I BUILD AI SYSTEMS. THAT SHIP.
AI & Data Science student building meaningful AI systems, agentic workflows, and full-stack ML products that move from idea to deployment.
I LIKE BUILDING SYSTEMS THAT ARE MEANINGFUL
I am drawn to AI products where the model is only one part of the system. The best builds have a clear problem, a reliable pipeline, a usable interface, and explanations people can trust.
My projects sit around agentic AI, explainable ML, ML governance, and full-stack product engineering - the kind of work that survives beyond a demo screen.
"Useful beyond demos is the bar."
CASE FILES FROM THE BUILD LAB
AlterScore
Alternative credit scoring for unbanked borrowers, turning behavioral, cognitive, and text signals into a 300-850 score with explainable recommendations.
Pathfinder
An AI-assisted attack-path finder that models networks as weighted graphs, uses A* search, and ranks patch strategies by mitigation impact.
Agentic Systems
Exploring agentic workflows, harness engineering, evaluation loops, and product patterns that make AI systems more reliable and useful.
THE CURRENT SIGNAL STACK
Agentic AI
Workflows where tools, memory, retrieval, and decisions are designed as a system, not just prompted into existence.
Harness Engineering
Building the scaffolding around models: evaluations, traces, checks, fallbacks, and controlled execution paths.
Explainable ML
Models that can show why they made a decision, what changed the output, and where the system is uncertain.
ML Governance
Drift tracking, fairness checks, counterfactual stability, and the boring-but-important pieces that make ML safer.
Full-stack AI Products
End-to-end products with usable interfaces, APIs, deployments, and feedback loops - not just notebooks.
BUILD SOMETHING MEANINGFUL?
Reach out for projects, internships, collaborations, or a good conversation about AI systems that should actually work.