My name is Vansh Patel! I am a student at the University of Kentucky majoring in computer science. My recent work includes leveraging machine learning models to build advanced forecasting models that solve real-world problems. Alongside my focus on AI, I have a deep enthusiasm for web development and crafting scalable high-performance applications with modern frameworks. My academic and project experiences span predictive analytics, automation, and immersive AR gaming, all guided by a commitment to continuous learning and practical impact. I thrive in collaborative environments where I can apply technical skills to create meaningful solutions. I am eager to contribute to forward-thinking teams at the forefront of innovation.
Bachelor of Science, Computer Science & Minor in Mathematics
Center for Computational Sciences (CCS), University of Kentucky
Engineered a comprehensive research platform using Next.js, TypeScript, and Tailwind CSS that accelerated AI integration into scientific workflows across 5 university departments
Technologies: Next.js, TypeScript, Tailwind CSS
Developed an automated transcription and formatting system using Python that transformed hours of audio/video content into structured data 2x faster than existing solutions
Technologies: Python, PIL, pytesseract
Orchestrated the design and deployment of software applications leveraging Chroma DB, enhancing data retrieval capabilities across 5 university departments by 30%
Center of Applied Artificial Intelligence, University of Kentucky
Led geospatial data visualization for RADOR-KY, developing predictive models that identified emerging opioid overdose hotspots
Technologies: Python, GeoPandas, NumPy, ArcGIS Pro
Created automated hotspot detection system using ArcGIS Pro that visualized high-risk areas with precision, enabling targeted resource allocation
Impact: Improved emergency response resource deployment
Engineered data pipeline using Python, GeoPandas, and NumPy, that processed daily health datasets, reducing map generation time by 17%
Lightweight card game developed in Go, utilizing concurrency for performance.
Key Achievement: Implemented fast and efficient gameplay using Go routines.
A machine learning system for optimized order forecasting and time-slot reservations, improving store operations for carryout and delivery.
Key Achievement: Achieved 92% accuracy in predicting store capacity and delivery windows.