For over 20 years, Lancaster University has been at the forefront of research in Statistics and Operational Research in the UK, and is internationally leading in these areas.
It has established an excellent track record of theoretical innovation arising from real world challenges. STOR-i builds on this by linking with leading UK industries, encouraging an integrated approach to research between academia and industry.
The Statistics and Operational Research (STOR) group at Lancaster University is at the forefront of research in Europe and beyond.
We have internationally recognized expertise that ranges widely across forecasting, optimization, simulation, statistical modelling and inference, with a particularly strong focus on computationally intensive approaches. This group have ranked in the top five universities in the UK in every review of RAE/REF since 1992.
Other indicators of Lancaster’s leadership of the STOR discipline include:
At the heart of STOR-i’s research philosophy is a continuous cycle of research that combines methodology and real-world applications in statistics, operational research (OR), and industry.
STOR-i builds on Lancaster University's strong foundation of statistics and operational research by emphasising hands-on, collaborative research, embracing and developing key methodological advances whilst working closely with industrial, charity, and academic partners to develop solutions that address specific problems.
Many of our partners have large teams of in-house analysts and share a strong interest in the need for high-quality methodological research in statistics and operational research. STOR-i's partnerships therefore aid in the development of advanced research methods that have a significant impact in both academic and industrial areas.
Discover STOR-i's primary research themesSTOR-i is committed to working closely with industry to ensure that real-world problems are fully considered when developing and analysing methods. Many of our industrial partners have large teams of in-house analysts and share a strong interest in the need for high-quality methodological research in statistics and operational research. STOR-i's partnerships therefore aid in the development of advanced research methods that have a significant impact in both academic and industrial areas.
Learn more about our current partners
PhD projects at STOR-i result from collaboration between academic supervisors and industrial partners, focused on addressing significant real-world problems. Our students benefit from interdisciplinary supervision, which allows them to explore their research from multiple angles. The strong link to industry in many projects means the work is both innovative and highly relevant for practical applications, ensuring that the research of our students has meaningful impacts both within academia and in wider industry contexts.
Explore previous students' PhD projectsBelow you will find a list of all the publications produced or in press from our current students and recent alumni.
Bold, M. and Goerigk, M. (In Press). Recoverable Robust Single Machine Scheduling with Polyhedral Uncertainty. Journal of Scheduling. Pre-print: https://arxiv.org/pdf/2011.06284
Clarkson, J., Lin, K. Y. (In Press). Computing Optimal Strategies for a Search Game in Discrete Locations. INFORMS Journal on Computing.
Greenstreet, P., Jaki, T., Bedding, A. and Mozgunov, P., (2023). A preplanned multi-stage platform trial for discovering multiple superior treatments with control of FWER and power. https://arxiv.org/abs/2308.12798
Murphy, C., Tawn, J.A., Varty, Z. (In Press). Automated threshold selection and associated inference uncertainty for univariate extremes. Journal of Technometrics.
Notice, D., Kheiri, A., Pavlidis, N.G.: (In Press). The algorithm selection problem for solving Sudoku with metaheuristics, in 2023 IEEE Congress on Evolutionary Computation.
