Simon D.A. Thomas
Papers
https://orcid.org/0000-0001-7911-1659https://scholar.google.com/citations?user=kKonjBYAAAAJ
Web of Science ResearcherID: ABE-8823-2021
Letters collected so far
BA MSc MRes MA (Cantab)
Summary
I am a PhD candidate at the University of Cambridge at the Department of Applied Mathematics and Theoretical Physics. I am funded through @ai4er-cdt MRes+PhD program.
My thesis `Optimally Emulating a Hurricane Storm Surge Model for Risk Estimation and Sensitivity Analysis’ is based on emulating a realistic storm surge model (ADCIRC).
I studied Natural Sciences (Physics, BA+MSc) at the University of Cambridge.
Projects that I am currently involved in
“Finding the potential height of tropical cyclone storm surges in a changing climate using Bayesian optimization” Read our preprint https://doi.org/10.31223/X57T5R and code https://github.com/sdat2/worstsurge to hear about a method for calculating the worst possible storm surge and its benefits!
Feel free to get in touch. My email address is ${my github handle} at cam dot ac dot uk .
Funding
I am supported by studentship 2413578 from the UKRI Centre for Doctoral Training in Application of Artificial Intelligence to the study of Environmental Risks (grant no. EP/S022961/1). I have also received funding from the NERC ACSIS project (grant no. NE/N018028/1), and a Natural Environment Research Council (NERC) Research Experience Placement (REP) project funded by the SPITFIRE Doctoral Training Partnership (grant no. NE/S007210/1).
Reviews
I have reviewed articles for:
- Journal of Physical Oceanography (JPO) (x1)
- Journal of Advances in Modeling Earth Systems (JAMES) (x1)
I have reviewed abstracts for:
- Climate Informatics 2024 (x2)
Past projects
- Risk Management Solutions (UK) Ltd.: Deep Learning for extreme wind superresolution/downscaling
- Detecting fronts in the Southern Ocean using different algorithms.
- Detecting habitat fragmentation using ML algorithms on earth observation products.
- Using sensitivity analysis on simplified global physical models to understand CMIP biases in the tropics.
- Looking at the return periods of storm surges produced by tropical cyclones in climate models.