![Owen Jones](https://profiles-cardiff.imgix.net/attachments/4037?w=200&h=200&auto=format&crop=faces&fit=crop&q=90")
Yr Athro Owen Jones
Cadeirydd mewn Ymchwil Gweithredol
- JonesO18@caerdydd.ac.uk
- +44 29225 10253
- Abacws, Ystafell 3.56, Ffordd Senghennydd, Cathays, Caerdydd, CF24 4AG
- Ar gael fel goruchwyliwr ôl-raddedig
Trosolwyg
Research Group
Operational Research
Research Interests:
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:
- How to balance economic, environmental and social objectives
- Quantifying the impacts of climate change
- Managing renewable energy
- Management of renewable and non-renewable resources
- Sustainable water usage
- Enabling sustainable cities and sustainable development
- Reducing carbon emissions
- Recycling, remanufacturing and waste management
- Disaster management
Operational research draws on a wide range of mathematical modelling and optimisation techniques. 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.
Cyhoeddiad
2024
- 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)
- Henley, L., Jones, O., Woolley, T., Finch, D. and Mathews, F. 2024. A simple and fast method for estimating bat roost locations. Royal Society Open Science
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)
- 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)
- 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)
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)
Articles
- 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)
- Henley, L., Jones, O., Woolley, T., Finch, D. and Mathews, F. 2024. A simple and fast method for estimating bat roost locations. Royal Society Open Science
- 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)
- 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)
- 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)
- Jones, O. D. 2019. Runoff on rooted trees. Journal of Applied Probability 56(4), pp. 1065-1085. (10.1017/jpr.2019.61)
- 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)
- 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)
- 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)
Book sections
- 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.
Books
- 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.
Conferences
- 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.
- 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.
- 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.
- 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)
- 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)
- 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.
- 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)
Ymchwil
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.
Addysgu
Current and Past Research Students
Nanda Aryal PhD: Spatio-Temporal Rainfall Models.
Sashireka Appalasamy PhD: Parametric and nonparametric regression for optimal power flow models in volatile scenarios: precision, robustness and reliability.
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
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.”
Bywgraffiad
Grants and consulting
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
