Picture

About

Hi! I'm an incoming fourth-year student at the University of Washington, Seattle, studying both Informatics (Software) and Business (Operations Supply Chain). I am the current Director of PR for UW IFC. Formerly I was an analyst at UW Research Compliance

I'm highly experienced in data, business, design, full stack, DB, systems architecture, ML and AI et cetera. I'm also experienced in DJing. Current interests include large data decomp and defense applications.

This summer I am at Amazon Bellevue on Fulfillment Technologies & Robotics

Experience

SDE Intern

Amazon • 2025-Present

Amazon Fulfillment Technologies - Amazon Insights for Manufacturing. Focus on Agentic AI

Research Compliance

UW Finance • 2023 - 2025

Reconciliations

2025

Grants, Awards, Subcontracts and Releases

Systems + Data Analyst

2023 - 2025

I used Tableau, Power BI and Excel to generate visualizations/projections of workload. I also automated various processes with Python and pulled data from a central SQL server to deliver insights.

Consulting

Montlake Consulting Group • 2023 - Present

UW's oldest student-run consultancy

Project Manager

2025

Managed consultants on projects for clients

Consultant

2023 - 2024

Projects for clients

Fast Facts

Technical Skills

Computer Languages

PythonJavaScriptTypeScriptSQLJavaC++BashHTMLRJSX

See Technicals for more

Tools Frameworks etc

GitPandasSeabornAgilescikit-learnReactFastAPINext.JSNode.JSpydanticInfrastructure as Code

Human Languages

English, Vietnamese, Mandarin Chinese, Spanish

SAT

1590

Relevant Coursework

Data Structures & Algorithms
Database Systems
Machine Learning
Project Management
Modeling for Managerial Decision Making
Client Side Development

Contact

The best way to contact me is through my email: mattcng9 (at) uw dot edu

Footer

Github Repo: Link

MATTHEW NGUYEN

Informatics + Business at the University of Washington

DESIGN WORK

UNIVERSITY OF WASHINGTON

INTERFRATERNITY COUNCIL

Developed + promoted coherent IFC brand representing 25 fraternities at the University of Washington.

HUSKY

TAEKWONDO

Designed and developed modern rebrand. Grew social media following by 150%. Directed promotional video and drove membership.

NUTRIDASH

Mobile delivery app design for older adults and their caretakers to manage and order groceries.

TABLE TOGETHER

Interactive website allowing UW students to explore the food around Seattle by small, family-owned restaurants. Mobile-first design paradigm and accessibility compliant.

TECHNICALS

In the process of learning: ADA, Rust, and OCaml

SKILLS

Languages

PythonJavaScriptTypeScriptSQLJavaC++BashHTMLRJSXSCSSLaTeXMarkdownTSX

Tools

GitPandasSeabornPlotly Dashscikit-learnReactFastAPINext.JSNode.JSPyTorchJupyterAgile frameworkQuantConnectMySQLSQLiteHuggingFaceViteGNUTailwindBootstrapRequestspydanticFigmaAWS Lambda

PROJECTS

Sentiment Trading Algorithm

Used BeautifulSoup and HuggingFace to analyze general sentiment of tickers to determine buy/sell strategy. Backtested with 140% returns and front-tested with -4% returns. Built on the QuantConnect platform

PythonTensorFlowNumPyQuantConnectBeautifulSoupHuggingFace

Reddit Query

Query reddit for anything. Fullstack application with custom API to parse pushpull. Deployed on Vercel with render backend.

Next.JSReactRequestsPythonJSXPyTestFastAPI

2D Physics Engine

Physics engine in a 2D space. Support for gravity, collision detection, and more. Built with C++ and OpenGL.

C++OpenGLPhysicsGraphics

Snowflake Figma Plugin

Figma plugin to generate patterns of snowflakes with Perlin Noise. Can be configured for multiple variants or other objects. Features custom UI.

TypeScriptHTMLCSSFigma API

Diabetes Predictor

Researched and implemented diabetes predictor based on biometric factors. Created web-app equivalent for kNN visualization with adjustable parameters

PythonPandasscikit-learnseabornPlotly DashJupyter

Pac Man Machine Learning

Designed + implemented ML concepts in the context of a Pac-Man Game, including BFS, DFS, cost function and A* search. Also explorations in minimax, expectimax, reflex and alpha-beta pruning agents; value iteration, policies, Q-learning and Epsilon-greedy approaches; and perceptron classification and feature design.

PythonMachine LearningNeural NetworksSearch AlgorithmsReinforcement Learning