Hi, I'm Loi a passionate web developer and data engineer
Idevelop web apps with Next.js and Node.jsbuild secure APIs and backend servicescreate scalable data pipelinesvisualize data for insights
Stick around to see some of my work.
Oh hello there, my name is Loi Phan, a recent graduate with a passion for both full-stack web development and data engineering. I enjoy bringing together the best of both worlds—building seamless, user-friendly web applications while handling the complexities of data processing and management. My technical skills range from Next.js and Node.js for developing robust web solutions to using Apache Spark, Kafka, and AWS for designing scalable data pipelines.
What excites me most is working on end-to-end projects that integrate full-stack development with data engineering, allowing me to create solutions that not only deliver great user experiences but also manage and process data efficiently. I'm constantly learning and eager to explore new opportunities to combine these two fields in innovative ways.
Built a portfolio website using Next.js 14, showcasing personal projects, skills, and contact information with a responsive, modern design. Integrated sections for introduction, about, technologies, and project highlights. Framer motion animation while scrolling through the page, persisting dark mode, sanity backend for projects and filter.
Connect with other developers with this social media
This project involves three key tasks: (1) Classifying furniture images into six categories (beds, chairs, dressers, lamps, sofas, tables), (2) Recommending 10 visually similar furniture items based on a user-provided image, using a custom similarity metric, and (3) Extending the recommendations by ensuring the suggested items match the interior style of the input image, with a model trained to recognize different design styles.
The objective of this project is to identify regions with similar economic, deforestation, and weather patterns in order to provide tailored policy recommendations.
This project focuses on three tasks: (1) Accurately classifying unseen images into one of eight flower types, (2) Recommending 10 visually similar flowers of the same type based on an input image, and (3) Developing separate models for flower classification and flower recommendation to optimize performance for each task.
Developed a web-based Learning Tool to assist lecturers in planning and managing classroom activities, tracking course modules, and providing real-time insights into weekly course content. The tool features a user-friendly drag-and-drop interface and integrates seamlessly with Canvas for enhanced course planning.
I work with the following technologies and tools:
Matplotlib
Seaborn