I chose electrical engineering because power systems sit where physics, economics, and software meet — a grid is the largest machine most engineers will ever work on, and every decision inside it is a trade-off you can actually model.

My postgraduate research focused on peer-to-peer energy trading in blockchain-enabled microgrids — how distributed prosumers can transact energy directly while the network stays stable. That work pulled me into the two disciplines this practice is built on: rigorous simulation of power networks, and machine learning applied to real energy problems rather than toy datasets.

Alongside consulting, I have written and published five engineering books, including work simplifying electrical machines, power systems, power electronics, and renewable energy for candidates preparing for competitive job exams. Teaching sharpened something consulting rewards: if you cannot explain a result plainly, you do not fully understand it.