Full-stack developer and Computer Science student at SDSU with experience building real-time web applications, secure payment systems, and data-driven tools. Skilled in Python, TypeScript, React, and SQL. Passionate about delivering scalable software, clean code, and user-centric solutions.
Overview
2
2
years of professional experience
Work History
Web Developer – Personal Portfolio Website
(Independent Project)
San Diego
05.2025 - Current
Created a personal portfolio site using React, TypeScript, Next.js, and Tailwind CSS to showcase projects and skills in an interactive, professional format.
Engineered dynamic features (animated code blocks, reusable UI components) for an engaging user experience, leveraging Next.js SSR and custom API routes to boost load speed and SEO.
Deployed via Vercel with a custom domain and CI/CD, resolving compatibility and pipeline issues to ensure reliable, hassle-free updates.
Creator & Full-Stack Developer – BidMe
Self Employed Web
03.2024 - Current
Built BidMe, a full-stack real-time auction platform from scratch using Next.js, Tailwind CSS, Prisma ORM, and PostgreSQL, demonstrating end-to-end development capabilities.
Implemented live bidding via WebSockets for instant updates and integrated Stripe for secure payments, with real-time notifications to enhance user engagement and trust.
Optimized performance and responsiveness with a modern, mobile-friendly UI (Tailwind CSS), delivering a seamless user experience under heavy bid traffic.
Undergraduate Researcher – SDSU
San Diego State University
05.2024 - 08.2024
Conducted bioinformatics research in a competitive STEM program, developing the BIGG-U database of genome-scale metabolic models to advance computational biology research.
Developed Python and MATLAB scripts to parse large genomic datasets (FASTA files) and extract protein IDs, automating data ingestion and analysis.
Designed and optimized relational database schemas in MySQL to store complex biological data, ensuring efficient retrieval and scalable expansion of the BIGG-U database.
Research Assistant – (UCSD)
University Of California San Diego
05.2023 - 08.2023
Co-developed a deep learning library for time-series forecasting on 7 years of high-frequency (6-second interval) solar data, leveraging Python with Jupyter notebooks and Google Colab for large-scale processing.
Implemented a Sequence-to-Sequence (Seq2Seq) deep learning model to forecast solar energy output, achieving robust performance on complex time-series data.
Authored comprehensive documentation and developer guides, enabling easy adoption and reproducibility of the forecasting tools by future researchers.