Jingxuan Dou

949-***-****

ad45pz@r.postjobfree.com

EDUCATION

University of California, Irvine Jan 2024

B.S. in Computer Science

GPA: 3.5

TECHNICAL SKILLS

Programming Languages: C/ C++, CSS, Java, JavaScrpt,MIPS, Python, Ruby, SQL Concepts: Agile methodology, Docker, Hashing, HTML, Linux, MVC, Trees Software: AWS, Matlab, Office 2016, Photoshop, R statistic, React, Shell, Typora EXPERIENCE

DSC Notes Provider Sep 2023-Mar 2024

Use MarkDown editor Typora to provide organized, thorough and searchable notes for disabled students. PROJECTS

Texas Hold’em game Jun 2022-Aug 2022

Analyze requirements an design UML class, forming use case and using GoogleTest and created a guide document .

Independently create a Texas Hold’em game supporting user mode and file mode which enables a player to play with several bot players and follows the real game rules.

Use a variety of python library modules for applications such as graphics, GUI, files and network programming. Background image was designed using Photoshop from the original casino table.

Increased intelligent by implementing Fisher-Yates Shuffle and betting based on winning probability. Lewis Carroll revisited Jan 2023-Mar 2023

Use an array-based binary min-heap as the priority queue data structure. Also tried several different kinds of hashing algorithms

Choose breadth-first search method from a list including linear, binary, Fibonacci, exponential, BFS, DFS, etc.

Use Cuckoo hashing to calculate the lowest Lewis Carroll distance between two strings with consideration of time complexity. Then created basic data structure and software design. Fabflix Web application Sep 2023-Dec 2023

Develop a real, web-based commercial web site to sell movies deployed on AWS.

Use HTML/CSS /JavaScript to generate HTML pages, Java Servlet for the micro-services architecture, Tomcat- JDBC to connect to MySQL

Developing an Android App for Fabflix Using Android Studio IDE.

Use image compression and JavaScript asynchronous and delayed loading for performance tuning Research

Deep Learning Project in Medical imaging

Build a rule-based method for automatically extracting abnormality labels from free-text radiology reports with an average F-score of 0.976 (min 0.941, max 1.0).

Use a deep convolutional neural network (CNN) l for multi-organ, multi-disease classification of chest CT volumes

Training on more labels improves performance significantly

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