible machine learning in health data science (Co-I) — Funding: HDR UK
I am a working on the synthetic data (WP2) and reproducible safe-haven environments (WP3) work packages with Aiden Doherty (PI, WP2 lead) and Catalina Vallejos (WP3 lead).

Research outputs     (see also Google Scholar Profile)

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, 86. doi:10.1186/s12859-024-05688-8

Liley, J., Bohner, G., Emerson, Samuel R., Mateen, B. A., Borland, K., Carr, D., Heald, S., Oduro, S. D., Ireland, J., Moffat, K., Porteous, R., Riddell, S., Rogers, S., Thoma, I., Cunningham, N., Holmes, C., Payne, K., Vollmer, S. J., Vallejos, C. A. & Aslett, L. J. M. (2024), ‘Development and assessment of a machine learning tool for predicting emergency admission in Scotland’, npj Digital Medicine, 7(1), 277. doi:10.1038/s41746-024-01250-1

Hu, S., Aslett, L. J. M., Dai, H., Pollock, M. & Roberts, G. O. (2024), ‘Privacy Guarantees in Posterior Sampling under Contamination’. arXiv:2403.07772 [math.ST]. Details and download

Gisslander, K., White, A., Aslett, L. J. M., 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), 762—770. doi:10.1016/S2665-9913(24)00187-5

Christ, R. R., Wang, X., Aslett, L. J. M., Steinsaltz, D. & Hall, I. (2024) ‘Clade Distillation for Genome-wide Association Studies’. bioRxiv. doi:10.1101/2024.09.30.615852

Scott, J., White, A., Walsh, C., Aslett, L. J. M., 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(e003962). doi:10.1136/rmdopen-2023-003962

Chislett, L., Aslett, L. J. M., Davies, A. R., Vallejos, C. A. & Liley, J. (2024), ‘Ethical considerations of use of hold-out sets in clinical prediction model management’, AI and Ethics. doi:10.1007/s43681-024-00561-z

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’, medRxiv. doi:10.1101/2024.02.13.24302753

Gisslander, K., Rutherford, M., Aslett, L. J. M., Basu, N., Dradin, F., Hederman, L., Hruskova, Z., Kardaoui, H., Lamprecht, P., Lichołai, S. & others (2024). ‘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. doi:10.1136/ard-2023-224571

Scott, J., Nic an Ríogh, E., Al Nokhatha, S., Cowhig, C., Verrelli, A., Fitzgerald, T., White, A., Walsh, C., Aslett, L. J. M., DeFreitas, D., Clarkson, M., Holian, J., Griffin, M., Conlon, N., O'Meara, Y., Casserly, L., Molloy, E., Power, J., Moran, S. & Little, M. (2022). ‘ANCA-associated vasculitis in Ireland: a multi-centre national cohort study’, HRB Open Research, 5(80). doi:10.12688/hrbopenres.13651.1

Haidar-Wehbe, S., Emerson, S. R., Aslett, L. J. M. & Liley, J. (2022), ‘Optimal sizing of a holdout set for safe predictive model updating’, arXiv:2202.06374 [stat.ML]. Details and download

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. J. M., Kelleher, J. D., Power, J., Ng, J., O'Sullivan, D., Hederman, L., Basu, N., Little, M. A. & Zgaga, L., (2022), ‘The association between ambient UVB dose and ANCA-associated vasculitis relapse and onset’, Arthritis Research & Therapy 24 (147), 1—14. doi:10.1186/s13075-022-02834-6

Rogers, D. R., Aslett, L. J. M. & Troffaes, M. C. M. (2021), ‘Modelling of modular battery systems under cell capacity variation and degradation’, Applied Energy 283, 116360. Details and download.

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’, AISTATS. Details and download

Krpelik, D., Aslett, L. J. M. & Coolen, F. P. A. (2021). ‘Simultaneous Sampling for Robust Markov Chain Monte Carlo Inference’. In: Vasile, M., Quagliarella, D. (eds) Advances in Uncertainty Quantification and Optimization Under Uncertainty with Aerospace Applications doi:10.1007/978-3-030-80542-5_11

Gallagher, R. E., Aslett, L. J. M., Steinsaltz, D. & Christ, R. R. (2019), Improved Concentration Bounds for Gaussian Quadratic Forms. arXiv:1911.05720 [math.ST]. Details and download

Huang, X., Aslett, L. J. M. & Coolen, F. P. A. (2019), ‘Reliability analysis of general phased mission systems with a new survival signature’, Reliability Engineering & System Safety 189, 416—422. Details and download.

