Innovator. Creator. Techonologist.
I’m Aditya Parekh, a Computer Science and Data Science student at the University of Wisconsin–Madison, driven by a passion for building intelligent systems that bridge innovation and real-world impact.
As a Software Engineer Intern at Hussmann, I engineered a predictive anomaly detection pipeline processing over 100,000 sensor readings using AWS and SageMaker—reducing repair costs by 40%. I also developed a VR demo and an AI-powered chatbot leveraging Azure OpenAI, which enhanced technician training and won 1st place in the Ideation & Innovation Challenge.
With the Wisconsin Autonomous Club, I helped architect a real-time perception system for autonomous vehicles, optimizing computer vision algorithms for sub-100ms performance and integrating the system into a full autonomous driving stack.
I’ve also worked as a Full-Stack Developer Intern at BooksToBorrow, deploying scalable web features with React, MongoDB, and Docker—supporting a platform launch that grew to over 13,000 users within 96 hours.
Beyond internships, I’m the co-founder of ClaimReady, a 5x award-winning AI startup that reduces insurance claim valuation time from 20+ hours to under 2 minutes, scaling to 5,000+ users and processing over $20M worth of assets.
Check it out here: useclaimready.ai.
Awards & Achievements
Recognized for technical excellence and software innovation.
1st Place MadData25 Hackathon
24-hour hackathon with 30+ teams and 120+ competitors. Won first place and best presentation.
HackathonView on DevpostClaimReadyTop 3 at University Madness Startup Pitch Competition
Placed Top 3 and were the Audience Favorite among 30+ teams across 9 universities.
National Startup CompetitionRead ArticleClaimReadyTop 3 at Badger Launchpad Startup Pitch Competition
Competed against 20+ teams from UW–Madison in a university startup competition.
University Startup CompetitionView LinkedIn PostClaimReadyWork Experience
Software Engineer Intern
Hussmann
Engineered a predictive anomaly detection pipeline for refrigeration systems using AWS S3, SageMaker, MySQL, and Python (Pandas, NumPy), analyzing 100K+ time-series sensor values and reducing costs and repairs by 40%. Authored reports outlining 15+ recurring failure conditions adopted company-wide. Built an immersive VR demo and an AI-powered chatbot using Azure OpenAI LLMs, fine-tuned on internal refrigeration documentation, enabling technician training simulations and accelerating support queries by 70%; awarded 1st place in the Ideation & Innovation Challenge.
Software Engineer
Wisconsin Autonomous Club
Architected a real-time perception system for autonomous vehicles enabling lane, cone, and boundary detection. Optimized computer vision algorithms (OpenCV, ML models) for sub-100ms performance and reproducibility. Integrated the perception module into the autonomous driving pipeline, collaborating with 30+ engineers through design reviews, CI/CD, and code quality initiatives.
Full-Stack Developer Intern
BooksToBorrow
Developed and deployed user-facing features such as Dashboard, MFA, OAuth, and Account Deletion using React and MongoDB, containerized with Docker and deployed on AWS ECS. Built and validated RESTful APIs with Postman to enhance reliability across staging and production environments. Supported a successful product launch achieving over 13,000 users within the first 96 hours.
Education
Bachelor of Science in Computer Science and Data Science
University of Wisconsin - Madison
Expected Graduation 2027
Skills & Technologies
A comprehensive overview of my technical expertise and proficiency levels.
Languages
Frameworks / Libraries
Developer Tools
Featured Projects
A showcase of notable projects I've worked on.
ClaimReady
Co-founded and led frontend development of ClaimReady, an award-winning AI web app that generates complete home inventories and reduces insurance claim valuation time from 20+ hours to under 2 minutes. Scaled to 5,000+ users by deploying an image valuation pipeline with Docker, Vercel, AWS ECS/Fargate, YOLO11 for detection, Gemini API for brand/price retrieval, and Supabase; processed over 22,000 images valuing $20M+ in items.
Gordon AI
Developed Gordon AI, an AI-powered cooking assistant that leverages Google Gemini API, prompt engineering, and persona emulation to deliver dynamic, in-character responses like Gordon Ramsay. Built a full-stack web app using React/Next.js and TypeScript with Supabase for user data persistence, enabling real-time interaction and personalized recipe recommendations.
Vecaid
Developed an AI-driven stock prediction system using deep learning and ensemble models (GRU, CNN-LSTM, XGBoost), improving trend prediction stability and reducing forecasting error across 60-day backtests. Engineered a Flask-based full-stack web app displaying data and real-time predictions for 50+ stock tickers.