cv

Basics

Name Seunghyun(Joe) Lee
Label Industrial Engineering Student
Email sehyun.lee@mail.utoronto.ca
Phone (437) 660-9391
Url https://github.com/sehyunlee217

Work

  • 2025.05 - Present

    Toronto, Canada

    Data Analyst Intern
    Kijiji
    Working as a data analyst intern at Kijiji, the largest Canadian online classifieds platform.
  • 2024.05 - 2024.08

    Toronto, Canada

    Undergraduate Research Assistant
    Safety, Equity, and Design (SED) Lab, University of Toronto
    Analyzed usability testing data and collaborated with SickKids Hospital to evaluate machine learning applications and team-based interventions in emergency healthcare settings.
  • 2021.06 - 2023.01

    Icheon, South Korea

    Military Service
    Republic of Korea Special Warfare Command

Education

  • 2020.09 - 2027.05

    Toronto, Canada

    Bachelor of Applied Science
    University of Toronto
    Industrial Engineering

    GPA: 3.7/4.0

    • Machine Learning
    • Operations Research

Awards

  • 2024.05
    MIE Summer Research Award
    University of Toronto
    Awarded $7,500 to support undergraduate research on the application of machine learning in patient safety interfaces and healthcare usability.

Skills

Programming Languages
Python
R
Java
JavaScript
Data Tools & Technologies
PostgreSQL
MongoDB
Apache Spark
Azure DataBricks

Projects

  • 2024.08 - 2024.11
    Modeling and Forecasting Ontario’s Monthly Energy Consumption
    Developed dynamic regression models to forecast monthly energy consumption trends in Ontario, achieving a 2% improvement in predictive accuracy over SARIMA baseline.
    • Analyzed historical energy and weather data to uncover non-linear seasonal trends.
    • Built a scalable pipeline for infrastructure planning and energy optimization.
  • 2024.07 - 2024.09
    Policy-Driven Birth Rate Analysis in South Korea
    Evaluated the impact of maternity policies on South Korea’s fertility rate using multi-linear regression.
    • Outperformed baseline models by 10% in predictive accuracy.
    • Forecasted a 20% fertility improvement by increasing maternity leave to 84.5% by 2030.
  • 2024.05 - 2024.07
    Coffee Bean Multi-Classification
    Trained a neural network using PyTorch and EfficientNet to classify four levels of coffee bean roasts.
    • Achieved 90% test accuracy.
    • Deployed live demo via Hugging Face Spaces for open-source access.