Java, Python, Mysql, Spring, Django, Hadoop, Git

FENG CHEN

206-***-**** ****.*****@*****.***.*** Seattle, WA Github Linkedin

EDUCATION

Northeastern University Seattle, WA

Master of Science in Computer Science Sep. 2019 - May. 2021 Nanjing University of Posts and Telecommunications Nanjing, China Bachelor of Science in Computer Science Sep. 2015 - Jun. 2019 COURSEWORK

Data Structure, Operating System, Web Development, Computer Network, Database System, Infor- mation Retrieval, Introduction to Software Engineering, Big Data Analytics, Programming Design Paradigm

TECHNICAL EXPERTISE

Languages: Java, Python, SQL, HTML/CSS, TypeScript, JavaScript Framworks: Spring, Django, Bootstrap, Angular, Thymeleaf, Hibernate, Hadoop Database/Tools: MySQL, PostgreSQL, Gradle, Maven, Tomcat, Git PROJECTS

Django based Social Network Web App Oct. 2019 - Dec. 2019

Used Python to build an Instgram-like website that enables users to post and share pictures, register/login, edit pro les, follow others, add like and comments.

Used HTML/CSS, Bootstrap and Django template language on front end. Used Ajax to add and update like and comments on the posts.

Implement MTV pattern to design the structure. Developed back-end with Django, and used SQlite3 for database. Used Master-detail interface to display master collection of pictures and details.

Used cloud application Heroku for website deployment. https://stormy-journey-28462.herokuapp.com/

Spring based Blog Web App Mar. 2019 - May. 2019

Designed and developed a full stack blog website with Spring Boot. Added login, post blogs, comments, tags, classi cation modules.

Implemented Spring MVC framework. Used Thymeleaf to build front-end.

Used Spring Boot to build back-end and implemented RESTful APIs.

Applied Hibernate for Object Relational Mapping and connect with MySQL database. Hadoop based Recommender System Jun. 2019 - Aug. 2019

Implemented Recommender System based on movie data from movielens.org.

Implemented Item Collaborative Filtering algorithm based on Hadoop.

Connected multiple Mapper by ChainMapper, implemented matrix multiplication and calculated recommendation matrix based on rating matrix and co-occurrence matrix.

Used four MapReduce Jobs in the entire work

ow, whole system based on MapReduce.

Accumulated recommendation scores to get recommendation list.

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