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I’m an experienced Research/HPC Engineer with a background in mathematical modelling and applied statistics, and a bit of scientific ML.

I work at the Natural History Museum as a research engineer in the Biodiversity Futures Lab on the Biodiversity Intactness Index. I lead the software engineering behind the BII, which involves developing and publishing R packages, lots of refactoring and optimisation (!), and a lot of HPC work to build a reproducible data pipeline to generate global biodiversity data.

I completed a PhD in applied maths at the University of Western Australia in 2022, and was a postdoc at the University of Cambridge from 2021-2024. I worked in-between scientific machine learning and applied mathematics, developing numerical methods to assimilate observations into physical models.

My undergrad degree was in Mathematics and Statistics, which I also completed at the University of Western Australia in 2018. In my honours thesis I worked on modelling daily rainfall across Australia using mixture models.

Experience

  • (2024-present) Research Software Engineer Natural History Museum, London.
  • (2021-2024) Postdoctoral Research Associate, University of Cambridge.
  • (2018-2020) Teaching Assistant, University of Western Australia.

Education

  • (2019-2022) PhD, Department of Mathematics and Statistics, University of Western Australia.
  • (2015-2018) BSc (Honours), Mathematics and Statistics, University of Western Australia.

Skills

Languages: R, Python, C++, CMake.

Frameworks/libraries:

  • (R): lme4, sf, terra, ggplot2, targets, Stan/brms, testthat.
  • (Python): numpy, Scipy, JAX, matplotlib, mpi4py, pytest.
  • (C++): FEniCS, FFTW, Eigen, catch2.

Tools: Git, Docker, AWS (EC2/S3), HPC (slurm, MPI).

Selected Software

  • predictsr: an R package to load the PREDICTS database into R dataframes and cache them to disk. Published on CRAN.
  • ssmooth: an R package for smoothing spatial data using statistical methods. Published on CRAN.

Publications

  • The statistical finite element method: A theoretical foundation for digital twins
    Duffin, Connor; Glyn-Davies, Alex; Vadeboncoeur, Arnaud; Girolami, Mark.
    Quality Engineering 38(1), 112–121 (2026).
  • Statistical finite elements via interacting particle Langevin dynamics
    Glyn-Davies, Alex; Duffin, Connor; Kazlauskaite, Ieva; Girolami, Mark; Akyildiz, Ö Deniz.
    SIAM/ASA Journal on Uncertainty Quantification 13(3), 1200–1227 (2025).
  • Probabilistic Super-Resolution for High-Fidelity Physical System Simulations with Uncertainty Quantification
    Zhang, Pengyu; Duffin, Connor; Glyn-Davies, Alex; Vadeboncoeur, Arnaud; Girolami, Mark.
    arXiv preprint arXiv:2502.10280 (2025).
  • Φ-DVAE: Physics-informed dynamical variational autoencoders for unstructured data assimilation
    Glyn-Davies, Alex; Duffin, Connor; Akyildiz, O Deniz; Girolami, Mark.
    Journal of Computational Physics 515, 113293 (2024).
  • Low-rank statistical finite elements for scalable model-data synthesis
    Duffin, Connor; Cripps, Edward; Stemler, Thomas; Girolami, Mark.
    Journal of Computational Physics 463, 111261 (2022).
  • Statistical finite elements via Langevin dynamics
    Akyildiz, Ömer Deniz; Duffin, Connor; Sabanis, Sotirios; Girolami, Mark.
    SIAM/ASA Journal on Uncertainty Quantification 10(4), 1560–1585 (2022).
  • Statistical finite elements for misspecified models
    Duffin, Connor; Cripps, Edward; Stemler, Thomas; Girolami, Mark.
    Proceedings of the National Academy of Sciences 118(2), e2015006118 (2021).

Preprints

  • Global indicator data tracking changes in species-level biodiversity persistence, ecosystem resilience under climate change, and protected-area representativeness and connectedness
    Ware, Chris; Valavi, Roozbeh; Vickers, Mat; Giljohann, Kathryn M; Mokany, Karel; Purvis, Andy; Walkden, Patrick A; De Palma, Adriana; Duffin, Connor; Contu, Sara; et al.
    EcoEvoRxiv (2026).
  • Exploring Model Misspecification in Statistical Finite Elements via Shallow Water Equations
    Duffin, Connor; Branson, Paul; Rayson, Matt; Girolami, Mark; Cripps, Edward; Stemler, Thomas.
    arXiv preprint arXiv:2307.05334 (2023).

PhD Thesis

  • Statistical finite element methods for nonlinear PDEs
    Duffin, Connor.
    Manuscript (2022).