Our history

In fall 2016, a group of faculty and staff members on campus came together to propose a new Transdisciplinary Area of Excellence (TAE) in Data Science. Establishment of the Data Science Working Group was one result of discussions. The working group moved forward with Data Salons, speakers, seed grant projects and collaborative discussions, while developing a robust proposal to become Binghamton University's sixth TAE. 

Faculty and staff members from almost every school and many units of the campus were involved with the Working Group, including volunteers who continue to serve on a Steering Committee that identifies gaps and opportunities on campus.

In July 2018, the Data Science Working Group was approved to become a TAE, and in fall 2018, the Data Science Initiative, which was established as one of Binghamton University's four Road Map Renewal University Initiatives in 2017, merged with the Data Science TAE. The merged effort seeks to recruit faculty and build facilities to support this evolving area, thus enhancing research and education offerings across the campus.

Data science is not an isolated group. The greater data science community encompasses and intersects with many departments, schools, research centers and institutes.  


Binghamton University is a recognized leader in data science and its integration across all scholarly disciplines, professions and sectors. 


The Data Science Transdisciplinary Area of Excellence serves as a central hub for data-centered research. We are a collaborative group of data scientists from across campus who are at the leading edge of the data science revolution within their own disciplines. We seek to foster collaboration on identification of significant problems, bridge between theory and applications, advance techniques to gather and interpret data, and address the urgent problems facing society.

Binghamton University will:

  • Advance the frontiers of data science by fostering collaborative and innovative research;
  • Provide transdisciplinary learning opportunities in data science at all levels;
  • Engage with local and global partners to develop solutions for real-world problems;
  • Establish a progressive and scalable infrastructure to support the dynamic needs of all data science activities.