priya-ranjan@station:~$ whoami --verbose

I am a Computer Science & Engineering undergraduate (B.Tech 2024–2028) driven by a passion for understanding computing from first principles. Rather than treating computers as black boxes, I care about what happens in the milliseconds between hardware instructions, operating system scheduling, network socket packets, and user interfaces.

• Low-Level Systems & OS Kinematics: Process scheduling, disk arm seek kinematics, memory layout, and Linux POSIX internals.
• Defensive Cybersecurity & Networks: Asynchronous socket concurrency, port vulnerability inspection, cryptographic audit logging (SHA-256), and STIX 2.1 threat modeling.
• Intelligent Applications & AI: Abstract Syntax Tree (AST) static code analysis, semantic vector retrieval with Qdrant, and explainable AI with TreeSHAP.
• Full-Stack Systems Architecture: Building resilient backends (FastAPI, AsyncIO, Flask) and responsive clients (React, Next.js, HTML5 Canvas) verified by automated test suites.
• 🌐 Digital Public Infrastructure (DPI) & Geospatial Systems: Architected India's Smart Tourism ecosystem (TECH-ON-TOUR / TravelSathi), implementing multilingual itinerary generation, anti-overtourism gatekeeping, and MongoDB 2dsphere geospatial search graphs.

🟩 CONTRIBUTION ACTIVITY FEED

A curated portfolio of open-source repositories, system kinematics simulations, and production prototypes:

• Core Problem: Hiring platforms rely on superficial keyword matching, rewarding buzzword stuffing rather than verified structural coding ability.
• Architecture & Innovation: Parses submitted source code using Python Abstract Syntax Trees (AST) to measure structural complexity without execution vulnerability. Pairs insights with Qdrant vector embeddings to build prerequisite learning roadmaps.
• Engineering Status: Validated with 153/153 passing automated test suites across FastAPI endpoints, AST visitors, and vector searches.
• Tech Stack: Python 3.11 • FastAPI • React 18 • Qdrant Vector DB • PostgreSQL • Docker Compose
• 🔗 Explore Repository →

• Core Problem: Web and internal services are frequently exposed to misconfigured headers, unpatched ports, and weak TLS handshakes.
• Architecture & Innovation: Built on Python 3.13 AsyncIO with bounded semaphore connection pools for sub-15ms multi-vector socket inspection, HTTP security header auditing, and SHA-256 tamper-evident cryptographic audit trails.
• Engineering Status: Enforces defensive security invariants with STIX 2.1 threat modeling integration.
• Tech Stack: Python 3.13 AsyncIO • Raw Sockets • STIX 2.1 • SHA-256 Audit Logs • Defensive Security
• 🔗 Explore Repository →

• Core Problem: Popular tourist corridors face unmanaged overcrowding, lack of regional language assistance, and fragmented transit routing.
• Architecture & Innovation: Architected as India's Digital Public Infrastructure for Smart Tourism. Combines GeoJSON 2dsphere spatial indexing, dynamic anti-overtourism gatekeeping algorithms, and a regional destination search graph.
• Engineering Status: Full-featured smart tourism platform with multilingual itinerary generation, dynamic transit clustering, and geospatial load balancing.
• Tech Stack: FastAPI • React • Python • MongoDB (2dsphere) • Leaflet • Search Graphs
• 🔗 Explore Repository →

• Core Problem: Visualizing and benchmarking seek latency tradeoffs between classical disk arm scheduling algorithms is difficult using static textbooks.
• Architecture & Innovation: Implements and benchmarks 11 classical disk arm scheduling algorithms (FCFS, SSTF, SCAN, C-SCAN, LOOK, C-LOOK) paired with real-time 60 FPS HTML5 Canvas physical arm seek kinematics.
• Engineering Status: Interactive OS simulator benchmark suite comparing head movements and turnaround times.
• Tech Stack: JavaScript (ES6+) • HTML5 Canvas API • OS Storage Kinematics • Algorithms
• 🔗 Explore Repository →

• Core Problem: Clinical machine learning risk models are often opaque "black boxes" that clinicians hesitate to trust without attribution.
• Architecture & Innovation: Enterprise preventive cardiology platform featuring explainable risk modeling powered by TreeSHAP attribution and dynamic interactive risk simulation sliders.
• Tech Stack: React • Machine Learning • TreeSHAP • Zustand • Clinical AI
• 🔗 Explore Repository →

• Core Problem: Traditional online testing suites suffer from static question sets, monolithic deployment architectures, and vulnerability to automated tampering during timed tests.
• Architecture & Innovation: Cloud-native assessment platform with dynamic question bank generation, zero-latency countdown timers with automatic submission fallbacks, deterministic question randomization, and an HTML5 Canvas visual analytics engine for historical performance tracking.
• Engineering Status: Packaged in an ultra-lean 2-stage multi-stage Docker build (<180 MB) with non-root security (appuser), persistent volumes, and automated GitHub Actions CI/CD workflows.
• Tech Stack: Python 3.13 • Flask 3.x • Docker Multi-Stage • PostgreSQL / SQLite • Bootstrap 5.3 • HTML5 Canvas
• 🔗 Explore Repository →

• Core Problem: Job applicants lack transparency into how automated Applicant Tracking Systems (ATS) and enterprise screening algorithms parse and grade their qualifications against job descriptions.
• Architecture & Innovation: Unites Scikit-Learn Machine Learning (Random Forest classifiers) and spaCy/BERT NLP semantic embeddings for 360° talent analysis. Computes multi-dimensional ATS parseability scores, provides an interactive drag-and-drop resume engineering studio with 6 executive templates, and evaluates skill gaps against live market descriptions.
• Engineering Status: Production-ready responsive UI built on React 19, Vite, and Tailwind CSS, backed by high-throughput FastAPI microservices and automated CI validation.
• Tech Stack: React 19 • FastAPI • Python 3.12+ • Scikit-Learn • spaCy / BERT NLP • Tailwind CSS • Docker
• 🔗 Explore Repository →

• Core Problem: Maintaining continuous repository telemetry and legitimate forward-moving git activity across repositories without manual daily chores.
• Architecture & Innovation: Autonomous, scheduled GitHub Actions engine with an organic dynamic decision engine, random jitter, and built-in ephemeral hourly GITHUB_TOKEN security.
• Tech Stack: GitHub Actions • POSIX Cron • Bash • Git Telemetry • Cloud Automation
• 🔗 Explore Repository →

• First-Principles Architecture: Understand the underlying hardware constraint, network protocol, or data structure before reaching for a complex dependency.
• Resilience Under Load: Bounded concurrency pools, explicit timeouts, and defensive invariants make software dependable when pushed to its limits.
• Clarity Over Cleverness: Code is read far more often than it is written. Clean types, intuitive interfaces, and modular structures outlive clever shortcuts.
• Telemetry-Driven Decisions: Profile memory, measure execution latency, and verify behavior with automated test suites—never optimize through guesswork.

Whether you're interested in systems engineering, defensive cybersecurity, full-stack applications, or exploring collaboration opportunities: