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Senior Research Fellow in Machine Learning for Weather and Climate Science at University College London. Theme lead for UCL’s Met Office Academic Partnership. My research interests focus on the intersection of data-driven approaches with high performance computing techniques in the area of atmospheric and ocean modelling.

Education

• Ph.D in Applied and Computational Mathematics, University College Dublin, Oct. 2017 - Jan. 2021
• B.Sc. in Theoretical Physics, University College Dublin, Sep. 2013 - May. 2017

Work experience

• Senior Research Fellow in Machine Learning for Weather and Climate, AI Centre, Department of Computer Sciences, University College London, Oct. 2023 - present
  • Supervising undergraduate and master students
  • Tools Systems and Practices Theme lead for the Met Office Academic Partnership
  • Consultant on the Met Office Transatlantic Data Science Academy
• Senior Research Software Engineer/Data Scientist, Centre of Advanced Research Computing (ARC), University College London, Jan. 2023 - Oct. 2023
  • Developer for the UCL/AWS Centre of Digital innovation (CDI). Built open-source data processing pipelines on AWS resources for startups within the cohort. Worked with and supported the PhD funded researchers.
  • Project lead for the optimisation of a massively parallelised cosmology code (DiRAC SWIFT). Project involves mapping computing processes to physical cores to minimise latency.
  • Line manager for Junior Research Software Engineer
• Research Fellow, Department of Statistical Sciences, University College London, Feb. 2021 - Jan. 2023
  • Core developer of an open source data assimilation software package which implements particle filtering techniques and has applications in atmospheric and tsunami modelling
  • Initiated an ongoing collaboration with a UK Met Office partner to improve the representation of high resolution information within the Unified Model for climate predictions.
  • Partnered with AON (reinsurance) and the Met Office to develop real-time approaches for providing historical context to severe weather events.
  • Developed the numerical software on a joint project with AON titled: “Production-level Cascadia tsunami hazard model incorporating far-field sources”.
  • Academic mentoring of two PhD students. Support focused on high performance computing methods, the implementation of statistical learning techniques and the running of climate models.
  • Associate Member of the Centre of Advanced Research Computing (ARC)
• Postgraduate Research Scholar, School of Mathematics and Statistics, University College Dublin, Oct. 2017 - Jan. 2021
  • Lead developer of a tsunami modelling software package optimised to utilise the latest high performance computing architectures
  • Created novel automated techniques which predict local variability of tsunami impact using transfer functions and machine learning methods.
  • Coupled a fast tsunami solver with a statistical emulator to provide tsunami warnings with associated uncertainties.
  • Vice-President of the SIAM-IMA Dublin Area Student Chapter
• Research Scholar, Department of Numerical Modelling, Paul Scherrer Institute (ETH Zurich), May. 2017 - Aug. 2017
  • Modelling of fluid flows in nuclear reactors with an emphasis on implementing particle tracking techniques to track radionuclides
  • Supervisor: Professor Bojan Niceno
• Research Scholar, Mathematics Applications Consortium for Science and Technology, University of Limerick, May. 2016 - Aug. 2016
  • Developed a mathematical model for predicting influential spreaders in complex networks and tested its performance by developing Python code and using idealised Erdos-R'enyi graphs
  • Supervisor: Professor James Gleeson

Academic Reviewer

Reviewed numerous submissions to the following journals

• Journal of Applied Mathematical Modelling; the European Journal of Mechanics /B Fluids; Wave Modelling; Estuarine, Coastal and Shelf Science; Applied Mathematics and Computing; Hydroinformatics; Natural Hazards and Earth System Sciences; International Journal for Uncertainty Quantification

Academic Supervisor

Academic co-supervisor for a student’s MSc project (UCL Machine Learning) (present)

• “Embedding a machine learning model within a global atmospheric model”

Academic co-supervisor for a student’s MSc project (UCL Scientific and Data Intensive Computing Masters) (May. 2021 - Sep. 2021)

• The student’s project was a collaboration with the UK Met Office and it focused on developing machine and statistical learning approaches for predicting atmospheric indices

Academic co-supervisor for a student’s (ENS Paris-Saclay) undergraduate internship (Apr. 2019 - Aug. 2019)