André, L., Wadsworth, J., and O’Hagan, A. (2024). Joint modelling of the body and tail of bivariate data. Computational Statistics Data Analysis, 189:107841. https://doi.org/10.1016/j.csda.2023.107841
Davison, M., Zografos, K., & Kheiri, A. (2024). Modelling and Solving the University Course Timetabling Problem with Hybrid Teaching Considerations. Journal of Scheduling. Available at: https://link.springer.com/article/10.1007/s10951-024-00817-w
Davison, M., Kheiri, A., and Zografos, K.G. (2024). Matheuristic for Approximating a Frontier for a Many-objective University Timetabling Problem. Proceedings of The 14th International Conference on the Practice and Theorgy or Automated Timetabling, PATAT 2024. Available at: https://patatconference2024.dtu.dk/-/media/websites/patatconference2024/patat/patat-2024-proceedings.pdf
Greenstreet, P., Jaki, T., Bedding, A., Harbron, C., & Mozgunov, P. (2024). A multi‐arm multi‐stage platform design that allows preplanned addition of arms while still controlling the family‐wise error. Statistics in Medicine, 43(19), 3613-3632. Available at: https://onlinelibrary.wiley.com/doi/full/10.1002/sim.10135
Murphy-Barltrop, C.J.R., Mackay, E. & Jonathan, P. (2024). Inference for bivariate extremes via a semi-parametric angular-radial model. Extremes. Available at: https://doi.org/10.1007/s10687-024-00492-2
Murphy-Barltrop, C.J.R., Wadsworth, J.L. & Eastoe, E.F. (2024). Improving estimation for asymptotically independent bivariate extremes via global estimators for the angular dependence function. Extremes. Available at: https://doi.org/10.1007/s10687-024-00490-4
Murphy-Barltrop, C. J. R., & Wadsworth, J. L. (2024). Modelling non-stationarity in asymptotically independent extremes. Computational Statistics & Data Analysis, 199, 108025. Available at: https://doi.org/10.1016/j.csda.2024.108025
Speers, M., Randell, D., Tawn, J.A., and Jonathan, P. (2024). Estimating metocean environments associated with extreme structural response to demonstrate the dangers of environmental contour methods. Ocean Engineering, Volume 311, Part 1. Available at: https://doi.org/10.1016/j.oceaneng.2024.118754
Yang, Z., Eckley, I. A., & Fearnhead, P. (2024). A communication-efficient, online changepoint detection method for monitoring distributed sensor networks. Statistics and Computing, 34(3), 1-16.
Aicher, C., Putcha, S., Nemeth, C., Fearnhead, P., Fox, E.B. (2023). Stochastic Gradient MCMC for Nonlinear State Space Models. Bayesian Analysis (Advance Publication) 1 - 23, 2023. https://doi.org/10.1214/23-BA1395
Black, B., Ainslie, R., Dokka, T., and Kirkbride, C. (2023). Distributionally robust resource planning under binomial demand intakes. European Journal of Operational Research 306(1):227-242. https://doi.org/10.1016/j.ejor.2022.08.019
Clarkson, J., Lin, K. Y., Glazebrook, K. D. (2023). A Classical Search Game in Discrete Locations. Mathematics of Operations Research, 48(2):687-707
Clarkson, J., Voelkel, M. A., Sachs, A.-L., & Thonemann, U. W. (2023). The periodic review model with independent age-dependent lifetimes. Production and Operations Management, 32, 813– 828. https://doi.org/10.1111/poms.13900
Daniells, L., Mozgunov, P., Bedding, A., Jaki, T. (2023). A comparison of Bayesian information borrowing methods in basket trials and a novel proposal of modified exchangeability-nonexchangeability method. Statistics in Medicine. 42(24): 4392–4417. https://doi.org/10.1002/sim.9867
D’Arcy, E., Murphy-Barltrop, C.J.R., Shooter, R., and Simpson, E. (2023). A marginal modelling approach for predicting wildfire extremes across the contiguous United States. Extremes 26, 381–398. https://doi.org/10.1007/s10687-023-00469-7
D'Arcy, E., Tawn, J.A., Joly, A., Sifnoti, D.E. (2023). Accounting for seasonality in extreme sea-level estimation. Ann. Appl. Stat. 17(4): 3500 - 3525. https://doi.org/10.1214/23-AOAS1773
Gillam, J., Killick, R., Taylor, S.A., Heal, J., Norwood, B. (2023). Identifying Irregular Activity Sequences: an Application to Passive Household Monitoring. Royal Statistical Society: Series C.
Graham, E., Harbron, C. and Jaki, T. (2023). Updating the probability of study success for combination therapies using related combination study data. Statistical Methods in Medical Research 32(4):712-731. doi:10.1177/09622802231151218
Grainger, J.P., Sykulski, A.M., Ewans, K., Hansen, H.F., and Jonathan, P. (2023). A multivariate pseudo-likelihood approach to estimating directional ocean wave models. Journal of the Royal Statistical Society Series C: Applied Statistics. https://doi.org/10.1093/jrsssc/qlad006
Grant, J.A. and Leslie, D.S. (2023). Learning to Rank under Multinomial Logit Choice. Journal of Machine Learning Research 24 (260):1-49.
