Michael has a passion for finding strategic insights for businesses, managers, and organizations engaged in competitive dynamics. With a background in corporate litigation and white collar criminal defense, as well as graduate-level education in strategic management, Michael is passionate about combining his previous skill set and experience with machine learning techniques such as boosted trees, support vector machines, and neural networks to generate competitive insights for decision-makers. He is particularly fascinated by technological innovation, media, and finance. Michael has an MS in Management & Organizations from Penn State University and a Juris Doctor from Fordham Law School. He is currently a Data Science Fellow at NYC Data Science Academy, where his recent projects include predicting extreme weather events in a historical climate database, predicting the success of unreleased blockbuster films as a classification problem, and participating in the Higgs Boson Kaggle competition.
NYC Data Science Academy’s mission is to provide accelerated data science training programs that prepare people for employment as data science professionals and to offer continuing education courses for professional development.