• The student’s work focused on developing ML techniques for capturing localised tsunami responses

Workshops & Conferences

Co-organised a mini-symposium at SIAM GS, Bergen 2023

• Fusing of machine learning with weather/climate modelling

Delivered a workshop at IIT Delhi, 2023

• Computing methods for Validation, Verification and Uncertainty Quantification

Awards & Scholarships

• University College Dublin Entrance Scholar (UCD, 2013)
• 2nd Year Academic Scholar (UCD, 2014)
• 3rd Year Academic Scholar (UCD, 2015)
• Science and Engineering Summer Research Scholarship (UL40) (University of Limerick, 2016)
• Summer Research Scholarship at the Paul Scherrer Institute (PSI, 2017)
• Government of Ireland Postgraduate Research Scholarship (2017)
• Bertram Broberg Memorial Medal (2021) - Awarded to the best PhD thesis in Mechanics across the Schools of Engineering, Mathematics & Statistics and Physics.

Publications

Embedding machine-learnt sub-grid variability improves climate model biases

Giles , D., et al.: Embedding machine-learnt sub-grid variability improves climate model biases, 2024.

A collaborative hackathon to investigate climate change and extreme weather impacts in justice and insurance settings

Macholl, J.D., et al.: A collaborative hackathon to investigate climate change and extreme weather impacts in justice and insurance settings, 2024.

Transatlantic Data Science Academy Project. Phase 1: Scoping and Shaping

Dance , S., et al.: Transatlantic Data Science Academy Project. Phase 1: Scoping and Shaping, 2024.

ParticleDA.jl v.1.0: a distributed particle-filtering data assimilation package

Giles, D., Graham, M. M., Giordano, M., Koskela, T., Beskos, A., and Guillas, S.: ParticleDA.jl v.1.0: a distributed particle-filtering data assimilation package, Geosci. Model Dev., 17, 2427–2445, 2024.

Time-dependent influence metric for cascade dynamics on networks

Gleeson, J., Cassidy, A., Giles, D. and Faqeeh, A.: Time-dependent influence metric for cascade dynamics on networks, 2024.

Meteotsunamis and other anomalous “tidal surge” events in western Europe in summer 2022

E. Renzi, C. Bergin, T. Kokina, D. S. Pelaez-Zapata, D. Giles and F. Dias: Meteotsunamis and other anomalous “tidal surge” events in Western Europe in Summer 2022. Physics of Fluids, 35, 4, 2023

Multilevel Bayesian Quadrature

Kaiyu, L., Giles, D., Karvonen, T., Guillas, S. and Briol, F.X.: Multilevel Bayesian Quadrature, Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, 206, 1845--1868, 2023.

Automated approaches for capturing localized tsunami response—Application to the French coastlines.

Giles, D., Gailler, A., & Dias, F.: Automated approaches for capturing localized tsunami response—Application to the French coastlines. Journal of Geophysical Research: Oceans, 127, 2022

Faster Than Real Time Tsunami Warning with Associated Hazard Uncertainties

Giles, D., Gopinathan, D., Guillas, S., and Dias, F.: Faster Than Real Time Tsunami Warning with Associated Hazard Uncertainties, Front. Earth Sci., 8, 226, 2021.

Performance analysis of Volna-OP2 – massively parallel code for tsunami modelling

Giles, D., Kashdan, E., Salmanidou, M. D., Guillas, S., and Dias, F.: Performance analysis of Volna-OP2 – massively parallel code for tsunami modelling, Computers & Fluids, 209, 104649, 2020.

Modelling with Volna-OP2—Towards Tsunami Threat Reduction for the Irish Coastline

Giles, D., McConnell, B., and Dias, F.: Modelling with Volna-OP2—Towards Tsunami Threat Reduction for the Irish Coastline, Geosciences, 10(6), 226, 2020.

The Volna-OP2 tsunami Code (version 1.5)

Reguly, I. Z., Giles, D., Gopinathan, D., Quivy, L., Beck, J. H., Giles, M. B., Guillas, S., and Dias, F.: The VOLNA-OP2 tsunami code (version 1.5), Geosci. Model Dev., 11, 4621–4635, 2018.