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’, Nature Scientific Reports 8. Details and download.

Aslett, L. J. M., Nagapetyan, T. & Vollmer, S. J. (2017), ‘Multilevel Monte Carlo for Reliability Theory’, Reliability Engineering & System Safety 165, 188—196. Details and download.

Esperança, P. M., Aslett, L. J. M. & Holmes, C. C. (2017), ‘Encrypted accelerated least squares regression’, AISTATS. Details and download

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, 67—88. Details and download

Aslett, L. J. M. (2016), Cryptographically secure multiparty evaluation of system reliability. arXiv:1604.05180 [cs.CR]. Details and download

Coolen, F. P. A., Coolen-Maturi, T., Aslett, L. J. M. & Walter, G. (2016), Imprecise system reliability using the survival signature, in ‘ICAMER 2016 Proceedings’. Details and download.

Aslett, L. J. M., Esperança, P. M. & Holmes, C. C. (2015), Encrypted statistical machine learning: new privacy preserving methods. arXiv:1508.06845 [stat.ML]. Details and download

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. Details and download.

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, COMPSTAT 2014, 103—110. Details and download.

Aslett, L. J. M. & Wilson, S. P. (2011), Markov chain Monte Carlo for Inference on Phase-type Models, in ‘ISI 2011 Proceedings’. Details and download.

Aslett, L. J. M. & Wilson, S. P. (2010), Modelling and Inference for Networks with Repairable Redundant Subsystems, in ‘SMRLO’10 Proceedings’. Details and download.

Technical reports & other outputs

Aslett, L. J. M. (2022). Sampling from Complex Probability Distributions: A Monte Carlo Primer for Engineers. In: Aslett, L. J. M., Coolen, F. P. A., De Bock, J. (eds) Uncertainty in Engineering. SpringerBriefs in Statistics. Details and download.

Aslett, L. J. M., Coolen, F. P. A. & De Bock, J. (editors) (2022), Uncertainty in Engineering: Introduction to Methods and Applications. SpringerBriefs in Statistics. ISBN: 978-3-030-83639-9. Details and download (all chapters open access).

Mateen, B., Kiraly, F., Vollmer, S., Aslett, L. J. M., Sonabend, R., Manolopoulou, I., Zor, C., Jankovicova, N., Cunningham, N., Kazlauskaite, I., Rao, A., Wang, C. & Oduro, S. (2019). Data Study Group final report: NHS Scotland — predicting risk of hospital admission in Scotland, Technical report, The Alan Turing Institute. doi:10.5281/zenodo.2539563.

Aslett, L. J. M., Esperança, P. M. & Holmes, C. C. (2015), A review of homomorphic encryption and software tools for encrypted statistical machine learning, Technical report, University of Oxford. arXiv:1508.06574 [stat.ML]. Details and download.

Aslett, L. J. M. (2015), ‘Doing Science Blindfold’, The Sundial, Corpus Christi College Oxford. Download (PDF).

PhD Thesis

Aslett, L. J. M. (2012), MCMC for Inference on Phase-type and Masked System Lifetime Models, PhD thesis, Trinity College Dublin. Details and download.

Supervisor: Simon P. Wilson External: Frank Coolen, Durham University.

Invited Talks

UK Government Department of Health and Social Care, 2022: ‘SPARRA: Scottish Patients At Risk of Re-admission and Admission’. Slides (PDF).

European Institute for Innovation through Health Data, 2021: Invited panelist for panel on ‘Anonymisation and Pseudonymisation’.

Van Dantzig National Seminar, Netherlands, 2019: ‘Privacy and Security in Bayesian Inference’. Slides (PDF).

Conference on Applied Statistics in Ireland, 2019, invited short course: ‘Statistics in the Amazon Cloud with R’. Slides (PDF).

