Cornell Data Science (CDS) is an undergraduate project team that builds data-driven solutions to a variety of real-world problems. Current initiatives range from devising a scalable mapping system for better natural disaster response to developing a tool that can summarize Amazon reviews for a better user experience. Our team of 80+ students is a great place to meet people with diverse interests, gain experience at the intersection of theory and application, and contribute to greater community initiatives.

Cornell Data Science

Evan Williams

Ryan Kannanaikal

Ryan Ho

Lisa Li

Zhongxuan W.

Skai Nzeuton

Ella Schneyer

Ryan Lee

Koji Kimura

Kenneth Chiem

Amrith Kumaar

Alice T. Um

Arya Patel

Santiago Palacios

Takuma Osaka

Tanish Tyagi

Travis Zhang

Andrew Chang

The Quantitative Finance Team at Cornell Data Science focuses on leveraging advanced data analytics and quantitative methods to solve complex financial problems. They develop models and algorithms to analyze market trends, assess risk, and optimize investment strategies, providing valuable insights that guide real-world financial decision-making. Collaborating closely with other teams, they contribute to innovative solutions that enhance users' understanding of financial markets and improve investment outcomes.

Quantitative Finance Team

Machine Learning Team

Data Engineering Team

Leadership Team

Quantitative Finance Team

About

Team members

Other teams at Cornell Data Science

Machine Learning Team

Data Engineering Team

Leadership Team