Introduction to PyTorch
Tutorial at StatML CDT, Oxford, UK
Education
- Ph.D in Statistics, University of Oxford, 2024 (expected)
- M.S. in Vision and Learning (MVA), ENS Paris-Saclay, 2019
- M.S. in Applied Mathematics, CentraleSupélec, 2019
Research experience
- Visiting Research Student — CISPA, Saarbrücken, 2023
- Subject: Generalisation guarantees from encoding linear domain knowledge in regression
- Supervisor: Krikamol Muandet
- Visiting Research Student — University of Valencia, Valencia, 2023
- Subject: Koopman mode decomposition with control for climate impact emulation
- Supervisor: Gustau Camps-Valls
- Part-time Research Intern — Met Office, Exeter, 2023
- Subject: Bayesian quality control methods for citizen weather station data
- Supervisor: Dawn Harrison, Ed Pavelin
- Research Intern — Cervest, London, 2020
- Subject: Deep generative modelling for multiresolution remote sensing fusion
- Supervisor: Jevgenij Gamper, Maxime Rischard
- Research Intern — Deepomatic, Paris, 2019
- Subject: Mixup regularisation of object detection deep learning architectures
- Supervisor: Vincent Delaître
Talks
Deconditional Downscaling with Gaussian processes
Talk at UCL Statistical Machine Learning Group, London, UK
A simple Bayesian model to reconstruct aerosols vertical profiles
Talk at spring School iMiracli 2022, Vienna, Austria
Probabilistic climate emulation with physics-constrained Gaussian processes
Talk at EGU23, Vienna, Austria
Teaching
- Part A Statistics, Tutor — Mansfield College, University of Oxford, 2021
- Part A Statistics, Tutor — Lady Margaret Hall, University of Oxford, 2022
- Applied Statistics, Lab Assistant — Department of Statistics, University of Oxford, 2022
- Computational Statistics, Lab Assistant — Department of Statistics, University of Oxford, 2022
- Applied Probability, Teaching Assistant — Department of Statistics, University of Oxford, 2022
Peer Review
- 2023: ICLR workshop on “Tackling Climate Change with Machine Learning”
- 2023: NeurIPS workshop on “Tackling Climate Change with Machine Learning”
- Earth System Dynamics journal