Jackson, H., Anzures-Cabrera, J., Simuni, T., Postuma, R.B., Marek, K. and Pagano, G. (2023). Identifying prodromal symptoms at high specificity for Parkinson’s disease. Front. Aging Neurosci. 15:1232387. https://doi.org/10.3389/fnagi.2023.1232387
Morgan, L.E., Nelson, B.L., Titman, A.C., and Worthington, D.J. (2023). A spline function method for modelling and generating a nonhomogeneous Poisson process. Journal of Simulation, 1-12
Murphy-Barltrop, C., Wadsworth, J. & Eastoe, E. (2023). New estimation methods for extremal bivariate return curves. Environmetrics.
Parmar, D., Morgan, L.E., Titman, A.C., Williams, R.A. and Sanchez, S.M. (2023). Input Uncertainty Quantification for Quantiles. 2022 Winter Simulation Conference (WSC). IEEE, pp. 97-108. ISBN 9781665476621
Putcha, S., Nemeth, C. & Fearnhead, P. (2023). Preferential Subsampling for Stochastic Gradient Langevin Dynamics. Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 206:8837-8856. Available from https://proceedings.mlr.press/v206/putcha23a.html
Rennie, N., Cleophas, C., Sykulski, A.M. and Dost, F. (2023). Outlier detection in network revenue management. OR Spectrum. Available at: https://doi.org/10.1007/s00291-023-00714-2
Rennie, N., Cleophas, C., Sykulski, A.M. and Dost, F. (2023). Analysing and visualising bike sharing demand with outliers. Discover Data. Volume 1, Issue 1. March 2023. Available at: https://doi.org/10.1007/s44248-023-00001-z
Rhodes-Leader, L., Worthington, D., Griffiths, I., and Samartzis, P. (2023). Simulation For Evaluating Long-term Maintenance Plans For Complex Systems. In C. Currie, & L. Rhodes-Leader (Eds.), Proceedings of the 11th Operational Research Society Simulation Workshop (SW23) (pp. 272-282). Operational Research Society. https://doi.org/10.36819/SW23.032
Richards, J., Tawn, J. A., Brown, S. (2023). Joint estimation of extreme spatially aggregated precipitation at different scales through mixture modelling. Spatial Statistics, 53:100725. https://doi.org/10.1016/j.spasta.2022.100725.
Ryan, S. and Killick, R. (2023). Detecting changes in the covariance structure of high dimensional data using random matrix theory. Technometrics.
Sherlock, C., Urbas, S. & Ludkin, M. (2023). The Apogee to Apogee Path Sampler. Journal of Computational and Graphical Statistics. DOI: 10.1080/10618600.2023.2190784
Spearing, J.H.M., Tawn, J, Irons, D. & Paulden, T. (2023). Modelling intransitivity in pairwise comparisons with application to baseball data. Journal of Computational and Graphical Statistics. https://www.tandfonline.com/doi/full/10.1080/10618600.2023.2177299
Thorburn, H., Sachs, A., Fairbrother, J., and Boylan, J.E. (2023). A time-expanded network design model for staff allocation in mail centres. Journal of the Operational Research Society. DOI: 10.1080/01605682.2023.2287613
Turnbull, K., Nemeth, C., Nunes, M., & Mccormick, T. (2023). Sequential estimation of temporally evolving latent space network models. Computational Statistics & Data Analysis, 179, [107627]. https://doi.org/10.1016/j.csda.2022.107627
Tendijck, S., Tawn, J., & Jonathan, P. (2023). Extremal characteristics of conditional models. Extremes 26:139-156. https://doi.org/10.1007/s10687-022-00453-7
