I'm a Software Engineer with a Master's degree in Computer Science from Southern Methodist University, specializing in Artificial Intelligence. I focus on Software Engineering and Full Stack Development, building web applications from the ground up—from the frontend user experience to the backend infrastructure. I enjoy incorporating AI technologies into my projects, using machine learning models and intelligent features to make applications smarter and more useful.

• Languages: Python, JavaScript, TypeScript, HTML/CSS, Java, Swift, MATLAB, C, C++, Objective-C, SQL
• Frameworks & Libraries: React, Next.js, React Native, Node.js, Spring Boot, Flask, FastAPI, Vite
• Data Science & ML: Pandas, NumPy, XGBoost, scikit-learn, Kaggle API
• Databases: PostgreSQL, MySQL, SQLite, MongoDB, Supabase
• Tools & Technologies: Git/GitHub, VS Code, IntelliJ IDEA, Google Calendar API
• Cloud & Deployment: AWS, Vercel, Railway, Render

• LinkedIn
• Personal Website

Here are some of my featured projects showcased on my portfolio:

1. Planno - Scheduling Platform | Code | Live Demo

   • Description: A full-stack scheduling platform that helps bishops create shareable booking links for interviews and meetings, enabling members to schedule directly from real-time availability.
   • Problem: Manual meeting coordination causes scheduling conflicts, missed appointments, and significant time spent on back-and-forth communication.
   • Solution: Shareable booking links with real-time availability, Google Calendar integration for automatic sync, and Apple Calendar download support for seamless cross-platform scheduling.
   • Tech Stack: Next.js, TypeScript, React, Supabase, PostgreSQL, Google Calendar API, Vercel

2. TastyFood - Food Ordering Platform | Code | Live Demo

   • Description: A custom full-stack food ordering platform that enables local restaurants to accept online orders directly from customers, providing complete ordering and delivery management.
   • Problem: Restaurants lose 15-30% of revenue to third-party delivery services and lose direct customer relationships and control over pricing.
   • Solution: Direct customer ordering through role-based dashboards, eliminating commission fees and giving restaurants complete control over customer relationships, pricing, and delivery logistics.
   • Tech Stack: React, Vite, Java, Spring Boot, PostgreSQL, SQLite, Railway

3. Premier League MVP Predictor | Code | Live Demo

   • Description: A machine learning project that analyzes player statistics from the English Premier League to predict and determine MVP candidates based on comprehensive performance metrics.
   • Problem: MVP selection is often subjective and doesn't account for all relevant performance metrics, potentially overlooking players who contribute significantly across multiple categories.
   • Solution: XGBoost regression model trained on comprehensive player statistics (goals, assists, clean sheets, influence, creativity, threat) to objectively predict player value and identify top 25 MVP candidates using data-driven analysis.
   • Tech Stack: Python, FastAPI, XGBoost, scikit-learn, Pandas, NumPy, Kaggle API, Render

Feel free to reach out if you have any questions or just want to connect!

• Email: dimitrilavin@gmail.com

• Advanced Full Stack Development
• AI/ML Integration in Web Applications
• Software Project Planning and Management

• Contribute to open-source projects
• Continue building scalable full-stack applications
• Enhance projects with AI/ML capabilities
• Expand my portfolio with innovative solutions

Thank you for visiting my profile! Have a great day! 😄