Daniel Neill, director of the Event and Pattern Detection Laboratory at Carnegie Mellon University's Heinz College, examines how over the past decade, the lab developed a variety of new statistical and computational approaches for detection of emerging events and other relevant patterns. To deal with the massive size and variety of real-world data, the lab proposes a fast multi-dimensional subset scan -- a novel approach for accurate and efficient pattern detection.

Neill’s talk focuses on the lab's recent work in scaling up these approaches to deal with data size and complexity. Furthermore, Neill addresses detection of emerging outbreaks of disease using Emergency Department visit records, and prediction of civil unrest using online social network data. Finally, Neill demonstrates that these approaches achieve more accurate, precise, and computationally efficient detection and predictions of real-world events, as compared to the current state-of-the-art methods.

Bio:

Daniel B. Neill is the Dean's Career Development Professor and Associate Professor of Information Systems at Carnegie Mellon University's Heinz College, where he directs the Event and Pattern Detection Laboratory.  He holds courtesy appointments in the Machine Learning Department and Robotics Institute at CMU's School of Computer Science and is an adjunct professor in the University of Pittsburgh’s Department of Biomedical Informatics. He received his M.Phil. from Cambridge University and his M.S. and Ph.D. in Computer Science from CMU.  His research focuses on machine learning and event detection in massive data sets, with applications ranging from medicine and public health to law enforcement and urban analytics. Dr. Neill was the recipient of an NSF CAREER award and an NSF Graduate Research Fellowship. Also, he was named one of the "top 10 artificial intelligence researchers to watch" by IEEE Intelligent Systems.