Researcher · Data Scientist · Machine-Learning Engineer · Quietly curious about the universe

_{"The book of nature is written in the language of mathematics." — Galileo}

I work where mathematics, computation, and applied science meet. My day-to-day is split between machine-learning systems that have to behave in the real world — genomic diagnostics, energy-grid siting tools — and a slower, deeper reading life in pure and applied mathematics: the parts of the subject that feel less like tools and more like terrain.

Underneath all of it is a deep love of data science — the craft of turning messy, real-world data into calibrated insight. I care about the whole arc: exploratory analysis, principled feature engineering, statistical inference, honest experiment design, and visualisations that tell the truth rather than flatter it.

I care about systems that are rigorous on the inside and useful on the outside: calibrated, observable, honest about their uncertainty, and patient enough to be re-derived from first principles if anyone asks.

data science                       ── exploratory analysis, feature engineering,
                                       statistical inference, experiment design,
                                       and visualisation that tells the truth

machine learning                   ── calibrated probabilistic models, drift-aware
                                       systems, ensembling, graph and sequence models,
                                       foundation-model embeddings as features

quantum computing                  ── circuit synthesis, variational algorithms,
                                       error correction, the quietly stubborn
                                       puzzle of measurement

mathematical physics               ── gauge theory, the geometry of phase space,
                                       why Lagrangians know things Newton didn't

computational mathematics          ── numerical PDEs, randomised linear algebra,
                                       making infinite-dimensional problems sit
                                       still long enough to be solved

…sitting on a foundation of advanced mathematics I keep returning to:

"Somewhere, something incredible is waiting to be known." — Carl Sagan

Threads I'm actively working through — a snapshot, not a static reading list:

• Topology — Hatcher's Algebraic Topology, with side trips into persistent homology for biological data
• Differential geometry — Lee's Smooth Manifolds; revisiting information geometry as a lens on ML calibration
• Category theory — Riehl's Category Theory in Context; Spivak / Fong's Seven Sketches for the applied flavour
• SPDEs — Hairer's lecture notes on regularity structures; numerical schemes for the stochastic heat equation
• Quantum — Nielsen & Chuang for the canon; Qiskit / PennyLane tutorials for variational circuits
• Statistics — van der Vaart on empirical processes; conformal-prediction literature for production calibration

_{"Imagination is more important than knowledge." — Albert Einstein}

I'm happy to hear from collaborators working on data science and statistical modelling, calibrated ML in high-stakes settings, scientific computing, quantum-classical hybrid algorithms, or anything where a careful mathematical idea is about to meet a real-world deadline. Open an issue on any of my repositories or reach out through GitHub.

_{Profile last refreshed June 2026.}