Welcome to Kai's Page

About Me

Kaicheng (Kai) Chu

As an engineering student specializing in Computer Science and Statistics, I am currently looking for Internship opportunities for the summer 2025. With a keen interest in both Full-Stack Development and Data Analysis, I am eager to immerse myself in real-life projects that challenge me and expand my learning.

Contact Me

email: chukaicheng1@gmail.com | cbf6ah@virginia.edu

phone: 434-2842773

University of Virginia

Major: Bachelor of Science Computer Science & Bachelor of Arts Statistics

Honors: Dean's List for 7 consecutive semesters

Relevant Course Work:

Artificial Intelligence, Database, Advanced Software Development, Machine Learning, Statistical Machine Learning, Cyber Security, Data Structure and Algorithm, Computer Systems and Organization, Software Development Essential, Intro to Regression Analysis, Data Analysis with Python, From Data to Knowledge

Future Plan Master of Engineering Program, University of California, Los Angeles.

Programming Languages:

Java, Python, R, HTML/CSS/JavaScript, SQL, C

Technology:

Spring Boot, Django, MySQL, MsSql, Git, Maven, Vue, Redis, Hibernate, Linux, RestfulAPI, Kafka, PyTorch

Language:

Chinese (native), English

Research on Project Based Learning(SPSS) UVA Undergraduate Research Asistant 2024.2 - Present

Project Description:

This is the project's second phase, extended from previous research titled "Creating Effective Project-Based Courses: Personal Relevance and Its Relation to Successful Group Work". This research mainly focus on two questions: 1. Analyze clustering effects of student-level and group-level motivations, efforts, and academic performance, applying random, fixed, mixed effects for combination of potential predictors; 2.Analyze the influence of various instructional modes on student outcomes such as motivations, efforts, and academic performances.

Collected, cleaned, and organized multi-source survey data in Excel, ensuring accuracy for statistical analysis.

Conducted exploratory data analysis in R, generating scatter plots, box plots, and histograms to visualize trends in student motivations, efforts, and academic performance.

Applied post-hoc tests in SPSS to identify significant differences in student motivation and group performance across various instructional modes.

Utilized SPSS feature selection techniques to build predictive models, incorporating random effects to enhance model performance.

Analyzed clustering effects of student- and group-level predictors using fixed, random, and mixed-effects models to assess their impact on academic performance.

Internship

Full-Stack Software Developing intern, PushKang Biotechnology (Java-Spring Boot) 2024.5 - 2024.8

Developed a secure user login API using Spring Boot, employing JWT for authentication and Redis for SMS- based session verification, enhancing security and user experience.

Contributed to a company-wide event-sharing service integrating with MyBatis to facilitate data management.

Implemented front-end functionalities using Vue.js, enhanced with Vite for efficient builds, and used Element- Plus with Pinia for responsive design and state management, improving application usability and speed.

Software Engineer Intern, Eth Tech, Newark, CA 2024.1 - 2024.4

Implemented a coupon template service using Java Spring Boot for generating coupon codes asynchronously and caching generated codes in Redis for quick retrieval.

Integrated Kafka to automate the removal of expired coupons, enhancing system efficiency.

Designed REST APIs for coupon management, specifically for retrieving coupons based on userId.

Developed algorithms to compute total prices considering various types of coupons, optimizing calculation.

Junior Java Engineer at Hangzhou Risen Information Technology Co (Java-Spring Boot) 2023.6 - 2023.7

During my internship at Risen Technology as a Junior Java Engineer, I engaged in a collaborative effort with project managers and other engineers to create an Online Bulletin Web Application. This project, developed using Spring Boot, aimed to enhance internal communication by introducing dynamic server-side functionality. I also worked closely with the UI team to craft and implement interactive user interfaces, focusing on responsive design and accessibility to ensure a seamless staff experience across various devices. In my technical contributions, I was responsible for creating a UML diagram that defined the database schemas, which facilitated the development of a MySQL database system. To guarantee the reliability and efficiency of our application, I performed both unit and integration testing using JUnit and Mockito frameworks, as well as API testing with Postman, ensuring that our code coverage exceeded 80%. Additionally, I played a key role in the requirement elicitation process by engaging with clients to gather essential requirements, thereby aligning our development efforts with client needs effectively.

Part-Time Job @ UVA

Teacher Assistant of Software Development Essential (Java) 2024.2 - 2024.5

Engineer School Tutor 2024.2 - 2024.5

Teacher Assistant of Computer System and Organization 1 (Linux, C) 2023.1 - 2023.5

Teacher Assistant of Introduction to Engineering 2022.8-2022.12

“Charlottesville Travel Planner” Group Project-Test Manager (Python-Django) 2023.9-2023.12

The "Charlottesville Travel Planner" is a specialized project designed to guide first-time visitors to Charlottesville by offering a comprehensive travel planning tool. This application serves two primary functions: it allows users to submit and read travel reviews, and it employs a sophisticated algorithm to generate personalized travel plans based on available time slots and preferred activities. During the development of this project, I integrated the Google Login API to manage user authentication and embedded Google Maps to enhance location-based services, significantly improving the application's functionality. I developed a Submit-Review feature using the MVC architecture, enabling users to share their travel experiences seamlessly. To ensure a tailored experience for different types of users, I designed separate interfaces for regular users and administrators, which not only improved user experience and management efficiency but also simplified future maintenance efforts. Comprehensive testing was conducted—including unit, system, and integration tests—to guarantee the application's reliability and performance. Additionally, I utilized Selenium for GUI testing to further enhance the application's robustness.

