Hello!
I'm Matt Zhu, a software engineer focused on building impactful web and mobile applications that solve real-world problems.
Get in touch mz223@duke.edu
I recently graduated from Duke University with an MS degree in Computer Science and Economics. Currently I work as a Software Engineer Intern at Bragr, where I'm responsible for full stack development and AI engineering.
My experience spans full-stack web development, mobile app development, cloud computing, and AI/ML. I have a strong foundation in multiple programming languages and frameworks. I'm actively looking for full-time software engineer roles in the US!
- Python
- JavaScript
- Java
- C/C++/C#
- Swift
- Dart
- SQL
- HTML
- CSS
- Django
- Flask
- Express.js
- React.js
- Vue.js
- Bootstrap
- UIKit
- SwiftUI
- Node.js
- REST APIs
- MongoDB
- MySQL
- SQLite
- PostgreSQL
- Firebase
- Figma
- Docker
- Kubernetes
- CI/CD
- Amazon Web Services (AWS)
- Google Cloud Platform (GCP)
- Microsoft Azure
connects to MediaBeacon, Duke's digital asset system. Users can view, download, and upload files, with changes syncing to the MediaBeacon server. This mobile interface enhances digital asset management efficiency.
enables professors to manage groups and posts, while both professors and students can create posts and answer questions, all authenticated via Keycloak. Built with Node.js, Express.js, Vue.js, and MongoDB, it features E2E testing in Playwright and a CI/CD pipeline in GitLab.
Engineered an iOS directory app in Swift, with a multi-view interface for efficient directory management, integrated robust HTTP/REST services for efficient handling of extensive JSON data and multiple CRUD operations
Implemented Dijkstra’s and A* algorithms and obstacle detection algorithms in C++ to find the shortest path, utilizing data structures such as Priority Queue and HashMap in the C++ STL for optimized graph traversal.
* Performed exploratory data analysis (EDA) and feature engineering to identify and handle imbalanced data issues, * Applied multiple balanced ML algorithms (e.g., Balanced Random Forest) to predict employee attrition rate, ensembled with Voting Classifier, * Produced a robust F1 score of 75%, ranking top 25% in Kaggle Competition