Prabakaran Chandran

I chose control engineering somewhat by accident. My plus-two marks weren't what I needed for the top courses — mechanical, computer, triple E. Control engineering was offered in just seven colleges across Tamil Nadu. Less competition, I thought. Maybe I'd get a gold medal. That was the whole plan. It was naive. I didn't get the medal either — health issues got in the way. But what I did get, without looking for it, was a lens. Control theory gives you something: everything is a system. Anything that can be seen as a system can be modeled, understood, acted upon. I've never stopped seeing the world that way.

Three things crystallized through that degree: systems, computation, and intelligence. Control systems and state-space models. Signal processing and soft computing — neural networks, genetic algorithms, evolutionary computing. Not deep, not research-level. But enough to kindle the interest and give me a frame. I didn't know it then, but that frame would organize everything that followed.

Then came Mu Sigma. Dheeraj and the whole complexity science orientation there aligned naturally with what I had already started building. Complexity, non-linearity, feedback, emergence — not just as engineering concepts but as a way of reading business problems and social systems. Over three years there, the frame kept widening: systems, behavior, intelligence. Behavior because when you model how people buy, how markets move, you're studying the behavioral aspects of social systems. Intelligence because that's what we were always pointing toward.

After that: Captain Fresh, Informatica, a stealth startup. Six-plus years of production ML systems — satellite imagery, aquaculture, document understanding, enterprise AI, agentic pipelines. I learned how to operate. I learned where the real friction is. I also noticed what I kept running into: the gap between knowing how to run a model and knowing why the model should work the way it does. Industry, under time pressure, lets you settle for "good enough." I stopped wanting to settle.

That's why I came to Columbia. MS in Data Science — reinforcement learning, causal inference, probabilistic modeling, dynamical systems. I'm also TA-ing in courses: Applied Risk Analytics, Causal Inference, Advanced Analytics, Statistical Analysis. The goal isn't to add credentials. The goal is to fill the gaps rigorously — to develop the theoretical foundation that makes my problem-solving genuinely different, not just experienced.

One month into the program I wrote about this: the metamorphosis. Unlearning the industry reflex to "just build a project" and learning instead to do the mathematical exercises, the derivations, the deliberate theoretical practice. A project should emerge as cumulative learning, not as a shortcut to something to show. That's a different way of working. I'm trusting it.

What I believe in: higher-order problem-solving. Not zero-order (pick a model from the list) or first-order (tune it and ship). The problems I care about don't have obvious algorithm choices. They require you to understand the system, formulate the problem correctly, think about mechanisms — not just fit to data. That's the direction.

I'm not chasing the usual signals. I know what I've built, I know what I'm building toward. The work is what matters. Manhattan is temporary.