Bayesian Statistics in the Big Data Era, Centre International de Rencontres Mathématiques, Marseille, France, 2018: ‘Privacy and Security in Bayesian Inference’. Slides (PDF).

CoSInES Launch, University of Warwick 2018, ‘Privacy and Security in Bayesian Inference’. Slides (PDF)

54th Gregynog Statistical Conference, 2018, invited short course: ‘Cryptography and Statistics: a short introduction’. Slides for lecture 1 & 2 (PDF); Slides for lecture 3 (PDF); R code (ZIP).

Isaac Newton Institute, Scalable Statistical Inference Workshop, 2017: ‘Towards Encrypted Inference for Arbitrary Models’. Slides (PDF), Video of Talk.

3rd UCL Workshop on the Theory of Big Data: ‘Towards Encrypted Inference for Arbitrary Models’. Slides (PDF).

Royal Statistical Society Emerging Applications Section, Privacy in Statistical Analysis Workshop 2017, ‘Doing Machine Learning Blindfolded’.

NICTA, Sydney, Australia 2016, ‘Encrypted statistical machine learning: new privacy preserving methods’.

Google Inc., European Headquarters, 2013: ‘Learning Component Reliability with Reduced Information’. Slides (PDF).

Contributed Talks, Seminars & Posters

OCEAN ISBA Satellite Workshop, Venice, Italy, 2024: ‘{BrownianMotion} R Package’.

FAIRVASC Invited Workshop, Trieste, Italy, 2024: ‘Federated Learning and Privacy’.

OCEAN Privacy Workshop, Les Houches, France, 2024: ‘Privacy Preserving Inference’.

Durham University Mathematics Colloquium, 2022: ‘From machine learning to cryptography: some adventures of a statistician’.

Poster @ Probabilistic Modeling In Genomics, Virtual, 2021: ‘kalis — An R package for quick local relatedness inference and probabilistic haplotype screening’. Download poster (PDF).

Poster @ AISTATS, Virtual, 2021: ‘Model updating after interventions paradoxically introduces bias’. Download poster (PDF).

Institute for Data Science, Durham, 2020, ‘Privacy and Confidentiality in Bayesian Inference’.

European Meeting of Statisticians, Palermo, Italy, 2019: ‘Towards Encrypted Inference for Arbitrary Models’. Slides (PDF).

1st UK Reliability Meeting, Durham, 2019: ‘Cryptographically secure multi-party evaluation of system reliability’. Slides (PDF).

University of Southampton S3RI Seminar, 2018: ‘Privacy and Security in Bayesian Inference’. Slides (PDF).

Lancaster University Seminar, 2018: ‘Privacy and Security in Bayesian Inference’. Slides (PDF).

UTOPIAE Training School II, 2018: ‘Sampling from complex probability distributions’. Slides (PDF).

Private Workshop on Parallel Monte Carlo Methods, University of Bristol, 2018: ‘Contemporaneous MCMC’. Slides (PDF).

Data Science North East Meetup, 2018: ‘Doing Data Science Blindfolded’. Slides (PDF).

BayesComp 2018, Barcelona: ‘Contemporaneous MCMC’. Slides (PDF).

Newcastle University Seminar, 2018: ‘Towards Encrypted Inference for Arbitrary Models’. Slides (PDF).

UTOPIAE Training School I, 2017: ‘Statistical Methods for System Reliability’. Slides (PDF).

Durham University Seminar, 2017: ‘Towards Encrypted Inference for Arbitrary Models’. Slides (PDF).

Privacy in Statistical Analysis, RSS Emerging Applications Section, 2017: ‘Doing Machine Learning Blindfolded’. Slides (PDF).

University of Warwick Young Researchers’ Meeting, 2016: ‘Taming the Inner Loop’. Slides (PDF).

Oxford R User Group inaugral talk: ‘Cheap and cheerful massively parallel batch R processing on EC2’. Slides (PDF).

ENBIS 2016, Sheffield: ‘Cryptographically secure multi-party evaluation of system reliability’. Slides (PDF).

ISBIS 2016, Barcelona: ‘Cryptographically secure multi-party evaluation of system reliability’. Slides (PDF).

