Wolfgang Gatterbauer
Research Interests
- Data management
- Information management
- Developing scalable approaches to perform inference over uncertain and networked data
Education
- PhD in Computer Science, Technical University of Vienna – Austria
- MS in Electrical Engineering and Computer Science, Massachusetts Institute of Technology
- MS in Mechanical Engineering, Technical University of Graz – Austria
Biography
Wolfgang Gatterbauer is an associate professor in the Khoury College of Computer Sciences at Northeastern University. Prior to joining Northeastern, he was a postdoctoral fellow in the database group at the University of Washington and an assistant professor in the Tepper School of Business at Carnegie Mellon University.
Gatterbauer is working on the theory of scalable data management. One of his goals is to extend the capabilities of modern data management systems in generic ways and to allow them to support seemingly difficult novel functionalities. He is a recipient of the NSF Career Award, a best paper award from IEDBT 2021 and “best-of-conference” mentions from VLDB 2015, SIGMOD 2017, and WALCOM 2017.
Research Interests
- Data management
- Information management
- Developing scalable approaches to perform inference over uncertain and networked data
Education
- PhD in Computer Science, Technical University of Vienna – Austria
- MS in Electrical Engineering and Computer Science, Massachusetts Institute of Technology
- MS in Mechanical Engineering, Technical University of Graz – Austria
Biography
Wolfgang Gatterbauer is an associate professor in the Khoury College of Computer Sciences at Northeastern University. Prior to joining Northeastern, he was a postdoctoral fellow in the database group at the University of Washington and an assistant professor in the Tepper School of Business at Carnegie Mellon University.
Gatterbauer is working on the theory of scalable data management. One of his goals is to extend the capabilities of modern data management systems in generic ways and to allow them to support seemingly difficult novel functionalities. He is a recipient of the NSF Career Award, a best paper award from IEDBT 2021 and “best-of-conference” mentions from VLDB 2015, SIGMOD 2017, and WALCOM 2017.