I'm a Materials Chemistry PhD applying deep scientific expertise to data science, with a focus on climate, energy, and environmental data.
My background is in solid-state chemistry and hydrogen storage materials: air-sensitive synthesis using Schlenk lines and gloveboxes, quantitative Rietveld refinements using GSAS, and neutron diffraction experiments at ISIS Rutherford Appleton Laboratory. During my PhD, I achieved a ~100 K reduction in hydrogen release onset temperature through nanostructuring of LiOH-MgH₂ systems, and proposed and validated dehydrogenation mechanisms for three hydride-hydroxide systems using combined in-situ neutron diffraction, ex-situ PXRD, and thermogravimetric analysis.
What has always driven me is the analytical side: designing experiments, finding patterns in complex data, and communicating results clearly. That's what I do now, with Python instead of lab glassware.
- PhD in Materials Chemistry, WestCHEM, University of Glasgow (2011-2015)
- Master's in Advanced Chemical Methodologies, University of Bologna (110/110 cum laude)
- Applied AI Programme, AllWomen (2026)
- 3 peer-reviewed publications in hydrogen storage and materials chemistry
- 3 patents in materials chemistry and hydroxyapatite electrodeposition
- Conference presentations at ACS (Dallas) and RSC (St. Andrews, Bath)
- Experienced science communicator: A-level Chemistry, AS-level Statistics, KS3 Science
| Project | Description | Tools |
|---|---|---|
| 🪸 Coral Bleaching Risk Prediction | End-to-end ML pipeline predicting Atlantic coral bleaching risk from 40 years of oceanographic data. Random Forest optimised for recall (0.82 on test set), deployed on AWS SageMaker with Streamlit dashboard | Python, scikit-learn, SHAP, AWS SageMaker, Streamlit |
| 🌊 Marine Heatwave Detection (Mediterranean Sea) | SQL/DuckDB pipeline detecting 253,145 marine heatwave events (2016-2026) in the NW Mediterranean using the Hobday et al. methodology, implemented from scratch with window functions and gaps-and-islands logic | SQL, DuckDB, Python, xarray |
| ⚗️ Hydride Property Prediction | Materials informatics applied to metal hydride systems: Random Forest (R²=0.765) predicts hydrogen storage capacity; honest failure documented for desorption temperature prediction (RMSE=87°C, insufficient data) | Python, scikit-learn, SHAP, chemparse |
| 🌿 Greenwashing Detection in Reef-Safe Sunscreen Claims | NLP project combining RoBERTa sentiment analysis and VADER on Reddit data, anchored by the ACCC vs. Edgewell regulatory case | Python, RoBERTa, VADER, spaCy |
| 🌡️ Land Temperature Trends (1850-2015) | Exploratory analysis and regression modelling of global land surface temperatures, asymmetric warming patterns and greenhouse effect signature | Python, pandas, seaborn, numpy |
libraries = ["pandas", "numpy", "matplotlib", "seaborn",
"scikit-learn", "SHAP", "RoBERTa", "VADER", "spaCy"]
ml = ["random forest", "logistic regression", "SVM",
"XGBoost", "SMOTE", "clustering"]
deployment = ["AWS SageMaker", "Streamlit"]
domain = ["hydrogen storage", "materials informatics",
"climate data", "oceanographic datasets",
"conservation data"]
tools = ["Jupyter", "VSCode", "GitHub", "Claude Code"]
When I'm not wrangling data, I'm probably underwater. I'm a PADI and SSI scuba instructor with 1,600+ dives and 200+ divers certified, around 1,200 of which were in the Coral Triangle. I've lived and worked in the UK, Cambodia, and Malaysia, which means I'm comfortable figuring things out from scratch in unfamiliar environments.