Still Mid-Service

When I was growing up in Chennai, the clearest ambition I had was the Indian Administrative Service. Not for the status — though I will not pretend the status did not exist in the air of the household — but for the nature of the work. To be embedded in a district. To be present for the administration of ordinary life: water, land, grievance, infrastructure. The IAS officer was the person who showed up, who knew the file, who had sat through the hard meeting.

My father ran a workshop. He worked beside his workers, not above them. That model was always more compelling to me than the one that watched from a distance.

The exam path diverged. Engineering happened; then a master's in Germany; then the deep pull of a specific kind of problem — how machines can learn to see structure in the data of physical simulation. The IAS did not happen. It was deferred, and then the deferral became the path.

What Deferral Teaches

There is a failure mode in ambitious people that I have seen close-up: the belief that the path not taken was the real one, and that whatever you are doing is a provisional arrangement before the true work begins. This is a way of not being present in your own career.

I do not know if I escaped this entirely. But I have come to think of the IAS dream not as the road I did not take but as a kind of persistent signal in the background — a orientation toward public utility, toward being useful rather than impressive, toward the long view of institutions rather than the short view of output.

That signal shapes what I work on, even inside machine learning for automotive simulation. Anomaly detection is not glamorous. It does not surface at conferences as often as generative models do. But it has a direct line to a safety outcome: the crash test, the vehicle, the occupant. The dangerous case nobody noticed is a failure of the institution of safety, not just a technical miss. Working on it feels continuous with the instinct that drove the IAS ambition — the instinct to be present where the stakes are real.

Learning in Public

This site — and these notes in particular — is an exercise in learning in public, which I do not do naturally. I was trained, as most engineers are, to present results rather than process. You put a paper into the world when the method is validated, not when you have an interesting hunch. You submit the thesis when the experiments are done.

I want these marginalia to capture some of that process: the currently wrong model, the vocabulary problem in mesh generation that is not yet solved, the hunch about geometric attention that might be the next step or might be a dead end. This is the working draft of a career, not the final submission.

The Blank Posting

The last item in the service record on this site is a placeholder. "The next posting — reserved, blank in the ledger." I added it because a ledger with a clean ending would be a lie. The record so far: a coastal childhood in Tamil Nadu, an engineering degree, a master's at RWTH Aachen, a thesis at Porsche, production work at Kube. That is four postings, each complete, each feeding into the next.

What comes after Darmstadt — I do not know yet. There are possibilities that involve staying inside industrial AI. There are possibilities that involve a pivot toward something that looks more like the public-utility instinct. There are possibilities I cannot see from where I am standing.

The simulation is still running. Iteration by iteration, the weight of each experience accumulating, the direction becoming clearer without the destination being fixed.

That is not a poetic frame I have imposed on the situation. It is the actual situation. The next posting is blank because I have not been posted there yet.

I will write about it when I arrive.