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Bachelor of Science in Computer Science, University of British Columbia, CA, Sept. 2023- April 2028
Relevant Coursework: Basic Algorithms and Data Structures, Computer Hardware and Operating Systems, Introduction to Computer Systems , Introduction to Human Computer Interaction Methods, Introduction to Relational Databases, Introduction to Software Engineering, Models of Computation , Software Construction

Assistant Aug 2022 – Sep 2022

PC Galore Repair Shop Vancouver, BC

Observed computer repair and recycling processes, including troubleshooting, diagnostics, and disk analysis
Set up new devices and securely wiped data from old devices to protect customer and store information
Applied prior technical knowledge to complete manager-assigned tasks, including PC and monitor setups for store displays

Sales Associate Jul 2021 – Jul 2022

Canadian Tire Vancouver, BC

Trained and onboarded new team members into store operations and customer service
Partnered with store manager to plan and execute floor layouts and seasonal displays
Conducted stock checks and assisted customers with accurate product information
Processed transactions and handled returns while maintaining a positive customer experience

Flight Sergeant Sept 2018 – Dec 2023

59 RCACS Vancouver, BC

Led a group of 20+ high school students biweekly through physical training and aviation activities
Prepared and taught lessons for each grade level, adapting content to varying presentation environments

Go Grocer, Hackathon Project | React Native, Expo, JavaScript, Google Gemini API

Built a mobile app to scan food products with a camera and compare prices at nearby grocery stores
Integrated a product recognition API to identify items from photos and extract brand/product metadata
Used expo and react-native-maps to show nearby store locations with prices on an interactive map
Designed a tab-based UI with a central camera button and live pricing data sorted by proximity

Diabetes Prediction | R, tidyverse, tidymodels, ggplot2, Jupyter

Built a k-NN classifier to predict diabetes likelihood in Pima Indian women from 768 patient records
Cleaned data and split 75/25 train-test using health features including glucose, BMI, age, and blood pressure
Tuned k from 1–100 using 5-fold cross-validation to maximize generalization accuracy
Visualized cross-validation results to select optimal k and illustrate the bias-variance trade-off