CV

A short & condensed CV lies below. Please contact me for the full version.

Technical skills

Languages: Proficient with Python. Familiar with R, SQL (Postgres), Julia, Javascript and C++
SciPy : numpy, scipy, scikit-learn, numba, pandas, matplotlib, hvplot, seaborn
Geospatial : xarray, rasterio, cartopy, geopandas, shapely, gdal, nco, cdo, Google Earth Engine
Computing: dask, unix, scripting, job scheduling (SLURM), storage (netCDF, HDF5, Zarr)
Cloud : AWS (cli, S3, EC2, ECR, Sagemaker, Fargate), Azure (blob), Coiled, Microsoft Planetary Hub
Version control git/hub, containerisation with docker/hub, documentation with Sphinx and Readthedocs
Python Package management with pip, miniconda and micromamba
Deep Learning frameworks: Tensorflow (proficient with keras API and some tf2 components). Familiar with PyTorch
Front-end frameworks: building geospatial R Shiny apps (using leaflet, ggplot2) and dash

Geospatial Analyst : Trove Research, London, UK
- Main project: Modelling the global supply of carbon credits
- October 2021 - Present

Graduate researcher : University of Toronto, Toronto, Canada
- Project: Predicting summertime tropospheric ozone over US using a hybrid deep learning model
- September 2020 - September 2021 (1 year, remote)
- Research Group: Atmospheric Modelling and Composition
- Supervisor: Professor Dylan Jones
Research Intern : British Antarctic Survey (BAS), Cambridge, UK
- Project : Exploring the effects of energetic particle precipitation on middle atmospheric dynamics
- June 2018 - September 2018 (3 months)
- Research Group : Atmospheric Dynamics and Space Weather
- Supervisors: Tracy Moffat-Griffin and Andrew Kavanagh