Tendijck, S., Jonathan, P., Randell, D., & Tawn, J. (2023). Temporal evolution of the extreme excursions of multivariate kth order Markov processes with application to oceanographic data. Environmetrics 35(3). https://doi.org/10.1002/env.2834
Bold, M., Goerigk, M. (2022). Investigating the recoverable robust single machine scheduling problem under interval uncertainty, in Discrete Applied Mathematics. https://www.sciencedirect.com/science/article/pii/S0166218X22000427
Bold, M., Goerigk, M. (2022). A faster exact method for solving the robust multi-mode resource-constrained project scheduling problem, in Operations Research Letters. https://www.sciencedirect.com/science/article/pii/S0167637722001006
Boyacı, B., Dang, T. H., Letchford, A. N., Improving a constructive heuristic for the general routing problem, Networks. (2022), 1– 14. Available online at: https://doi.org/10.1002/net.22119
Boyacı, B., Dang, T. H., Letchford, A. N., Fast upper and lower bounds for a large-scale real-world arc routing problem, Networks. (2022), 1– 18. Available online at: https://doi.org/10.1002/net.22120
D’Arcy, E., Tawn, J.A., Sifnioti, D.E. (2022). Accounting for Climate Change in Extreme Sea Level Estimation. Water, 14(19): 2956. https://doi.org/10.3390/w14192956
Davison, M., Kheiri, A. & Zografos, K.G. (2022). Optimising Scheduling of Hybrid Learning using Mixed Integer Programming. In De Causmaecker, P., Özcan, E. & Vanden Berghe, G. (eds), Proceedings of the 13th International Conference on the Practice and Theory of Automated Timetabling - PATAT 2022 Volume III (2022), pp279-286. PATAT 2022, Leuven, Belgium, 30/08/22. http://www.patatconference.org/patat2022/proceedings/PATAT_2022_paper_33.pdf
Fisch, A., Bardwell, L. and Eckley, I.A. (2022). Real time anomaly detection and categorisation. Statistics and Computing, 32(4), pp.1-15.
Fisch, A.T., Eckley, I.A. and Fearnhead, P. (2022). A linear time method for the detection of collective and point anomalies. Statistical Analysis and Data Mining: The ASA Data Science Journal.
Fisch, A.T., Eckley, I.A. and Fearnhead, P. (2022). Subset multivariate collective and point anomaly detection. Journal of Computational and Graphical Statistics, 31(2), pp.574-585.
Gillam, J., Killick, R., Heal, J., Norwood, B. (2022). Modelling and Forecasting of at Home Activity in Older Adults using Passive Sensor Technology Statistics in Medicine. 41(23)4629-4646
Jackson, H., & Jaki, T. (2022). An alternative to traditional sample size determination for small patient populations. Statistics in Biopharmaceutical Research, 1-27. https://www.tandfonline.com/doi/full/10.1080/19466315.2022.2107565
McGonigle, E., Killick, R., Nunes, M.A. (2022). Modelling Time-Varying First and Second-Order Structure of Time Series via Wavelets and Differencing. Electronic Journal of Statistics 16(2):4398-4448
McGonigle, E., Killick, R., Nunes, M.A. (2022). Trend locally stationary wavelet processes. Journal of Time Series Analysis 43(6):895-917
Morgan, L. E., Rhodes-Leader, L.A., Barton, R.R. (2022). Reducing and Calibrating for Input Model Bias in Computer Simulation. INFORMS Journal on Computing 34(4):2368-2382.
