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Jordan Chen
About Me
I'm a student at the University of California San Diego (expected June 2028) pursuing a B.S. in Mathematics Computer Science with minors in Data Science and Cognitive Science.
I work at the intersection of full stack development, data visualization, and human computer interaction, with interests in AI tooling, computer vision, and statistical modeling.
Experience
UCSD Design Lab
CurrentPackaged an AI assisted chatbot component for easier installation within the Meridian npm package (UCSD developer package). Developed prompt engineering and Python tooling so Meridian users can dynamically generate customized UI components from chat prompts. Built full stack demo applications integrating Meridian with React and Next.js to exercise data binding and custom UI generation. Contributed developer feedback in design meetings that shaped upcoming Meridian features.
Sanford Burnham Prebys
CurrentIntegrated data visualization into an existing Python pipeline for graphical views of neural signals. Processed gigabytes of neural data through MEAnap, oscillation, and spike sorting workflows. Built a Next.js application that converts raw data into processed outputs formatted for roughly 2x faster analysis in Prism. Used Prism for two-way ANOVA graphs. Debugged and set up virtual environments so colleagues can run processing scripts reliably.
NGYL
Built the NGYL website with HTML, CSS, and JavaScript to grow awareness, roughly double volunteer sign ups, and support mission and event communication. Organized a STEM career seminar with four industry professionals and over 200 participants. Taught 300+ students across Python, drone robotics, Lego robotics, and machine learning.
Projects
No Code EDA
- Redesigned the data analysis workflow with AI assisted human computer interaction principles.
- So users can sort, filter, reorder, and visualize datasets through natural language with an AI assistant.
- Full stack work with Next.js, FastAPI, and OpenAPI to connect chatbots to EDA tool calls.
- Integrated the Meridian package to data bind CSV data with visualizations.
- Built isolated, version controlled analysis environments for collaborative EDA similar to Google Docs.
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DeckScan
- Computer vision application that automatically scans and reconstructs poker card orderings, increasing data collection efficiency by about 3x.
- Processed and analyzed 600+ wash shuffle trials to build large scale datasets for probabilistic modeling and statistical analysis.
- Next.js and Python tie image capture to batch inference and structured CSV exports so trial runs stay organized and reproducible.
Signal Normalization
- Web app with React and Node.js that processes fluorescent signal Excel data, turning raw plate and well layouts into a standardized format and cutting manual preprocessing time by about 50%.
- Python pipelines with pandas and openpyxl normalize uploaded datasets for consistent scaling.
- Automated summary statistics and structured tables enable direct import into GraphPad Prism and faster downstream analysis.