My main research interests are in data management. I am interested in developing principled solutions to practical problems that are motivated by novel forms of data or interactions with data. One of my goals is to extend the capabilities of modern data management systems in generic ways and to allow them to support novel functionalities that seem hard at first.
My current research focus centers around scaling probabilistic inference, i.e. finding faster ways for drawing conclusions from large amounts of uncertain and inconsistent data. This problem is one of several key computational bottlenecks for modern applications such as information extraction, knowledge aggregation, question-answering systems, computer vision, and machine intelligence. For a quick overview of my research, please have a glance at the following three recent papers: (1. Approximate lifted inference, 2. Bounding Boolean functions, 3. Linearized Belief Propagation).
My work is generously supported by an NSF CAREER Award and I am looking for motivated students who are excited to work on these problems.
Here is an awesome quote about Leonard J. Savage's approach to research (Source: Michael Hamada and Randy Sitter: "Statistical Research: Some Advice for Beginners", 2004):
- As soon as a problem is stated, start right away to solve it. Use simple examples.
- Keep starting from first principles, explaining again and again what you are trying to do.
- Believe that this problem can be solved and that you will enjoy working it out.
- Don't be hampered by the original problem statement. Try other problems in its neighborhood; maybe there's a better problem than yours.
- Work an hour or so on it frequently.
- Talk about it; explain it to people.
Wolfgang Gatterbauer is an Assistant Professor in the Tepper School of Business at Carnegie Mellon University, and by courtesy in the Computer Science Department of Carnegie Mellon University. He received his PhD in Computer Science with Georg Gottlob from Vienna University of Technology and did a Post-Doc with Dan Suciu at University of Washington. Wolfgang is the recipient of a CAREER award from the National Science Foundation and a "best-of-conference" mention from VLDB 2015. His current work focuses on ways to scale approximate probabilistic inference.