https://doi.org/10.1287/ijoc.2022.1183
Mosley, L., Eckley, I.A. and Gibberd, A. (2022). Sparse temporal disaggregation. Journal of the Royal Statistical Society: Series A (Statistics in Society). http://doi.org/10.1111/rssa.12952
Reynolds, E., Maher, S.J. (2022). A data-driven, variable-speed model for the train timetable rescheduling problem. Computers & Operations Research, 142. https://doi.org/10.1016/j.cor.2022.105719
Rhodes-Leader, L. A., Nelson, B. L., Onggo, B. S. , & Worthington, D. J. (2022). A multi-fidelity modelling approach for airline disruption management using simulation. Journal of the Operational Research Society, 73:10, 2228-2241, DOI: 10.1080/01605682.2021.1971574
Richards, J., Tawn, J. A. (2022). On the tail behaviour of aggregated random variables. Journal of Multivariate Analysis, 192, 105065. doi.org/10.1016/j.jmva.2022.105065
Richards, J., Tawn, J. A., Brown, S. (2022). Modelling extremes of spatial aggregates using conditional methods. Ann. Appl. Stat. 16 (4) 2693 - 2713. doi.org/10.1214/22-AOAS1609
Bold, M., Goerigk, M. (2021). A compact reformulation of the two-stage robust resource-constrained project scheduling problem, in Computer and Operations Research. https://www.sciencedirect.com/science/article/pii/S0305054821000241
Boyaci, B., Dang, T., Letchford, A. (2021). Vehicle routing on road networks: how good is Euclidean approximation? In: Computers and Operations Research, 129. Available online at: https://www.sciencedirect.com/science/article/pii/S0305054820303142?via%3Dihub
Boyaci, B., Dang, T., Letchford, A. (2021). On matchings, T-joins and arc routing problems. In: Networks. Available online at: https://onlinelibrary.wiley.com/doi/10.1002/net.22033
Fisch, A.T., Eckley, I.A. and Fearnhead, P. (2021). Innovative and additive outlier robust Kalman filtering with a robust particle filter. IEEE Transactions on Signal Processing, 70, pp.47-56.
Grainger, J.P., Sykulski, A.M.; Jonathan, P. and Ewans, K. (2021). Estimating the parameters of ocean wave spectra. In: Ocean Engineering, 229. https://doi.org/10.1016/j.oceaneng.2021.108934
Grant, J.A. and Szechtman, R. (2021). Filtered Poisson Process Bandit on a Continuum. European Journal of Operational Research. 295(2):575-586. https://doi.org/10.1016/j.ejor.2021.03.033
Jackson, H., Anzures-Cabrera, J., Taylor, K.I., and Pagano, G. (2021). Hoehn and Yahr stage and striatal Dat-SPECT uptake are predictors of Parkinson's disease motor progression. In: Frontiers in Neuroscience. Available online: https://www.frontiersin.org/articles/10.3389/fnins.2021.765765/full
Jackson, H., Bowen, S. and Jaki, T. (2021). Using biomarkers to allocate patients in a response-adaptive clinical trial. In: Communications in Statistics - Simulation and Computation, DOI: 10.1080/03610918.2021.2004420
Kalyanam, K., Clarkson, J. (2021) Sequential Attack Salvo Size is Monotonic Non-decreasing in both Time and Inventory Level. Naval Research Logistics, 68(4): 485-495
McGonigle, E., Killick, R., Nunes, M.A. (2021). Detecting changes in mean in the presence of time-varying autocovariance. Stat 10:e351
O'Malley, M., Sykulski, A.M., Laso-Jadart, R., and Madoui, M.-A. (2021). Estimating the travel time and the most likely path from Lagrangian drifters. Journal of Ocenais and Atmospheric Technology. Available at: https://journals.ametsoc.org/view/journals/atot/aop/JTECH-D-20-0134.1/JTECH-D-20-0134.1.xml
Oscroft, S., Sykulski, A.M., and Early, J.J. (2021). Separating Mesoscal and Submesoscale Flows from Clustered Drifter Trajectories. Fluids, 6, 14. Available online at: https://doi.org/10.3390/fluids6010014
Parmar, D., Morgan, L.E., Titman, A.C., Sanchez, S.M. and Regnier, E.D. (2021). Comparing Data Collection Strategies via Input Uncertainty When Simulating Testing Policies Using Viral Load Profiles. 2021 Winter Simulation Conference (WSC).
Parmar, D., Morgan, L.E., Titman, A.C., Williams, R.A. and Sanchez, S.M. (2021). A Two Stage Algorithm for Guiding Data Collection Towards Minimising Input Uncertainty. 2021 Operational Research Society Simulation Workshop (SW21).
