Professor Owen Jones

Chair in Operational Research

: Available for postgraduate supervision

I am part of the Operational Research group in the School of Mathematics at Cardiff University. Operational research (OR) is a way of using analytical methods to help make better decisions. I am committed to developing the role of OR in the Environment, Energy & Sustainability. For example:

Sustainable water management
Optimal production and distribution of renewable energy (tidal, hydo, wind, etc.)
Risk quantification and disaster management (such as flood, drought and fire)
Biological modelling for population management and disease control

Operational research draws on a wide range of mathematical modelling and optimisation techniques, and seeks to balance economic, environmental and social objectives. My background is mostly in stochastic modelling and simulation, and I am particularly interested in likelihood free methods for fitting complex stochastic models to data.

Date
Type

2024

Henley, L., Finch, D., Mathews, F., Jones, O. and Woolley, T. E. 2024. A simple and fast method for estimating bat roost locations. Royal Society Open Science 11(4), article number: 231999. (10.1098/rsos.231999)
Perry, W. B., Ahmadian, R., Munday, M., Jones, O., Ormerod, S. J. and Durance, I. 2024. Addressing the challenges of combined sewer overflows. Environmental Pollution 343, article number: 123225. (10.1016/j.envpol.2023.123225)
Henley, L., Jones, O., Mathews, F. and Woolley, T. 2024. Bat motion can be described by leap frogging. Bulletin of Mathematical Biology 86, article number: 16. (10.1007/s11538-023-01233-5)

2023

Jones, O., Poudevigne-Durance, T. and Qin, Y. 2023. Synthesis of time-series with missing observations using generative adversarial networks. Presented at: 34th Panhellenic Statistics Conference, 19-22 May 2022. Greek Statistical Institute pp. 154-166.

2022

Twumasi, C., Jones, O. and Cable, J. 2022. Spatial and temporal parasite dynamics: microhabitat preferences and infection progression of two co-infecting gyrodactylids. Parasites and Vectors 15, article number: 336. (10.1186/s13071-022-05471-9)
Wilde, H. et al. 2022. Accounting for dilution of SARS-CoV-2 in wastewater samples using physico-chemical markers. Water 14(18), article number: 2885. (10.3390/w14182885)
Poudevigne-Durance, T., Jones, O. D. and Qin, Y. 2022. MaWGAN: a generative adversarial network to create synthetic data from datasets with missing data. Electronics 11(6), article number: 837. (10.3390/electronics11060837)

2021

Aryal, N. and Jones, O. 2021. Spatial-temporal rainfall models based on Poisson cluster processes. Stochastic Environmental Research and Risk Assessment 35, pp. 2629-2643. (10.1007/s00477-021-02046-5)
Xue, J., Ahmadian, R., Jones, O. and Falconer, R. A. 2021. Design of tidal range energy generation schemes using a genetic algorithm model. Applied Energy 286, article number: 116506. (10.1016/j.apenergy.2021.116506)
Mehrnegar, N. et al. 2021. Exploring groundwater and soil water storage changes across the CONUS at 12.5 km resolution by a Bayesian integration of GRACE data into W3RA. Science of the Total Environment 758, article number: 143579. (10.1016/j.scitotenv.2020.143579)

2020

Aryal, N. and Jones, O. 2020. Fitting the Bartlett-Lewis rainfall model using approximate Bayesian computation. Mathematics and Computers in Simulation 175, pp. 153-163. (10.1016/j.matcom.2019.10.018)
Xue, J., Ahmadian, R. and Jones, O. 2020. Genetic algorithm in tidal range schemes’ optimisation. Energy 200, article number: 117496. (10.1016/j.energy.2020.117496)
Mehrnegar, N., Jones, O., Singer, M. B., Schumacher, M., Bates, P. and Forootan, E. 2020. Comparing global hydrological models and combining them with GRACE by dynamic model data averaging (DMDA). Advances in Water Resources 138, article number: 103528. (10.1016/j.advwatres.2020.103528)

2019

Jones, O. D. 2019. Runoff on rooted trees. Journal of Applied Probability 56(4), pp. 1065-1085. (10.1017/jpr.2019.61)

2018

Aryal, N. and Jones, O. D. 2018. Fitting a spatial-temporal rainfall model using Approximate Bayesian Computation. Presented at: IWSM2018: 33rd International Workshop on Statistical Modelling, Bristol, UK, 16-20 July 201833rd International Workshop on Statistical Modelling, Vol. 2. University of Bristol pp. 7-12.

