This section captures how I think about problems — and what I think about.
🔬 Research Interests →
The technical areas I find genuinely interesting — not just what I’ve worked on, but what I keep thinking about.
Covers: dynamics and instability, contact mechanics, Hamiltonian formulations, evolutionary algorithms, physics-informed ML, and the patterns that keep appearing across domains that look unrelated on the surface.
🧠 How I Approach a Problem →
The mental models and instincts I keep returning to — physics-first decomposition, failure-mode analysis, timescale separation, and why AI is most useful when grounded in structure. Includes the seven engineering principles that have shaped real decisions across 15 years.
💡 Ideas & Open Questions →
Ongoing directions that haven’t resolved into finished projects yet — half-formed hypotheses, active explorations, and the questions that keep coming back.
📓 Lessons Learnt →
Essays and field notes from 15 years of simulation, battery modelling, and AI tooling — what broke, what held, and why.
Six pieces covering: the evolution of battery modelling, why physics-based models are debuggable in ways black-box models aren’t, the difference between a tool and a framework, what AI can and can’t inherit from an experienced engineer, and what running local LLMs on constrained hardware actually teaches you.