“Graduate Program Recommendation System” (Python) 2023.9-2023.12

The primary aim of this project is the creation of a recommendation system designed to provide prediction to graduate program admission decisions, thereby narrowing the information gap and aiding administrative decision-making. The system will leverage a multi-criteria machine learning algorithm to evaluate and match applicants’ profiles with program prerequisites. The expected outcome is an enhancement of the application process for prospective students, providing a streamlined and targeted approach to navigating the complexities of graduate program admissions. The anticipated benefits of such a system include time and resource efficiency for the applicants and an improved mechanism for programs to identify and attract candidates who fulfill their specific criteria.

“CyberChat: Bangladesh Bank Robbery of 2016” 2024.2

The Bangladesh Bank Heist of 2016 was one of the most audacious cybercrimes in the history of banking, involving the theft of $81 million from the central bank of Bangladesh, which causes a significant impact on the country’s economy and banking reputation. This video expands more on the robbery and technology details.

“Global Life Expectancy in 2015 Analysis” (R) 2023.9-2023.12

Conducted comprehensive statistical analysis on global health and economic data from 179 countries using R, employing multiple regression models (OLS, ridge, lasso) and tree-based methods (decision trees, random forests) to identify key predictors of life expectancy.

Performed thorough exploratory data analysis revealing significant correlations and potential multicollinearity issues, strategically reducing predictors to enhance model performance.

Implemented classification techniques including logistic regression and recursive binary splitting trees to predict country development status, achieving a 3.3% error rate with random forest models.

Evaluated model performance through test MSE comparison, confusion matrices, and error rate analysis, determining OLS regression (for prediction) and random forests (for classification) as optimal approaches.

Applied statistical findings to develop actionable insights, identifying that education levels (>11.7 years of schooling) and child mortality rates (<5.05 deaths per 1000) were the most significant predictors of national development status.

“Factors Influencing Per Capita Income” (SAS) 2022.9-2022.12

Description:

The "Factors Influencing Per Capita Income" research project was initiated to explore the key elements affecting individual earnings, emphasizing wage levels which significantly influence living conditions and quality of life. Through extensive data analysis, this research aimed to identify, understand, and predict the factors contributing to per capita income disparities, using a combination of quantitative and qualitative methods. Our approach involved a thorough exploratory data analysis (EDA), where we scrutinized relationships and interactions between variables, followed by a meticulous multiple linear regression analysis. This enabled us to understand the impact of various demographic and socioeconomic variables on per capita income. Specifically, we investigated the effects of work tenure, educational attainment, and geographical differences. The study was particularly insightful during the challenging times of the COVID-19 pandemic, highlighting the exacerbated economic strains affecting millions. Ultimately, the project not only provided a detailed statistical model predicting per capita income but also offered crucial insights that could inform policies aimed at mitigating income disparities.

Technology:

Multiple Linear Regression Analysis: Leveraged advanced regression techniques to model per capita income as influenced by demographic and socioeconomic variables, using SAS for robust statistical analysis and model validation.

Exploratory Data Analysis (EDA) and Multicollinearity Assessment: Utilized statistical software to conduct EDA, revealing crucial variable interactions and relationships. Assessed and mitigated multicollinearity using Variable Inflation Factors (VIF), ensuring a high degree of reliability in predictive modeling.

Variable Selection and Model Optimization: Conducted stepwise regression to pinpoint relevant variables, refining model accuracy and efficiency. This included the strategic addition and removal of variables based on their statistical significance and impact on model performance.

Interaction Testing and Model Adjustments: Systematically tested and integrated interactions between quantitative and qualitative variables using nested F-tests, refining the model structure to accurately reflect complex real-world influences on income levels.

Regression Diagnostics and Data Transformation: Employed a variety of data transformations, including the Box-Cox method, to satisfy normality and homoscedasticity assumptions. Utilized comprehensive diagnostic tests to validate model assumptions and enhance the predictive validity and accuracy of the final regression model.

Club

Chinese Student & Scholar Society, Deputy Chair of Publicity & Communication 2024.4-Present

What we do: Build and maintain our club website; use canva to design posters for our events.

Club

XiaCunTuCaoJun 2022.10-2023.12

What we do: Every week, we collect everyone's complaints about life or unusual stories and post them on our public WeChat account. Additionally, every year we hold an offline Roasting Convention, providing students with debate topics for discussion.

Others

Tennis

Climbing

League of Legends & TFT