Yoonsik Park

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.

About Me

I live in Winnipeg, Manitoba. This past summer, I worked at Mutuo Health Solutions on applying Natural Language Processing models to patient transcripts and medical journals.

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 explore the applications of machine learning to healthcare.

Projects

Adobe Cube LUT Image Tool

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 Variations

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 Sensor

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.

Publications

Preparing Medical Students for the Impact of Artificial Intelligence on Healthcare. Canadian Federation of Medical Students AGM 2020. [PDF]

Genome Gerrymandering: Optimal Division of the Genome into Regions with Cancer type Specific Differences in Mutation Rates. Pac Symp Biocomput 2020. [PubMed] [PDF]

Appearances

Back in the Freedom Dimension (Episode 398). Linux Unplugged Podcast. March 2021. [Link]

Contact

PGP Key: 49F4 C2D1 720F BD2E EA37 D2A3 D013 D029 499B 69D7