Shengzhe(Jason) Ji

• 1、InsightUBC(Intelligent Campus Navigation System)
• Developed a full-stack application to query and manage historical UBC course and room data, leveraging TypeScript and following a domain-specific query language defined by EBNF.
• Designed and implemented backend functionality for dataset management (add, remove, list) and query execution, ensuring code quality through unit tests, integration tests, and coverage metrics.
• Built a user-friendly frontend interface for interaction with the backend query engine, enabling efficient data exploration and retrieval.
• 2、Scholar Forum (Scholarly Interaction Platform)
• Developed a full-stack web application following the MVC pattern, using JavaScript for the backend and HTML/CSS for the frontend, enabling authors to publish their works publicly and allowing users to read and like them.
• Designed and maintained a PostgreSQL database to manage user accounts, author profiles, and published content.
• Implemented key features including content submission, user interaction tracking, and real-time content updates to enhance user engagement.
• 3、Wisdom (Course Registration System)
• GitHub Link: github.com/JasonJi831/Wisdom
• Designed a Java application to help students efficiently register and drop courses, streamlining the course guidance process.
• Utilized Java Swing Library to create an engaging interface with a colorful grid layout and GIFs for an improved user experience.
• Implemented a JSON-based local storage system with real-time state updates through a refreshing mechanism.
• 4、Province Prediction of Canadian COVID-19 Case
• Built a supervised learning Classification KNN model to classify Canadian provinces based on daily COVID-19 case trends and recovery rates using a multi-year dataset (Jan 2020–Dec 2022) from the Government of Canada website.
• Cleaned and engineered features from 40 original attributes, selecting key indicators (confirmed case count, recovery percentage) to optimize model performance and interpretability.
• Conducted exploratory data analysis to quantify inter-provincial disparities in infection and recovery patterns, visualizing temporal trends and population-adjusted case growth.
• Evaluated the finalized model and identified the best-performing model for province prediction based on health-report summaries.
• 5、Optimization of Investment Strategies
• GitHub Link: githubhttps://github.com/DanielHuangjiakang/PKU_SMOL_Project
• Maximized Sharpe ratio for three companies using the Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm to optimize risk-adjusted returns.
• Minimized average waiting time in a GI/GI/1 queueing system with a Stochastic Approximation (SA) algorithm for improved efficiency.
• Conducted simulations and statistical analysis to validate algorithm performance and ensure robust, reliable results.

I am an undergraduate student at the University of British Columbia (UBC), majoring in Computer Science and Data Science, with a GPA of 4.0 and ranked in the top 5% of the Faculty of Science. I have been awarded the J. Fred Muir Memorial Scholarship in Science.

I have served as a teaching assistant and computer lab assistant in UBC’s Computer Science Department, helping students master data structures, algorithms, and computer systems fundamentals. I have also participated in several technical projects, including Scholar Forum, InsightUBC (a full-stack query system built with TypeScript) and etc.

I am passionate about machine learning, data visualization, prompt engineering and model construction in risk management, and I hope to continually sharpen my technical skills through real-world projects and contribute meaningful value to the team.