ABOUT ME
Summary
Hello, I am Qui, a committed student pursuing a degree in Computer Science at Weber State University. I am set to graduate with my bachelor's degree in May 2024. Currently, I work as a Computer Science tutor and Programmer at the university. My tutoring role has sharpened my analytical and problem-solving skills, while my programming position has provided real-world tech experience, focusing on Windows application maintenance, feature development, and database management.
During my academic journey, I have completed several projects involving web development, parallel computing, and data science. Details of these projects are available in the portfolio section below. Currently, I am collaborating with my team to build a language learning app for mobile platforms, a project that extends beyond the scope of my school curriculum.
Through these experiences and projects, I've developed a well-rounded skill set in software development and data analysis, preparing me for the dynamic field of computer science.
Awards
- 2022/2023 Outstanding Student
- Davis Campus Outstanding Student for Fall Semester 2023
People said about me
"Qui is an outstanding CS student and works as a CS tutor for the School of Computing on the Davis campus. He is always patient and kind when he is working with students, who can sometimes be frustrated. There are a couple of students that hang out in the tutoring room, while they do their homework just in case they have a question. They are very comfortable with him. We will be sad when he graduates and is no longer working for us. He always has a friendly smile and greeting for me when I am on the Davis campus."
WHAT DO I KNOW
FullStack Development
MERN STACK, DJANGO, DOTNET
Parallel computing
CUDA, MPI, KOKKOS, CPU VECTORIZATION
Data Analyst
PANDA, SPREADSHEET, VISUALIZATION, STATISTIC, SKLEARN
Programming Languages
C++, JAVA, C#, PYTHON, JAVASCRIPT
Database
MYSQL, MSSQL, MongoDB
Speed Game
This is a full-stack project that I collaborated on with three other members. The stack that we used for this project is MERN (MongoDB, Express, React, Node.js). The project is a web-based card game that allows two players to play against each other in real-time. The game includes two modes: Traditional and California. The Traditional Speed Game is a fast-paced card game for two players where the goal is to be the first to get rid of all your cards by playing them on center piles based on sequential order, without regard to suit. Players rapidly play cards one higher or one lower than the top cards of the center piles, continuously drawing from their personal decks to maintain a five-card hand, with the game demanding quick reflexes and instant decision-making. California Speed is a variation of the traditional Speed card game, and the objective is similar to the traditional, which is the first one get rid of all cards. But in California Speed, players do not take turns. Instead, both players flip over cards from their piles simultaneously and place them on either of two central piles if the cards match in rank, with no need to match them in sequential order. The game's functionality includes authentication, lobby room where users find a room, waiting room, a top high score board, and chat functionality for players in game and lobby.
Scholarship
Indeed, this project involved collaboration with my team on an existing project, unlike the previous one we worked on. The project is a scholarship website for the School of Computing at Weber State University, designed to allow students to apply for scholarships. It was originally built using .NET. Our responsibility was to transition the project from .NET 2 to .NET 7. In addition, we also introduced new features, updated the database, fixed bugs, and enhanced both the performance and user interface of the application. The application's functionalities include authentication, a scholarship application process for students, and scholarship management and review features for administrators.
Job Portal
This project is a job portal website that I developed using Django. It allows users to search for jobs and apply for them. The system features two fundamental roles: job seekers and employers. Job seekers can search for jobs, apply for them, upload their resumes or create one on the site, and manage their applications. Employers can post jobs, manage job postings, and view applications. The application's functionalities include authentication, job searching, job application, resume management and creation, and job posting management.
Movie Recommendation
The project is a movie recommendation website that I developed using Django with the combination of data science and web development. The user will search and select movie, the system will use selected movie data to analyze and recommend similar movies to the user. The recommendation is based on the user's selected movie's genre, script, title, and year. The application's functionalities include movie searching, movie recommendation, and user's movie history.
NVIDIA Stock Prediction
The stock prediction project is a data science project developed by a friend and me using Python. It predicts future stock prices using historical NVIDIA stock data. In addition to using available stock data, we also employed web scraping to collect NVIDIA's earnings reports information and applied feature engineering to create new features for the model, such as Bollinger Bands, moving average, and RSI. We utilized SVR, Random Forest, XGBoost, and LSTM machine learning models for stock price prediction. The application's functionalities include stock data collection, stock data analysis, and stock price prediction.
Image Processing
In this project, I utilized multiple MPI ranks for image smoothing, leveraging information from neighboring ranks with MPI sends and receives. The image is divided into four strips, for each of the four MPI ranks, with each rank using all available CPU cores for OpenMP and MPI parallelism. The Gaussian Smoothing algorithm is applied, adjusting pixel colors based on nearby pixel values, where closer pixels have more influence. For a detailed explanation of Gaussian Smoothing, see here.
Portable Parallel N-Body With Barnes-Hut
In this project, I leveraged Koko to develop a portable parallel application capable of running the Barnes-Hut algorithm on both CPU and GPU architectures. This approach significantly enhances the efficiency of n-body simulations, which typically present an O(n2) computational complexity. By applying the Barnes-Hut algorithm, the complexity is reduced to O(n log n), dramatically speeding up the process. The fundamental principles of this algorithm are well-documented on the Princeton University course archive, which can be accessed here. For this implementation, the simulation was executed with an N-Body count of 100,000, showcasing the algorithm's capability to efficiently manage large-scale simulations.
CONTACT ME
951-386-6345
quingo2804@gmail.com
1141 E 2500 N Layton, Utah