Rennie, N., Cleophas, C., Sykulski, A.M. and Dost, F. (2021). Identifying and responding to outlier demand in revenue management. European Journal of Operational Research. Volume 293, Issue 3, 16 September 2021, Pages 1015-1030. Available at: https://doi.org/10.1016/j.ejor.2021.01.002
Richards, J., & Wadsworth, J. L. (2021). Spatial deformation for nonstationary extremal dependence. Environmetrics, 32(5), e2671. Available online at: https://doi.org/10.1002/env.2671
Shooter, R., Tawn, J., Ross, E., and Jonathan, P. (2021). Basin-wide spatial conditional conditional extremes for severe ocean storms. Extremes, 24, 241-265. Available online: https://doi.org/10.1007/s10687-020-00389-w
Simpson, E. S., Wadsworth, J. L. and Tawn, J. A. (2021). A geometric investigation into the tail dependence of vine copulas. Journal of Multivariate Analysis, 184:104736. Available at: https://doi.org/10.1016/j.jmva.2021.104736
Spearing, J.H.M. (2021). Swimming meets statistics. Significance. 18(2), pp.8-9. Available online at: https://rss.onlinelibrary.wiley.com/doi/10.1111/1740-9713.01501
Tendijck, S., Eastoe, E., Tawn, J., Randell, D., & Jonathan, P. (2021). Modelling the extremes of bivariate mixture distributions with application to oceanographic data. Journal of the American statistical Association. DOI: 10.1080/01621459.2021.1996379
Tickle, S.O., Eckley, I.A. and Fearnhead, P. (2021), A computationally efficient, high-dimensional multiple changepoint procedure with application to global terrorism incidence. J R Stat Soc Series A, 184: 1303-1325. https://doi.org/10.1111/rssa.12695
Wilson, R.E., Eckley, I.A., Nunes, M.A. et al. A wavelet-based approach for imputation in nonstationary multivariate time series. Stat Comput 31, 18 (2021). https://doi.org/10.1007/s11222-021-09998-2
Barlow, A. Sherlock, C. and Tawn, J. A. (2020). Inference for extreme values under threshold-based stopping rules. In: Journal of the Royal Statistical Society: Series C (Applied Statistics), 68(4): 765-789.
Barnett, H.Y., Geys, H, Jacobs, T, Jaki, T. (2020) Methods for Non-Compartmental Pharmacokinetic Analysis With Observations Below the Limit of Quantification. Statistics in Biopharmaceutical Research. Available online pre-publication DOI: 10.1080/19466315.2019.1701546
Chapman, J. L., and Killick, R. (2020). An assessment of practitioners approaches to forecasting in the presence of changepoints. In: Quality and Reliability Engineering International, Vol 36, Issue 8:2676-2687. https://onlinelibrary.wiley.com/doi/10.1002/qre.2712
Chapman, J-L., Eckley, I.A., Killick, R. (2020). A nonparametric approach to detecting changes in variance in locally stationary time series. Environmetrics 31:e2576
Clarkson, J., Glazebrook, K. D., and Lin, K. Y. (2020) Fast or Slow: Search in Discrete Locations with Two Search Modes. Operations Research, 68(2):552-571
Ford, S., Atkinson, M.P., Glazebrook, K., and Jacko, P. (2020). On the dynamic allocation of assets subject to failure. European Journal of Operational Research 284 (1), https://doi.org/10.1016/j.ejor.2019.12.018
Graham, E., Jaki, T. & Harbron, C., 31/12/2019, A comparison of stochastic programming methods for portfolio level decision-making. In : Journal of Biopharmaceutical Statistics. 25 p.
Grant, J.A., and Leslie, D.S. (2020) “On Thompson Sampling for Smoother-than-Lipschitz Bandits”. In Proceedings of 23rd International Conference on Artificial Intelligence and Statistics. PMLR 108: pp2612-2622.
Grant, J.A., Leslie, D.S., Glazebrook, K., Szechtman, R., and Letchford, A.N. (2020) “Adaptive Policies for Perimeter Surveillance Problems”. European Journal of Operational Research. 283 (1) pp265-278.
Grundy, T., Killick, R. & Mihaylov, G. High-dimensional changepoint detection via a geometrically inspired mapping. Stat Comput 30, 1155–1166 (2020). https://doi.org/10.1007/s11222-020-09940-y
Laidler, G., Morgan, L., Nelson, B. and Pavlidis, N. (2020). Metric Learning for Simulation Analytics. In: Proceedings of the 2020 Winter Simulation Conference. IEEE.
