A recent graduate from Northwestern University with a B.S. in Computer Science, Kyle has a strong background in computer engineering and programming concepts. His previous work and academic studies contains a panoply of topics including machine learning, artificial intelligence, computer systems, signal processing, and programming languages. Kyle is well versed and in multiple programming languages and computing environments including C++, C#, Java, Python, R, Lisp, and Matlab. As an aspiring data scientist, he has built a strong foundation in statistical learning and data manipulation methodologies in R and Python. Recent projects of Kyle’s involve scraping StockTwits data, performing exploratory data analysis on census data, creating an anomaly detection tool using Shiny, and participating in the Higgs Boson machine learning Kaggle competition, scoring 26th out of thousands (post competition completion) with his team through the use of stacking and ensembling various models.