Phillipp Cun
As a software engineer, I am fascinated by the depth and complexity of computer science and the endless ways problems can be approached and solved. I enjoy exploring different strategies, learning from each solution, and continuously improving my skills to build efficient, elegant, and practical systems. Every challenge offers an opportunity to think creatively, adapt, and grow both technically and intellectually.
Skills
Languages: Python, Java, JavaScript, TypeScript, SQL (PostgreSQL, SQL Server), HTML, C, C++ and C# Developer Tooks: GIT, JUnits and Docker Operating Systems: Windows, macOS and Linux
Experience
Web Developer Independent Consultant
- Developed responsive, user-centered web applications leveraging the Microsoft stack (C#, .NET, SQL Server, Entity Framework, Azure) and front-end technologies like HTML, CSS, and JavaScript, ensuring cross-platform compatibility and increasing user engagement by 30%
- Collaborated with clients to analyze requirements and translate business needs into functional web solutions, integrating backend data systems for improved workflow efficiency
- Implemented iterative design and testing processes within an Agile framework to enhance application performance and usability, resulting in faster delivery cycles and higher client satisfaction
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Developed Python-based scripts for backend services to streamline data processing, reporting workflows, and improved operational efficiency across the enterprise's systems
- Designed and maintained CI/CD pipelines for multi-component applications, ensuring smooth deployments, versioning, and release management
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Integrated multiple backend systems and APIs to streamline data collection and processing, which enables faster analysis for business
EUD Technician Trinity Health
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Provided technical support for enterprise systems, collaborating with cross-functional teams and maintaining detailed documentation in ServiceNow
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Troubleshot and resolved hardware, Office 365, Teams, and SharePoint issues using ServiceNow for ticketing and Active Directory for user account management
Undergraduate research Assistant The University Of Iowa
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Built automated pipelines to ingest, validate, and analyze datasets from multiple sources using Python and PostgreSQL
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Developed containerized applications using Docker for reliable deployment of data processing workflows
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Implemented unit testing and continuous integration checks to ensure data integrity, system reliability and collaborated with team members to integrate backend services into a full-stack analysis platform
Projects
Spotify Music Analysis & Recommendation Tool
This full-stack project transforms music into meaningful data insights by analyzing Spotify playlists through the data science and machine learning. By integrating with the Spotify API, this application extracts audio features such as tempo, energy, danceability, and mood to processes them using Python data tools. Leveraging libraries like Pandas and NumPy for data manipulation, and Matplotlib for visualization, the project analyze patterns and trends within playlists, giving users a deeper understanding of their music preferences. It then applies machine learning techniques, specifically cosine similarity, to recommend tracks that closely match a user’s taste. More than just a music tool, this project highlights the powerful intersection of technology and creativity demonstrating how data analysis can enhance the way we experience and discover music.
Expense Tracker
A full-stack expense tracking application that allows users to record, categorize, and analyze personal spending through an interactive dashboard. The system uses a React frontend for dynamic user interfaces and a .NET Web API backend for secure data processing and persistence. The application is deployed on Google Cloud Run, demonstrating cloud-native deployment and scalable backend services. Users can create, update, and delete expenses, while real-time analytics visualize spending patterns by category through interactive charts.
Smart Reminder
I developed a full-stack web application using React for the frontend and a RESTful backend to help users manage task urgency and notifications, making it easier to stay organized and prioritize work. The app supports real-time data persistence, so updates are saved and reflected instantly. I also built dynamic task management features like priority levels and scheduled notifications by connecting frontend state management with backend APIs, creating a smooth and reliable experience that helps improve productivity.
Calculator (Static)
I developed a static, dependency-free calculator that supports core operations like addition, subtraction, multiplication, division, exponentiation, parentheses, and decimals, ensuring accurate and safe arithmetic processing. The project included a user-friendly interface with interactive buttons and a small calculation history, making it easy to use and quickly reference past results. Behind the scenes, I used arrays and stacks to efficiently evaluate expressions, allowing the calculator to handle complex inputs while keeping performance smooth and reliable.
House Price Prediction
This project is a full-stack machine learning application that delivers real-time house price predictions through a seamless integration of a cloud-based API and an interactive frontend. The backend is built with FastAPI and deployed on Google Cloud Run, where it processes user-inputted housing features and runs a regression model to generate predictions efficiently. On the frontend, a React-based interface provides a modern, user-friendly experience with slider controls and dynamic chart visualizations, allowing users to explore how different variables impact pricing. The system demonstrates practical skills in API development, cloud deployment, and data-driven application design, showcasing how machine learning models can be integrated into scalable, production-ready web applications.