My name is Yoonsik Park. I completed my B.Sc. at the University of Toronto for Bioinformatics and Computational Biology, Computer Science and Immunology.

About Me

I currently reside in Toronto, ON, but my hometown is Winnipeg, MB. I recently finished a computational biology research project at The Vector Institute for Artificial Intelligence.

There are two reasons why I decided to specialize in computational biology:

First, I truly enjoy solving both biological and computer science problems. I find the process of synthesizing two completely different mindsets to produce novel solutions extremely rewarding.

Second, I believe there will be an important industry rising from this field. The collection of huge amounts of biological data is being fueled by the low costs of nucleotide and protein sequencing. Inside all this data are mysteries waiting to be unraveled.


Predicting the Pathogenicity of Copy Number Variations

Summer 2018

Copy Number Variations (CNVs) are large DNA duplications or deletions of 50 base-pairs or more. In this project, a few different machine learning techniques (t-SNE, neural networks, XGBoost trees) were used to understand how certain features of a CNV affect its ability to cause disease. The code repository and Jupyter notebooks are on GitHub.

Particulate Matter Sensor

October 2017

Check out the air quality in my home! This graph displays the amount of particulate matter in the air in units of µg/m³. Particulate matter consists of very small particles suspended in the air, that are linked to premature death, heart problems, and serious health effects. The code for the sensor software, and the interactive graphs are available.

Kindle Weather Display

January 2016

A guide on creating your very own Kindle Weather Station! This project lets you view the current weather conditions of a city directly on your Kindle's e-paper display. Weather data is scraped from Yahoo. There is a video of the build process, as well as a repository for the server-side software.



Keybase: yoonsikp

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