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Postdoctoral Researcher
Postdoctoral Researcher
Emily is a postdoctoral researcher at Bodeker Scientific, based in Wellington. She has recently completed her PhD at Cardiff University in the UK, studying the mathematics of data science methods, so has a background working with neural networks and their applications across many fields.
Emily is working on the DeepWeather project here at Bodeker Scientific, which aims to train a neural network to generate weather forecasts at an extremely small scale, a task that is currently prohibitively expensive to perform. If successful, this project will be implemented into MetService’s weather prediction.
Academic qualifications
Academic qualifications
2022 PhD, Mathematics, Cardiff University
(Thesis: Distance measures and whitening procedures for high dimensional data)2018 BSc, Mathematics (Operational Research and Statistics), 1:1 Hons, Cardiff University
Positions held
Positions held
March 2023 – present Postdoctoral Researcher, Bodeker Scientific
2018 - 2022 Lead Statistics Tutor & Tutorial Demonstrator, Cardiff University
2018 - 2021 PhD Researcher, Office for National Statistics, UK Government, United Kingdom
2016 - 2017 Statistician, Department for Business, Energy and Industrial Strategy, UK Government, United Kingdom
Publications
Publications
Jonathan Gillard, Emily O’Riordan, and Anatoly Zhigljavsky. Simplicial and minimal-variance distances in multivariate data analysis. J. Stat. Theory Pract., 16(1):1–30, 2022.
Jonathan Gillard, Emily O’Riordan, and Anatoly Zhigljavsky. Polynomial whitening for high-dimensional data. Computational Statistics, 2022.
Presentations
Presentations
‘Polynomial whitening for high-dimensional data’
Cardiff University SIAM Three Minute Thesis competition 2021‘Simplicial distances in high-dimensional spaces’
SIAM UKIE Annual Meeting 2020 and the Smith Institute’s TakeAIM awards 2020‘On simplicial distances with a view to applications in statistics’
NUMTA 2019, Welsh Mathematics Colloquium 2019 and Cardiff University’s statistics seminar, 2019
Research Skills & Training
Research Skills & Training
Programming in Python: Proficient at programming in Python, having produced my own packages and taught the language to undergraduate students. Attended a research software development course to learn best practices.
Computational Statistics: Attended courses on ‘Computer-Intensive Statistics’ and ‘High-Dimensional Statistics’, run by the Academy for PhD Training in Statistics (2019).
Machine Learning Methods: Implemented several machine learning methods from scratch, and attending courses such as the ISI’s ‘Statistics of Deep Learning’
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