Kyle comes from a background in molecular and cellular biology with professional work experience using R for a biotech. He implemented visualizations, statistical tests, and unsupervised machine learning algorithms to develop analytical methods for entirely novel pharmacological and internal data. At NYCDSA, Kyle and his team used their abilities to scrape data and photos from Instagram. Using this data, they built a deep learning model to predict photo popularity and an app to implement their model for interactive use. Kyle’s greatest assets are his ability to learn quickly and his excellent interpersonal skills. With his analytical and presentation prowess from biology, Kyle is excellent at communicating complex scientific methods in a palatable and simplified manner to any audience.