I work where artificial intelligence meets biology.

My research asked how a single cell becomes an entire organism. My path since hasn't run in a straight line — molecular genetics, financial markets, machine learning — but it keeps circling back to that question, and the questions next to it that AI is finally making tractable: can we model a mutation's effect in silico, learn the signatures of a cell type, steer a stem cell's fate, build a digital model of human disease?

I grew up in Kerala, on the southern tip of India, and became a scientist there — learning by doing through the Kerala Forum for Science Literature, whose magazines Eureka and Sasthra Keralam were my first window into research. That carried me to CCMB — often called the crown jewel of Indian biological research, which takes roughly ten students a cycle from thousands — where I also fell for computers; the first site I ever built, a lab page, is somehow still online twenty years later. After a post-doc at UC Davis, the 2008 crash pulled me into markets full-time — long enough to learn they run on emotion, and to build an automated Python/Django trading platform (SEC-EDGAR scraping, a live Interactive Brokers link) to take the emotion out. A health-data fellowship at Insight Data Science then turned that habit of building into machine learning, aimed back at biology.

Here's the belief underneath all of it: the real power of AI isn't handing you the most probable answer — it's that it can write complex code that produces deterministic, reproducible results. Give it the rules of a system and it makes them executable; follow that far enough and the ambition is to codify an entire organism in code. That's why I work where AI meets biology — the rigor of the bench, the discipline of the markets, and the craft of shipping software were all preparation. Some recent work in that direction: reanalyses of public brain data on multiple sclerosis.

Robotics is where my love of engineering meets teaching. For years I've coached, mentored, and judged competitive robotics — because the best way to grow the field is to grow the people in it.

Science, markets, machine learning, and the mind — a few of the books that shaped how I think.

Experiments in neural style transfer.