Letchford, A.N. & Souli, G. (2020), Valid inequalities for mixed-integer programs with fixed charges on sets of variables. Operations Research Letters, vol. 48, issue 3, pp. 240-244.
Letchford, A.N. & Souli, G. (2020) Lifting the knapsack cover inequalities for the knapsack polytope. Oper. Res. Lett., 48(5), 607-611.
Lowther, A.P., Fearnhead, P., Nunes, M.A., Jensen, K. Semi-automated simultaneous predictor selection for regression-SARIMA models. Statistics and Computing (2020). https://doi.org/10.1007/s11222-020-09970-6
Ludkin, M. (2020). Inference for a generalised stochastic block model with unknown number of blocks and non-conjugate edge models, Computational Statistics & Data Analysis, Volume 152, 2020, 107051, ISSN 0167-9473, https://www.sciencedirect.com/science/article/pii/S0167947320301420
Montagné, R., Torres Sanchez, D., and Storbugt Olsen, H. (2020). “VRPy: A Python package for solving a range of vehicle routing problems with a column generation approach”. In: Journal of Open Source Software 5.55, p. 2408. doi: 10.21105/joss.02408
Moss, H. B., Leslie, D. S. & Rayson P. (2020). BOSH: Bayesian Optimisation by Sampling Hierarchically. Workshop on Real-World Experimental Design and Active Learning at the International Conference for Machine Learning (ICML: RealML).
Moss, H. B. &Griffiths R. R. (2020). Gaussian Process Molecule Property Prediction with FlowMO. Machine Learning for Molecules Workshop at Advances in Neural Information Processing Systems (NeurIPS: ML4Molecules).
Moss, H. B., Aggarwal, V., Prateek, N., Gonazlez, J. & Barra-Chicote, R. (2020). BOFFIN TTS: Few-shot Speaker Adaptation by Bayesian Optimisation. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
Moss, H. B., Leslie, D. S. & Rayson, P. (2020). MUMBO: Multi-task Max-value Bayesian Optimisation. European Conference on Machine Learning (ECML).
Moss, H. B., Beck, D., Gonzalez, J., Leslie, D. S. & Rayson P. (2020). BOSS: Bayesian Optimisation over String Spaces. Advances in Neural Information Processing Systems (NeurIPS).
Simpson, E. S., Wadsworth, J. L. and Tawn, J. A. (2020). Determining the dependence structure of multivariate extremes. Biometrika, 107(3):513-532
Sharkey, P., Tawn, J. A. and Brown, S. J. (2020). Modelling the spatial extent and severity of extreme European windstorms. Journal of the Royal Statistical Society: Series C (Applied Statistics). 69(2):223-250
Spearing, J.H.M., Tawn, J., Irons, D., Paulden, T., and Bennett, G. (2020). Ranking, and other properties, of elite swimmers using extreme value theory. In: Journal of the Royal Statistical Society: Series A (Statistics in Society), https://doi.org/10.1111/rssa.12628
S.O. Tickle, I. A. Eckley, P. Fearnhead & K. Haynes(2020)Parallelization of a Common Changepoint Detection Method, Journal of Computational and Graphical Statistics, 29:1, 149-161, DOI: 10.1080/10618600.2019.1647216
Torres Sanchez, D. (2020). “cspy: A Python package with a collection of algorithms for the (Resource) Constrained Shortest Path problem”. In: Journal of Open Source Software 5.49, p. 1655. doi: 10.21105/joss.01655
Torres Sanchez, D., Boyacı, B., and Zografos, K.G. (2020). “An Optimisation Framework for Airline Fleet Maintenance Scheduling with Tail Assignment Considerations”. In: Transportation Research Part B: Methodological 133, pp. 142–164. doi: 10.1016/j.trb.2019.12.008.
Urbas, S., Sherlock, C., & Metcalfe, P. (2020). Interim recruitment prediction for multi-centre clinical trials. Biostatistics, kxaa036, https://doi.org/10.1093/biostatistics/kxaa036
Bardwell, L., Eckley, I, Fearnhead, P., Smith, S. and Spott, M. (2019). Most recent changepoint detection in panel data. M. Technometrics. 1 (61). 88-98.
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