Poster @ ISBA 2016, Sardinia: ‘Doing Statistics Blindfolded’. Download poster (PDF).

Poster @ i-like Workshop, Lancaster University, 2016: ‘Multi-level Monte Carlo for Reliability Theory’. Download poster (PDF).

Amazon Machine Learning, Berlin, 2016: ‘Doing Machine Learning Blindfolded’. Slides (PDF).

LMS Mathematical Methods in Reliability, Durham University, 2016: ‘Multi-level Monte Carlo for System Reliability Simulation’. Slides (PDF).

Université de Lille Statistics Seminar, 2016: ‘Towards Encrypted Statistics and Machine Learning’. Slides (PDF).

University of Oxford CSML Seminar, 2016: ‘Doing Statistics Blindfolded’. Slides (PDF).

2nd UCL Workshop on the Theory of Big Data (2016) Talk: ‘Scalability Issues and the Potential for Encrypted Machine Learning’. Slides (PDF).

University of Warwick Algorithms Seminar, 2015: ‘An Introduction to Homomorphic Encryption for Statistics and Machine Learning’. Slides (PDF).

RGU Research Seminar, 2015: ‘Data Science and Statistics in the Amazon Cloud with R’. Slides (PDF).

University of Warwick Young Researchers’ Meeting, 2015: ‘Background on HPC for Statistics’. Slides (PDF).

Poster @ i-like Workshop 2015: ‘Doing Statistics Blindfold’. Download poster (PDF).

CASI 2015 Talk: ‘Cryptographically secure multiparty evaluation of system reliability’. Slides (PDF).

Poster @ CASI 2015: ‘Doing Statistics Blindfold’. Download poster (PDF).

London Mathematical Society MMR @ Durham 2015 Talk: ‘Cryptographically secure multiparty evaluation of system reliability’. Slides (PDF).

Poster @ i-like Workshop 2014: ‘Coupled Hidden Markov Models: some computational challenges’. Download poster (PDF).

Durham University Seminar, 2014 (joint presentation with Frank Coolen): ‘Bayesian inference for reliability of systems and networks using the survival signature’. Frank Coolen’s slides (PDF), my slides (PDF), code (R).

University of Warwick Algorithms Seminar, 2014: ‘Coupled Hidden Markov Models: computational challenges’. Slides (PDF).

i-like Virtual Seminar, 2014: ‘Considerations in parallel algorithm design’. Slides (PDF).

GDRR 2013 Talk: ‘Parametric and Topological Inference for Masked System Lifetime Data’. Slides (PDF).

SIMRIDE 2013 Talk: ‘Learning Component Reliability with Reduced Information’. Slides (PDF).

Durham Risk Day 2012 Talk: ‘Parametric and Topological Inference for Masked System Lifetime Data’. Slides (PDF).

TCD Statistics Workshop 2012 Talk: ‘Parametric and Topological Inference for Masked System Lifetime Data’. Slides (PDF).

ISBA 2012 Talk: ‘Inference on Phase-type Models via MCMC, with application to networks of repairable redundant systems’. Slides (PDF).

CASI 2012 Talk: ‘Markov chain Monte Carlo for Phase-type Models’. Slides (PDF).

Trinity Statistics Seminar Talk: ‘Using the New TCD Statistics Cluster’. Download slides (PDF) and demo files (ZIP).

Durham Risk Day 2011 Talk: ‘Inference on Phase-type Models via MCMC with Application to Repairable Redundant Systems’. Slides (PDF).

TCD Statistics Workshop 2011 Talk: ‘Markov chain Monte Carlo for Inference on Phase-type Models’. Slides (PDF).

Poster @ ISI 2011 & BISP 7: ‘Markov chain Monte Carlo for Inference on Phase-type Models’. Download poster (PDF) and spotlight talk (PDF).

Trinity Statistics Seminar Talk: ‘GPU Programming Basics: Getting Started’.

Poster @ 9^th Valencia Meeting: ‘Networks with Repairable Redundant Subsystems: Faster Inference for Phase-type Distributions’. Poster (PDF).

Trinity Statistics Seminar Talk: ‘Speeding up R: Calling C from R’. Download slides and code (zip).

