🔬(he/him)
Senior Postdoctoral Research Fellow
My work focuses on tumour microenvironments through the lens of spatial transcriptomics and network biology. I develop computational methods that integrate multi-modal spatial data, graph theory, and machine learning to map how cancer cells interact with their immune neighbourhood.
Current projects include the METABRIC-MX platform for multimodal breast cancer spatial analysis, the MOSAIK toolkit for cross-platform spatial transcriptomics integration, and a Cell-cell communications project establishing a mathematical equivalence between PDE-based models and random walk with restart on spatial graphs (details currently under NDA).
I’m always open to collaboration — feel free to reach out.
A selection of recent work. See the full publications list for more.
📖 _Nature Communications_ · 2024 — Using Ricci flow to chart cellular differentiation trajectories and reveal the geometric structure of gene expression landscapes during …
📖 _Scientific Reports_ · 2024 — A formal connection between deep learning dynamics and Ricci flow on Riemannian manifolds, giving a geometric framework for neural network training.
📖 _Nature Cancer (under review)_ · 2025 — A spatio-molecular atlas characterising response trajectories in triple-negative breast cancer using spatial transcriptomics and …
📖 _Nature Communications_ · 2025 — A mathematical framework for mining higher-order triadic interactions in complex networks, showing how signed triadic relationships shape network …
📖 _Journal of Open Source Software_ · 2026 — Open-source toolkit for integrating spatial transcriptomics data from Xenium, CosMx and MERFISH into a standardised SpatialData …
A mathematical framework using walk with restart on spatial graphs.
A toolkit for integrating Xenium, CosMx, and MERFISH spatial transcriptomics data into standardised SpatialData objects.
Multimodal eXplorer for breast cancer spatial transcriptomics — integrating Xenium, metallomics, IF imaging, and H&E into unified SpatialData objects.