Zhendong Lu

Master of Computer Science @ NCSU

Talent

Raleigh, RaleighMember since September 1, 2023

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About

I'm Zhendong, a Master student of Computer Science at NC State University. Now I'm actively seeking an full-time opportunity as a software engineer.

Here are my technical skills:

Languages: Java, Python, C++, C, JavaScript, TypeScript, SQL

Frameworks/Libraries: Spring, Spring MVC, Spring Boot, Spring Cloud, FastAPI,Node.js, Express.js,Django,React,

Vue.js, MyBatis, Hibernate, PyTorch, Scrapy, Scikit-learn, Numpy, Matplotlib, Pandas

Tool/Database: MySQL, MongoDB, Redis, Docker, Azure,AWS, Git, Maven, RabbitMQ, ElasticSearch, Jenkins, Celery,

FastDFS, Tkinter, Swagger, Kafka,

Knowledge : OOD/OOP, Design Patterns(MVC, Factory), Machine Learning/Deep Learning

Experience

Sports Excitement

May. 2023 – Aug. 2023

Software Development Engineer Intern New York, NY

• Implemented RESTful API endpoints using Node.js and Express.js, integrating MongoDB as database to enable athlete blog publication, retrieval, and dynamic user interactions, all while ensuring efficient communication flows.

• Developed front-end using React and applied Redux for state management, enabling instantaneous updates for user interactions like likes and comments.

• Implemented a robust CI/CD pipeline with Jenkins and Docker, ensuring reliable integration, testing, and deployment for a consistently stable and updated platform.

Dongguan University of Technology

Mar. 2021 – Nov. 2021

Research Assistant

Dongguan, China

• Developed an innovative RGB-D video dataset, showcasing real-life gesture-based human-computer interaction scenarios to empower the deaf and mute community.

• Engineered streamlined data preprocessing pipelines in Python, incorporating key libraries like numpy, OpenCV, and pandas. This enabled effective handling of extensive 200GB video files and seamless integration of data from Intel

RealSense cameras, significantly expanding dataset sourcing capabilities and bolstering subsequent analyses’ robustness.

• Utilized PyTorch to build and optimize deep learning models for classification, achieving a 70% accuracy rate, thus confirming the dataset’s effectiveness and potential.