I am primarily interested in scalable probabilistic inference, i.e. drawing conclusions from large amounts of uncertain and inconsistent data. This problem is a key computational bottleneck for modern applications such as information extraction, knowledge aggregation, question-answering systems, computer vision, and machine intelligence. Please read one of the following three recent articles to get an idea of my research: (1. Approximate lifted inference, 2. Bounding Boolean functions, 3. Linearized Belief Propagation). And here is a recent CACM 2015 article by Stuart Russel that gives the basic arguments for combining methods from databasess (first-order logic plus scalability) with those from machine learning (statistical reasoning plus learning).
Wolfgang Gatterbauer is an Assistant Professor in the Tepper School of Business, and by courtesy in the Computer Science Department of Carnegie Mellon University. He received a Dipl.-Ing. degree from Graz University of Technology (Mechanical Engineering), two M.Sc. degrees from Massachusetts Institute of Technology (Electrical Engineering & Computer Science; and Technology & Policy), and his PhD in Computer Science from Vienna University of Technology (Database and Artificial Intelligence Group). Prior to joining CMU in 2011, he was a Post-Doc at University of Washington (Database Research Group). He also won a Bronze medal at the International Physics Olympiad, worked in the steam turbine development department of ABB Alstom Power, and in the German office of McKinsey & Company.