Cornell University Astrophysics Researcher Conducts Data Mining Workshop

Created: April 10, 2014  |  Last Updated: December 16, 2019  |  Category:   |  Tagged:

WASHINGTON, Pa. (Nov. 7, 2013)—Greg Hallenbeck, an astrophysics researcher at Cornell University, delivered this week to a group of students and faculty at Washington & Jefferson College (W&J) an interactive, hands-on workshop on the role of data mining in the field of astronomy.

Using the Arecibo Legacy Fast ALFA survey and the Sloan Digital Sky Survey, Hallenbeck provided real-world examples of how astronomers use structured query language (SQL) programming and data mining to explore and analyze the vast amount of data produced by astronomical instruments.

Thomas Lombardi, Ph.D., assistant professor of computing and information studies (CIS), who is currently teaching data mining, said, “it’s a great experience for students to see data mining’s contribution to scientific research. Distinguishing stars and quasars is an important astronomical problem, but collecting full spectra to distinguish them is expensive. Students were able to work with real data to see how data mining on inexpensive image data can help astronomers only collect these expensive spectra when it is likely to pay off.”

The presentation was designed for students interested in the application of new technologies to scientific research.

Amanda Holland-Minkley, Ph.D., associate professor and chair of CIS, said, “this was an excellent opportunity to bring together both CIS and physics students with an active astrophysics researcher to learn how these two fields collaborate to deal with the data explosion in the sciences. Students and faculty alike had a great time learning the software Greg uses and trying to reproduce some of the published results we learned about from him.”