My name is Yoonsik Park. I completed my B.Sc. at the University of Toronto for Bioinformatics & Computational Biology and Computer Science. I am currently studying Medicine at the University of Manitoba.
I live in Winnipeg, Manitoba. This past summer, I worked on a cancer machine learning project at the Vector Institute in Toronto.
I decided to specialize in computational biology because I truly enjoy solving both biological and computer science problems. The process of synthesizing two different mindsets to produce novel solutions is extremely rewarding to me. Now, I am excited to learn more about the applications of machine learning to healthcare.
Adobe Cube LUT Image ToolDecember 2019
Cube LUTs are powerful filters used to alter the look and feel of images, but generally
require the use of proprietary software such as Adobe Photoshop. pycubelut was created to be the first free and easy
to use command-line tool for applying Cube LUTs to images. Download from PyPI or GitHub.
Predicting the Pathogenicity of Copy Number VariationsSummer 2018
In this project, multiple machine learning techniques (t-SNE, neural networks, XGBoost
trees) are used to understand how certain features of large DNA duplications or deletions can
predict disease outcome. The code repository and Jupyter notebooks are on GitHub.
Particulate Matter SensorOctober 2017
Check out the current air quality in my home! This project measures the level of particulate matter, i.e. very small particles suspended in the air, which have been linked to premature death, heart disease, and other serious health effects. The code for the sensors and the live graph is available.
PublicationsGenome Gerrymandering: Optimal Division of the Genome into Regions with Cancer type Specific Differences in Mutation Rates. Young A, Chmura J, Park Y, Morris Q, Atwal G. Pac Symp Biocomput 2020. [PubMed] [PDF]
PGP Key: 49F4 C2D1 720F BD2E EA37 D2A3 D013 D029 499B 69D7