
Full-Stack AI App
Prelegal
AI legal document generator with authenticated users, chat-driven field collection, manual fill, live Markdown preview, saved drafts, and PDF export.
Full-Stack Software Developer
Entry-level software engineer building database-backed web apps, AI document workflows, and practical ML systems.
Full Stack SWE
Focus
UC San Diego
Education
ML & Neural Computation
Specialization

SWE / AI Workflows
FastAPI, Next.js, data systems, testing, and practical AI products.
Project Highlights
Four focused projects that show full-stack engineering, AI workflow design, testing, deployment, applied machine learning, and signal analysis.

Full-Stack AI App
AI legal document generator with authenticated users, chat-driven field collection, manual fill, live Markdown preview, saved drafts, and PDF export.

Local-First AI Tool
PDF intelligence pipeline that parses documents with Docling, reviews layout items, corrects tables, summarizes visuals, and exports JSON/Markdown/Excel outputs.

ML Research Simulator
Headless neuroevolution trainer and Pygame demo for evolving a perceptron policy, with reproducible experiments and PRML-style analysis.

Signal ML Analysis
Real public EEG workflow using raw signal inspection, 1-40 Hz bandpass filtering, Welch PSD, spectrograms, window features, model evaluation, anomaly detection, and saved artifacts.
Engineering Approach
I connect a cognitive science and machine learning background with practical full-stack software work.
I studied Cognitive Science at UC San Diego with a specialization in Machine Learning and Neural Computation, plus a Computer Science minor. That background makes me think a lot about how people reason, how systems learn, and how interfaces can make complicated work feel clearer.
In practice, I use that foundation to build software: full-stack apps, database-backed workflows, document intelligence tools, AI-assisted interfaces, and evaluation-heavy projects where the output needs to be understandable and useful.
Each area is a way I turn background knowledge into working software: full-stack systems, structured data flows, applied AI, and careful technical evaluation.
Tech Stack
A practical stack for full-stack web apps, AI document workflows, data pipelines, testing, and deployment.
Experience
Recent engineering work, technical ownership, and the academic foundation behind the projects.
Millenia Ventures
Software Engineering / Data Automation
Built the first structured technical system for an AI fundraising intelligence platform, spanning PostgreSQL schema design, AI/OCR extraction, validation workflows, dashboard flows, and stakeholder review.
Sep 2021 - Mar 2026
UC San Diego
Specialization: Machine Learning & Neural Computation
Sep 2021 - Mar 2026
UC San Diego
Machine Learning, Data Science in Practice, Neural Signal Processing, Probability & Statistics, Linear Algebra, Data Structures & Algorithms, and Business Analytics.
Contact
Open to entry-level software engineering roles, internships, and teams building data-heavy or AI-assisted products.
Location
San Diego, California / Open to relocation