Jack Cook

• Collaborated with a team of 30+ interns to work on a large scale company project aimed at educating about high level technical disciplines; duties included full stack website development, database management, and machine learning engineering
• Participated and led project team meetings to identify issues, project plan and prioritize tasks to hit deadlines and implement best practices for code testing processes
• Individually built out a model for recommending next courses to users. The model uses other users’ course data and a supervised learning approach to create personalized curriculum recommendations to all users
• Worked with software team to both build out company websites’ frontend features in React.js, setup api endpoints using Django Rest Framework, and persist user accounts in a postgresql2 database

• Selected through rigorous application process to join student run organization focused on App development and IT consulting
• Collaborate with team of 10 student consultants for Ithaca based business to improve IT and business functionality
• Projects include: Developed an IOS ordering app for Cornell affiliated grocery business
• Currently developing central IT and app for a Michigan-based startup, GoPassed

• Identified by Math department professors as qualified peer instructor for Calculus 1 and 2
• Complete grading of weekly course assignments and conduct office hours to provide additional course instruction

• Interned for New Jersey based environment impact foundation
• Researched and wrote educational curriculum for elementary school systems to teach impacts of environmental contaminants on the Barnegat Bay ecosystem
• Led student-based discussion and learning sessions and trained educators on curriculum for implementation

Selected to be a teaching assistant for a 4000-level class centered around Artificial Intelligence and Machine Learning techniques. Duties include hosting office hours, assisting with project debugging, and grading of assignments and exams

Completed a human-level ChessBot implemented with a minimax algorithm improved by various techniques such as alpha-beta pruning, iterative deepening, and Quiescence Search. I also used deterministic evaluation functions based on long-studied chess strategy and game play.