Entering Neon District
SANJAY.SEKAR.SAMUEL // PERSONAL GRID
TORONTO NODE — 43.65°N 79.38°W
ALT 300M · SIGNAL STABLE
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Incoming transmission // above the cloud layer

Sanjay
Sekar Samuel

AI engineer. I build machine-learning systems that ship — clinical intelligence for healthcare, generative discovery for materials science. From on-device neural inference to models that search chemical space at machine speed. This city is my work. Fly through it.

District 01 // The Vital Grid

Intelligence at the
point of care

I engineer AI for healthcare — embedding-driven matching at population scale, on-device neural inference at the edge, closed-loop pipelines that turn raw clinical signal into decisions in milliseconds. Privacy-preserving by design. Sovereign by default. Built to run where the patients are.

On-Device InferenceVector Embeddings Closed-Loop AIPrivacy-Preserving ML
District 02 // The Foundry

Searching matter at
machine speed

In materials science I point deep learning at the periodic table — graph neural networks for property prediction, generative models that traverse chemical space orders of magnitude faster than any lab, simulation-in-the-loop pipelines that close the gap between hypothesis and synthesis. The search space is effectively infinite. The model doesn't blink.

Graph Neural NetsGenerative Discovery Property PredictionSimulation-in-the-Loop
District 03 // The Archive

Notes from a
future in progress

Essays on humans and AI, written before it was a headline — where the machines are taking us, and why people still matter when they get there. Long-form thinking, stored in the stacks below.

Humans & AIEssaysMedium · Blog
Final descent // The Core

Build with me

You made it to street level. The grid is open — say hello.

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