2017

Jones, O., Robinson, A., Shield, M. and Sibley, J. 2017. The allocation of inspection resources. In: Robinson, A. P. et al. eds. Invasive Species: Risk Assessment and Management. UK: Cambridge University Press, pp. 1-16.
Aryal, N. R. and Jones, O. D. 2017. Fitting the Bartlett-Lewis rainfall model using Approximate Bayesian Computation. Presented at: 22nd International Congress on Modelling and Simulation, Hobart, Tasmania, Australia, 3 - 8 December 2017.

2016

Jones, O. D., Lane, P. N. J. and Sheridan, G. J. 2016. The stochastic runoff-runon process: Extending its analysis to a finite hillslope. Journal of Hydrology 541(B), pp. 677-688. (10.1016/j.jhydrol.2016.06.056)

2015

Rolls, D. A. and Jones, O. D. 2015. An improved test for continuous local martingales. Communications in Statistics - Theory and Methods 44(13), pp. 2674-2688. (10.1080/03610926.2013.788709)
Decrouez, G., Hambly, B. and Jones, O. D. 2015. The Hausdorff spectrum of a class of multifractal processes. Stochastic Processes and their Applications 125(4), pp. 1541-1568. (10.1016/j.spa.2014.11.007)
Eng, F., Gan, H. and Jones, O. 2015. The role of communication in emergency evacuations: an analysis of a ring network with a static disruption. Presented at: 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), Qingdao, China, 8-11 October 20142014 IEEE 17th International Conference on Intelligent Transportation Systems (ITSC 2014), Vol. 2. Institute of Electrical and Electronics Engineers (IEEE) pp. 1186-1193., (10.1109/ITSC.2014.6957848)
Appalasamy, S., Jones, O., Moin, N. H. and Sin, T. C. 2015. New multivariate linear regression real and reactive branch flow models for volatile scenarios. Presented at: 2015 IEEE Power & Energy Society General Meeting, Denver, CO, USA, 26-30 July 20152015 IEEE Power & Energy Society General Meeting. Institute of Electrical and Electronics Engineers (IEEE) pp. 258-262., (10.1109/PESGM.2015.7285669)

2014

Jones, O., Nyman, P. and Sheridan, G. J. 2014. Modelling the effects of fire and rainfall regimes on extreme erosion events in forested landscapes. Stochastic Environmental Research and Risk Assessment 28(8), pp. 2015-2025. (10.1007/s00477-014-0891-6)
Sheridan, G., Noske, P., Lane, P., Jones, O. and Sherwin, C. 2014. A simple two parameter model for scaling hillslope surface runoff. Earth Surface Processes and Landforms 39(8), pp. 1049-1061. (10.1002/esp.3503)
Kowalewski, A., Jones, O. and Ramamohanarao, K. 2014. Volatility homogenisation kernel for forecasting. Presented at: International Work-Conference on Time Series (ITISE 2014), Granada, Spain, 25-27 June 2014.
Jones, O., Maillardet, R. and Robinson, A. 2014. Introduction to scientific programming and simulation using R. Chapman & Hall/CRC The R Series. Chapman & Hall/CRC.
Kowalewski, A. W., Jones, O. and Ramamohanarao, K. 2014. Volatility homogenisation decomposition for forecasting. Presented at: 2014 IEEE Computational Intelligence for Financial Engineering & Economics (CIFEr), London, UK, 27-28 March 20142014 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr). Institute of Electrical and Electronics Engineers (IEEE) pp. 182-189., (10.1109/CIFEr.2014.6924071)

I am an applied mathematician with a background in data analytics, optimisation and simulation. Many of my projects involve assembling data from divers sources, using it to build simulation models, then using those models to inform management decisions. Previous collaborators include the Australian Department of Agriculture, Fisheries and Forestry (planning for the Post Entry Quarantine facility, a national infrastructure project); the Australian Office of Transport Security (improving security procedures in Australian airports); Rate Valuation Services (a financial services company); McLaran International (the racing team); Merlin Power Systems (a feasibility study for an emergency response scheme for power generators in the UK); and National Air Traffic Systems (responsible for air traffic control in the UK).

My work makes use of a wide range of computational, analytical and mathematical techniques. I have taught graduate courses in machine learning and data mining, and I am the principal author of a best-selling text book on programming and simulation using the language R. Much of my current research concerns complex spatio-temporal environmental data, in particular problems of water runoff in catchment areas.

