Physics First
Most problems I work on start with the governing equations, not the data. Before reaching for a model or a tool, I try to understand: what forces are acting, what constraints exist, where the system can fail, and how it behaves at its boundaries.
This isn’t dogma — it’s practical. A model built from physical understanding generalises better, degrades more predictably, and can tell you why it’s wrong when it fails. A model built from curve-fitting can’t.
Failure Reveals Structure
Stable, well-behaved systems are degenerate — they all look similar at equilibrium. It’s the instabilities, edge cases, and nonlinear transitions that reveal the actual structure underneath.
This shows up everywhere: a battery at the edge of lithium plating, a simulation that diverges at a specific timestep, a codebase that breaks when a specific dependency is modified. In each case, the failure tells you something about the system that nominal operation hides.
The instinct is to treat failures as the most informative data points, not as noise to be avoided.
Timescale Separation
Most complex systems operate across multiple timescales simultaneously. In battery modelling: charge transport (milliseconds), thermal response (seconds to minutes), ageing (hours to years). In software: function execution (microseconds), test cycles (minutes), architectural evolution (months).
Recognising which timescales are coupled and which can be treated independently is usually the key modelling decision. It’s what determines whether a lumped approximation is valid, or whether you need a full coupled simulation.
AI as Reasoning Infrastructure
Machine learning is useful — but the framing matters. The interesting direction isn’t AI replacing physical models; it’s AI helping reason about physical models.
A system that can understand a codebase’s dependency structure before modifying it. A surrogate model that respects physical constraints rather than just fitting training data. A debugging assistant that starts from failure modes, not token prediction.
Structure before generation. Understanding before output.
The Pattern
Physics → model → automate → scale.
This has been the repeating cycle across battery modelling, simulation toolchains, and AI systems. The domain changes; the cycle doesn’t.
→ Where this leads technically: Research Interests → What’s still unresolved: Ideas & Open Questions