SMRLO’10 Talk: ‘Modelling and Inference for Networks with Repairable Redundant Subsystems’. Slides (PDF).

Poster and Video: IRCSET Symposium 2009, display to Irish government and general audience of work being undertaken by IRCSET funded researchers. Download poster (PDF) / video (MPEG-4).

Awards

Durham University Exceptional Contribution Award — for making a sustained contribution over and above the normal expectations of the role. 2023.

Durham University Exceptional Contribution Award — to recognise exceptional and sustained contribution to the University. 2021.

Shortlisted in Oxford University Student Union student-led teaching awards for the ‘Outstanding Supervisor’ category. Shortlist of 4 people from whole MPLS division.

Corpus Christi College Junior Research Fellowship — highly competitive fellowship award open to post-doctoral researchers in all disciplines of the University of Oxford.

University of Oxford Award for Excellence — lump sum award for having ‘consistently exceeded core responsibilities’ with ‘commitment and enthusiasm for the Department’. 2015.

Academic Service

University Tutoring (2017 — present, Durham University)

Data Science and Statistical Computing (2021/22, 2022/23, 2023/24, 2024/25)
Lecturer for 2nd year course covering Monte Carlo methods and R programming on the BSc/MMath Mathematics degree.
Course notes available online.

Advanced Statistics & Machine Learning (2019/20, 2020/21, 2021/22, 2022/23, 2024/25)
Lecturer for module covering machine learning and AI methods for classification problems on the MSc in Computational Science and Data Analysis degree.

Topics in Statistics III/IV (2018/19)
Lecturer for 3rd/4th year course covering generalised linear models on the BSc/MMath Mathematics degree.

Statistics I (2017/18)
Lecturer for first year introductory statistics course on the BSc/MMath Mathematics degree (also service course across the university).

University Tutoring (2013 — 2017, University of Oxford)

OxWaSP CDT
I was module leader for the “Scalable methods for analysis of large and complex datasets” module for the first 3 years of the Oxford-Warwick Centre for Doctoral Training programme.

MSc in Applied Statistics
Supervisor for students’ dissertations in the Statistical Science MSc programme. Past students: Dan Zhang, Ella Kaye and Xi Chen.

Graduate Lecture Series
An Introduction to Reliability Theory (2014) (slides, PDF)
Background on HPC for Statistics (2016) (Youtube video, slides, PDF)

University Tutoring (2008—2013, Trinity College Dublin)

ST4003, Data Mining
I was responsible for writing and delivering the R computer laboratory sessions for the MSISS & Mathematics course on Data Mining run by Dr Myra O’Regan. Details here.

Lab Supervision
Answering questions and helping undergraduates in numerous computer laboratory courses (R/Minitab/Excel).

Tutorials
Mathematics tutorials for mature first year business/economics students and mature first year science students. Probability & Statistics tutorials for first year undergraduate mathematics students.

Revision Lectures
Writing and delivering first and second year undergraduate short exam revision courses in ‘Introduction to Statistics’ and ‘Probability and Theoretical Statistics’ for mathematics students.

Archived Projects/Events

STATICA Compute Cluster — This is the small compute cluster of the Statistics research group at Trinity College Dublin which I was responsible for specifying, setting up and administrating during my time there.

CASI 2012 and GDRR 2013 — As one of the organising committee of both the Conference on Applied Statistics in Ireland 2012 and the Third Symposium on Games and Decisions in Reliability and Risk, I developed the online abstract submission and registration payment systems.

Computational Statistics Reading Group (2013—14) — Pierre Jacob and I organised the weekly reading group in computational statistics and machine learning in 2013—14, held from 4—5pm on Fridays.

‘The Network (2014—15)’ — Michael Salter-Townshend and I organised ‘The Network’ which provided social and academic events for DPhil students and postdocs in the Department of Statistics at the University of Oxford to meet, discuss their research and find peer support.

i-like workshop 2014 — I served on the organising committee.

Workshop on Privacy in Statistical Analysis 2017 — I helped organising this workshop with Richard Everitt under the auspices of the Royal Statistical Society Emerging Applications Section.

Contact

If you would like to get hold of me, feel free to email me at please enable JavaScript to see it!

You can also find me here on Mastodon or here on LinkedIn.