Rahul Yadav

I am a final-year Computer Science and Engineering student at VIT Vellore.
My focus is on building integrated systems at the intersection of AI, Augumented Reality, and Cloud Computing to bring real-time health intelligence into normal life, with the aim of improving human health and enabling early disease prevention. To this end, I lead Project Aero Stream, a cloud-connected health platform that captures and analyzes nasal breathing patterns in real time to enable precision respiratory diagnostics and remote patient monitoring.

Throughout my studies at VIT Vellore,I have I have actively contributed to several projects aimed at advancing health innovation and education, including Project Aero Stream, Uni-Papers.

Projects  /  Leadership

Email  /  CV  /  GitHub  /  Linkedin /  Twitter

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Projects

My work focuses on developing new AI methods to understand and engineer health systems, with the goal of enabling early disease prevention and fostering collaborative approaches in education to improve quality life. By bridging computation with experimental validation, I aim to create novel technologies for early detection of life-threatening conditions such as cancer makers, early stage infections and to advance educational tools that make learning more accessible and impactful.

All of these projects are my original work, and I retain full ownership and authorship of each.
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Early Stage Disease Diagnosis System: Platform for detecting early stage diseases using Nail Images, ML model in backend.
Next.js,Python,Python Web Frame works,CNN,IBM Watson,Deep Learning
Link / code

I led the development of the Nail Disease Diagnosis System, an AI/ML-powered platform designed to detect early-stage diseases through analysis of human nail images. Users can upload images of their nails, and the system returns diagnostic insights in real time. By training a deep convolutional neural network on a large, curated dataset, we achieved a validation accuracy of 97.8% with minimal loss, demonstrating strong generalization performance. The system is optimized for low-latency inference and is built with scalability and user accessibility in mind.

Uni-Papers: Platform for sharing notes and exam papers while earning revenue from adsense.
Next.js, Supabase, Google Gemini, Twilio, Vercel
Link / code

I built Uni-papers.com as an AI-powered academic platform where students can access, upload, and monetize university-level papers. By combining smart search with auto-review tools, the site helps students write better, faster — while earning revenue through paper uploads and Google AdSense integration.

AeroStream: LLM-powered Disease Detection via Breath Analysis (Ongoing)
Next.js, Azure, Tailwindcss, Supabase, Vercel, Twilio, Custom ML model, GPT
code

We introduce AeroStream, a contactless disease screening pipeline that analyzes breath signals using large language models, achieving early detection of respiratory and metabolic conditions. This approach improves accessibility and diagnostic speed without increasing hardware or computational cost.

Text-Aware Image Processor – 70% Cost-Reduction OCR Pipeline
opencv-python, matplotlib, numpy, pandas, torch, torchvision, tqdm, pytesseract, paddleocr
code

We propose a two-stage OCR pipeline that first detects the presence of text before running full recognition, reducing compute usage by over 70%. This architecture enables scalable, cost-efficient document processing without compromising OCR accuracy.

URL shortener: AWS Serverless Stack
Nextjs, AWS Lambda, S3, DynamoDB, Cloudflare
Link / code

We developed a high-availability truley free URL shortener using a fully serverless architecture—Next.js on the frontend, with AWS Lambda, S3, and DynamoDB on the backend, secured and accelerated via Cloudflare. The system is optimized for low-latency redirection and infinite horizontal scalability with near-zero infrastructure overhead.

Real Time Patient Health Data Hardware Device
Sensors: AD8232 , MAX30102 , BMP180, MLX90614
Other Modules: Raspberry pi 4, Display module
code

We present a wearable disease detection system leveraging multimodal biosensors and hierarchical ML inference on Raspberry Pi 4. By integrating vitals and breath biomarkers with patient history, our system achieves real-time risk prediction with low-latency, on-device intelligence and cloud-based reporting.

Leadership

In addition to technological innovation, I am passionate about leadership and business, and strive to create solutions that empower people and improve lives. This project reflects that vision—a wearable disease detection system that brings accessible, real-time health insights to patients and doctors through intelligent, sensor-driven design.

Aarogya – Rural Health Data Platform (Pre-Launch)

As a Software Engineer at Aarogya, I developed a zero-cost health data platform for rural Nepal, combining React Native and Google APIs to support underserved communities. Beyond engineering, I contributed to business strategy, logistics, and patient outreach—driven by a mission to make healthcare more accessible and impactful.

Education

My education has been driven by a deep curiosity for technology, innovation, and real-world impact. I’ve focused on building a strong foundation in computer science while actively applying my skills through research, projects, and interdisciplinary learning that bridges engineering, healthcare, and entrepreneurship. Looking ahead, I aspire to expand my knowledge and grow through opportunities at some of the world’s leading institutions.

Vellore Institute Of Technology
Bachelor of Technology,
Computer Science and Engineering [2022 - 2026]
CGPA : 7.96 / 10

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