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PROJECTS
Subteams
Our Subteams
Cornell Data Science (CDS) is comprised of 92 dedicated members who specialize in various domains of data science and machine learning, organized into four focused subteams: Data Science, Machine Learning Engineering, Data Engineering, and Quantitative Finance. Each subteam has a distinct area of focus, contributing uniquely to our overarching goal of advancing the field of data science.
Machine Learning Engineering (MLE)
The MLE subteam is focused on building robust infrastructure and systems that enable the efficient and effective deployment of machine learning models. This includes enhancing hardware performance, integrating large-scale data tools, and refining MLOps frameworks to support advanced ML applications in real-world settings.
Data Engineering (DE)
Our Data Engineering subteam is dedicated to advancing the field of data engineering. The team works on foundational projects that involve the architecture and maintenance of scalable data systems, enhancing the computational infrastructure that supports our data-driven initiatives.
Data Science (DS)
The Data Science subteam sits at the nexus of machine learning theory and practical application, developing systems designed to tackle complex problems beyond human scalability and precision. This subteam is also committed to educational outreach, offering courses and hosting regular scholarly discussions to keep pace with cutting-edge research.
Quantitative Finance (QF)
Focusing on the convergence of data science, statistics, and finance, the Quantitative Finance subteam applies statistical and algorithmic approaches to analyze financial markets. The subteam encourages a broad exploration of quantitative finance, supporting a wide array of studies that contribute to innovative financial solutions.