CSE Assistant Professor Alan Ritter has received a Faculty Early Career Development (CAREER) Award
CSE Assistant Professor Alan Ritter has received a Faculty Early Career Development (CAREER) Award from the National Science Foundation (NSF). The CAREER program is a Foundation-wide activity that offers NSF’s most prestigious awards in support of early-career faculty. In conjunction with the award, Alan will receive $500,000 to support his research.
Alan’s five-year project, entitled “Large-Scale Learning for Information Extraction”, will substantially advance the capability of machines to read large document collections and reason about the knowledge contained within them using minimal human effort. This will help people to overcome information overload and make better decisions by analyzing vital information that is locked away in unstructured text. The research will address the machine reading data bottleneck by inventing new methods that can learn effectively from large, noisy datasets using distant supervision. These methods will address the challenge of label noise inherent in distant supervision by performing inference over latent variables during learning, filling in missing information, and resolving ambiguities. The approach combines the benefits of structured learning and neural networks. This will catalyze the rapid development of extractors for many new tasks and domains. Furthermore, the research will push the boundaries of minimal supervision for Information Extraction by exploring new applications that demonstrate the generality of the approach, including entity, relation and event extraction, time normalization and learning to extract a real-time feed of cyber-threat intelligence using distant supervision from the National Vulnerability Database (NVD). This research effort will support the rapid development of information systems for a broad range of new tasks and domains using minimal human effort.
Alan joined the department of Computer Science and Engineering in 2014. Prior to joining OSU, he was a postdoctoral fellow in the Machine Learning Department at Carnegie Mellon University. He received his Ph.D. in Computer Science from the University of Washington.