Current and Past Research Students

Thomas Poudevigne-Durance PhD: Generative Adversarial Networks for Rare Event Augmentation
Eferhonore Efe -Eyefa PhD: Simulation of Rainfall Events
Clement Twumasi PhD: In silico modelling of parasite dynamics
Lucy Henley PhD: Locating Bat Roosts using Diffusion Modelling
Nurudeen Oshinlaja PhD: Improving global estimates of groundwater storage variability by integrating models, in-situ observations, and remotely sensed data
Nooshin Mehrnegar PhD 2021: Bayesian integration of satellite geodetic data with models to separate land hydrology and surface deformation signals.
Nanda Aryal PhD 2018: Spatio-Temporal Rainfall Models.
Felicia Eng PhD 2016: Game-Theoretic Approaches to Natural Disaster Evacuation Modelling.
Adam Kowalewski PhD 2016: Volatility Homogenisation and Machine Learning for Time Series Forecasting.
Craig Mason MPhil 2016: Improved modelling of post fire hydro-geomorphic risks.
Geoffrey Decrouez PhD 2009: Generation of multifractal signals with underlying branching structure.
Wahib Arroum PhD 2008: Time-Changed Self-Similar processes: An Application To High Frequency Financial Data

Grants and consulting

2021 UKRO Interdisciplinary Funding Call: Discipline hopping for freshwater solutions
2021 CU Impact Fund: Digital solutions for bat protection during construction planning
2020 GCRF Catalyst project: Wastewater surveillance, from COVID19 to long term vision
2020 Ser Cymru: Wastewater Monitoring and Environmental Surveillance of SARS-Cov-2/COVID-19 Using Metaviromic Approaches
2020 Cardiff University, Bangor University, Public Health Wales and Dŵr Cymru Welsh Water. Wales Environmental Wastewater Analysis & Surveillance for Health
2020 EU Horizon2020 Grant: Translation of climate information into multilevel decision support for social adaptation, policy development and resilience to water scarcity in the Horn of Africa drylands
2019 KESS2: Generative Adversarial Networks for Rare Event Augmentation
2018 Royal Statistical Society Mardia Workshop Prize: funding for a sequence of workshops on extremal trends in weather (WET Weather)
2016 Australian Research Council Linkage Grant: Mitigating extreme water supply contamination in bushfire burned catchments
2015 Australian Federal Office of Transport Security: Report on Utility of Explosive Trace Detection Deployment Arrangements
2013 Australian Federal Department of Agriculture, Fisheries and Forestry: Risk Analysis for the new Post-Entry Quarantine Facility
2012 Rate Valuation Services: Multivariate Approaches to Breach Detection
2011 Bushfire Collaborative Research Centre Grant: Quantifying water quality risks following wildfire
2009 Norman Waterhouse Lawyers: Advice on a question of changing market share
2008 French-Australian Science and Technology Program International Science Linkage: Objective Risk Evaluation and Decision Making for Large Systems
2006 Melbourne University Grant: Brownian motion with a multifractal time-change
Australian Research Council Discovery Grant: Multifractal models in finance via the crossing tree
2005 Virgin Blue: Comments on Audit Sampling Dictionary
2004 Nuffield Foundation Grant: Modelling self-similar telecommunications data
2003 Merlin Power Systems: Modelling delivery of emergency generators
2002 McLaren International: Optimal settings for a race car
UK EPSRC Grant: Self-similar processes with embedded branching process
1999 UK National Air Traffic Systems: Analysis of Real-time Simulation Trials

Subjects Taught

Stochastic Operational Research Models

Stochastic Search and Optimisation

Risk, Hazard and Management

Textbook

Owen Jones, Robert Maillardet, and Andrew Robinson.Introduction to Scientific Programming and Simulation Using R, Second Edition. The R Series, Chapman & Hall/CRC. 2014.

From the preface: “This book has two principal aims: to teach scientific programming and to introduce stochastic modelling. Stochastic modelling in particular, and mathematical modelling in general, are intimately linked to scientific programming because the numerical techniques of scientific programming enable the practical application of mathematical models to real-world problems. In the context of stochastic modelling, simulation is the numerical technique that enables us to analyse otherwise intractable models.

“Simulation is also the best way we know of developing statistical intuition.”

Contact Details

JonesO18@cardiff.ac.uk
+44 29225 10253
Abacws, Room 3.56, Senghennydd Road, Cathays, Cardiff, CF24 4AG

Finding bat roosts no longer like searching for “a needle in a haystack”