John W.S. Lee

a data science enthusiast
with a strong background in engineering,
particularly in polymer processing

About Me

I have always had a passion for working with data, even during my previous career in engineering.
With 14 years of experience in polymer processing, I have gained invaluable expertise in the field of extrusion,
injection molding, foaming, and compounding processes, which serves as a strong foundation of
domain knowledge. (Please refer to Curriculum Vitae for full details)
However, my true calling emerged when I delved into the world of Data Science from 2021 to 2022.
Exploring the vast realm of data analysis and predictive modeling, I discovered a profound fascination for the field.
Ever since then, I have become an ardent advocate for Data Science, leveraging its power to unlock fresh perspectives within my previous research domain.
Join me on my homepage as I explore the exciting intersection of data and engineering.

extrucal

Python library for throughput calculation in single screw extrusion

Dashboard for Extrusion

Dashboard for throughput estimation in cables, tubes, rods, and sheets extrusion processes

Machine Learning Using Extrusion Data

Study on machine learning using data generated by analytical solution in extrusion

Defect Classification in Injection Molding

Study on defect classification by supervised learning, Mahalanobis distance, and variational autoencoder

Analysis on Fine Dust and Weather in Seoul, Korea

Part 1: Effect of weather on fine dust concentration
Part 2: Prediction of air quality using 1-D CNN

Collection of Data Science Libraries

Personal collection of useful Python libraries for data science

Gr. Project @ UBC-MDS
bccovideda

Package to generate the summary statistics as well as plots for the COVID19 cases in British Columbia, Canada

Gr. Project @ UBC-MDS
Bee Count Comparison

A data analysis (inferential) project for the number of bees across different types of bee sites

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