Final-year CS undergrad in Bengaluru. I build complete, production-minded systems across the stack — a low-latency Go backend, a real-time full-stack product, explainable ML services, and data investigations that land concrete findings. Every project below is finished, documented, tested, and reproducible from a single command.
🔗 linkforge · Backend systems · Go
A production-minded URL shortener focused on correctness and operational maturity.
~26,500 req/s on the redirect hot path at p99 ≈ 5.4 ms, 99.99% cache hit rate, zero errors (single instance, Postgres + Redis).
Cache-aside Redis, rate limiting, async click analytics, Prometheus metrics, k6 load tests, testcontainers integration tests.
📋 devboard · Full-stack product · TypeScript
A real-time Kanban tool (Trello/Linear-style) with live multi-client sync.
JWT refresh-rotation with reuse detection, optimistic drag-and-drop with rollback, typed end-to-end contracts (shared Zod schemas) —
docker compose upand you're running.
Socket.IO rooms, role-based sharing (owner/editor/viewer), live activity feed, React + Express + Prisma, Jest + Supertest.
🧮 credit-risk-explainer · Machine learning · Python
An end-to-end loan-default model that explains every prediction with SHAP.
ROC-AUC 0.871, PR-AUC 0.404 vs 0.067 baseline, Brier 0.049 — with the decision threshold set by an explicit 5:1 miss-cost, not accuracy.
Reproducible pipeline → calibrated XGBoost → FastAPI service → Streamlit dashboard. A score you can't explain is a score you can't audit.
📉 hidden-mutual-fund-fees · Data investigation · Python
Measuring what India's "Regular" mutual-fund plans quietly cost investors.
Across 1,084 matched fund pairs, Direct beat Regular in 99% of cases — costing a typical equity SIP ~₹27 lakh over 20 years (18% of the corpus). Paired Wilcoxon p ≈ 10⁻¹⁷⁶.
A clean, assumption-free measurement from official NAV data — identical portfolio, two share classes, the gap is the commission.
🏭 wafer-yield-predictor · ML under imbalance · Python
Semiconductor fab pass/fail prediction from ~590 sensor measurements, where only 6.6% of wafers fail.
Catches 86% of defective wafers — while a naive "all-pass" model scores 93% accuracy and catches zero. Accuracy is deliberately not the headline.
Leakage-safe preprocessing, imbalance-aware model selection, per-wafer SHAP attribution so an engineer can trace a yield excursion to the responsible sensors.
🇮🇳 Civic data tools — schemefinder · safedose · mandicompass · jalcheck
Four tested, Dockerized tools that turn buried Indian public data into something usable: government-scheme eligibility, spurious-drug batch checks, mandi net-price ranking with MSP alerts, and district water-contamination lookup.
Shared design principle: three-state engines that never overclaim or over-reassure. 133 tests across the four.
Recent contributions:
- dolthub/dolt — pinned the aborted-rebase state in enginetests; reproduced & triaged a reported error-message bug
- alibaba/open-code-review — fixed the black-screen flash on lazy route transitions with React 18
startTransition - cozystack/cozystack — contract tests pinning the label-automation workflows (CNCF)
Stack: Go · TypeScript/React/Node · Python (scikit-learn, XGBoost, SHAP, pandas, FastAPI) · PostgreSQL · Redis · Docker · k6 · pytest/Jest