I'm Sagar Tailor, a Computer Science & Engineering student building around AI/ML, backend engineering, systems and scientific computing.
I am most interested in the part of a product that sits underneath the interface — the data pipeline, the model, the API, the concurrent process, the architecture, the failure mode, and the engineering decisions that make a prototype dependable.
My GitHub is meant to show that progression through actual repositories, experiments and systems I can explain from the inside out.
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AI / ML Machine learning |
BACKEND APIs |
SYSTEMS Concurrency |
SCIENTIFIC Satellite data |
OBSERVE → MODEL → ENGINEER → VALIDATE → SHIP
REST APIs · WebSockets · Concurrency · System Design · Automation
NumPy · Pandas · Scikit-learn · Machine Learning
· Deep Learning · Remote Sensing · Geospatial Data
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High-performance deadlock simulation & concurrency visualizer. A real-time systems project with a concurrent Go simulation engine and a reactive Next.js frontend. It models resource contention, evaluates safe states using Banker's Algorithm, detects cyclic dependencies and pushes state changes through WebSockets. Built with
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Atmospheric Knowledge — AQI from Satellite Harmonics. A scientific-computing project around satellite-derived atmospheric data, environmental analysis and engineering workflows for working with large scientific datasets. Built with
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The layer underneath. A systems-minded personal portfolio designed as a cross-section: surface → interface → engine → substrate Built with
· → Live |
Airfare Price Index — collaborative work. A quality-adjusted, high-frequency airfare price-index project built around real-market evidence, an auditable collection pipeline and explicit statistical safeguards. I keep this under Collaborative Work because the repository is currently owned by Built with
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This is where the profile gets a little more alive.
Instead of relying on CSS or JavaScript that GitHub will strip, the motion is carried by self-contained animated SVG assets.
The car is deliberately a visual metaphor rather than decoration:
the interface moves around the system, but the system is the thing underneath.
I like problems where the obvious layer is not the interesting one.
A race condition nobody noticed.
A dataset that rejects the clean assumption.
A model that looks correct until you test it.
An API that works locally but breaks under pressure.
A system that becomes elegant only after the architecture is understood.