Computer Science undergraduate building high-performance web applications, intelligent AI systems, and scalable production-ready software.
π Currently Building: High-performance full-stack applications and intelligent AI-powered systems.
π€ Focused On: Applied AI, Machine Learning, Temporal Intelligence, Graph-Based Systems, and scalable backend architecture.
π± Currently Learning: Advanced ML, LLMs, System Design, Cloud Architecture, and Data Structures & Algorithms.
πΌ Experience: Former Full Stack Developer Intern at GoGlobalways, where I contributed to the Bharat AI Olympiad (BAIO) platform.
π Publication: Co-Author of a research paper accepted in Elsevier SSRN Proceedings (ICDPN 2026).
π Achievement: Finalist in the Meta Γ Hugging Face Γ OpenEnv Γ PyTorch Hackathon 2026, ranking in the Top 2.5% among 71K+ developers.
π― Goal: Build intelligent products and scalable systems that solve meaningful real-world problems.
A real-time fraud intelligence system that combines tabular machine learning with temporal and relational transaction signals.
Traditional fraud detection evaluates transactions individually.
VYUH looks beyond a single transaction.
It combines:
- β‘ Real-time transaction intelligence
- π§ Machine Learning fraud prediction
- πΈοΈ Temporal relationship analysis
- π Bipartite transaction graphs
- π Streaming fraud signals
- π³ Containerized deployment architecture
π¦ Total Transactions Processed β 590K+
π§ Joint Feature Model β 23 Features
π PR-AUC Improvement β +29.6%
π― Fraud Recall Improvement β +51.2%
π Chronological Holdout β 118K Records
β‘ P50 End-to-End Latency β 7.46ms
β‘ P95 End-to-End Latency β 8.38ms
π ADD YOUR VYUH GITHUB REPOSITORY LINK HERE
An AI-powered space weather forecasting system for probabilistic GEO electron-flux prediction.
- Fine-tuned Salesforce Moirai-1.0-R-Small
- Used LoRA for efficient model adaptation
- Trained on 11 years of space-weather data
- Processed 1.26M+ five-minute observations
- Combined solar-wind and geomagnetic signals
- Engineered physical wave-derived features
- Used Morlet Continuous Wavelet Transform
- Built inference APIs with FastAPI
- Developed a React-based application layer
π Training Data β 1.26M+ Observations
π
Historical Data β 11 Years
π Forecast MAE β 42.1 β 14.5 pfu
π MAE Improvement β 65.6%
π― Threat Classification β 94.2%
β‘ CPU Inference β <5 Seconds
π ADD YOUR PRAHARI GITHUB REPOSITORY LINK HERE
A production-ready MERN e-commerce platform built with a focus on security, payments, real-time functionality, and backend performance.
- π JWT Authentication
- π‘οΈ Helmet.js Security Hardening
- π³ Razorpay Payment Integration
- π HMAC-SHA256 Payment Verification
- π¦ Real-Time Order Tracking
- β‘ Socket.IO WebSockets
- π Complete E-Commerce Workflow
- π Production Deployment
π₯ Requests Stress Tested β 50,000
β‘ Throughput β 12,502 RPS
π Average Latency β 5.86ms
π― P99 Latency β 12ms
β
Success Rate β 98.5%
May 2026 β June 2026
Worked on the Bharat AI Olympiad (BAIO) platform, contributing across multiple production workflows.
- Built functionality across Student, School, and Admin portals
- Implemented JWT-based Role-Based Access Control
- Developed registration workflows
- Worked on payment workflows
- Implemented result-management systems
- Optimized MongoDB query plans and indexes
- Performed local backend load testing
β‘ Authenticated Reads β 41.4 RPS
β
Success Rate β 100%
π Health-Check Throughput β 11,494 RPS
π₯ Winner β Devcation Delhi Hackathon 2026, IIT Delhi
π₯ 2nd Position β SSH 1.0 Hackathon, Satyaansh SoftTech Pvt. Ltd.
π₯ 3rd Runner-Up β InnovAItion Hackathon, Intuitive.ai
π Finalist β Meta Γ Hugging Face Γ OpenEnv Γ PyTorch Hackathon 2026
Ranked in the Top 2.5% among 71K+ developers
π Co-Author β Research paper accepted in Elsevier SSRN Proceedings (ICDPN 2026)
π Ranked in the Top 20% of reviewed submissions
π¨βπ» Web Development Lead β Google Developers Group On Campus (GDG OC)
π§© 100+ LeetCode Problems Solved
Co-Author
Accepted in:
π Top 20% of reviewed submissions
βοΈ Building intelligent AI-powered systems
πΈοΈ Exploring graph-based and relational intelligence
π€ Learning advanced Machine Learning architectures
β‘ Improving backend performance and system design
π Mastering Data Structures & Algorithms
π Shipping impactful real-world products
| Project | Focus | Status |
|---|---|---|
| βοΈ VYUH | Temporal Relational Fraud Intelligence | π Active |
| π°οΈ PRAHARI | AI Satellite Radiation Forecasting | π€ AI |
| ποΈ Wobblix | Production MERN E-Commerce | π Live |
| π BAIO | Bharat AI Olympiad Platform | πΌ Experience |
Building. Learning. Shipping. Repeating. π
