Our team is made up of 92 members and works on many areas of machine learning and data science. As a result, we have a total of four subteams: Data Science, Machine Learning Engineering, Data Engineering, and Quantitative Finance, each with their own primary area of focus.
Cornell Data Science is more than just a project team – we also teach courses, lead educational initiatives, and invest in the exploration of new frontiers. For this reason, we have an executive board which manages our general operations and several subteams which lead our technical direction. Our focus is on building a strong and diverse data science community for undergraduates. We work closely with university administration, professors, and companies to prepare students for the information age.
We build infrastructure and systems that enable fast, efficient, and practical deployment of machine learning models. This spans exploring hardware optimizations, large scale big data tools, MLOps frameworks, and production level ML environments.
Our focus is on the exploration and application of data engineering. Our members have worked on projects ranging from the creation and maintenance of CDS' own compute server cluster to automated profiling of geographic information systems. Current team objectives include the expansion of CDS infrastructure and the development of a distributed game server.
We work at the intersection of machine learning research and practical engineering to create systems that can solve problems that humans cannot, whether that is due to scalability or accuracy concerns. Our work ranges from natural language processing to computer vision and reinforcement learning. Examples of current efforts include automatic question answering, Poker / Wordle / Smash Bros playing bots, and Arxiv PDF math formula searching. We are proud of our robust educational series and our course, INFO 1998: Intro to Data Science. To keep up with the latest research we also hold biweekly paper reading groups and publish paper summaries on our blog.
Our goal is to explore the intersection of data science, statistics, and finance. More specifically, many of our projects involve using statistical and algorithmic models to analyze financial markets and securities. Our members have the freedom to explore any topic within quantitative finance, allowing for a diverse portfolio of projects. Previous projects range from developing a cloud-based trading bot to using weather data to predict orange juice futures prices.