Energy systems researcher developing computational methods for planning, power systems and electricity markets.
I develop computational methods for spatial energy planning and power-system analysis, combining energy-system modelling with graph learning, geospatial methods and interpretable machine learning.
A graph-learning framework for turning settlement-scale electrification outputs into spatially connected planning units and interpretable investment tranches.
Code & reproducibility →
Ongoing research on how network constraints, operating conditions and spatial demand changes shape marginal emissions signals in transmission-constrained, decarbonising electricity systems.
Spatial electrification · optimisation · infrastructure
Graph learning · geospatial ML · interpretable learning
Marginal emissions · transmission constraints · market and system signals
I am a PhD researcher at Imperial College London working at the intersection of energy-system modelling, computational methods and electricity markets.
Alongside my doctoral research, I work with Climate Compatible Growth on energy-system research supporting country work in Zambia and Malawi.
Climate Compatible Growth →Python · PyTorch · PyTorch Geometric · scikit-learn · optimisation · graph neural networks · geospatial analytics · PyPSA · OSeMOSYS · OnSSET · GeoPandas · NetworkX