Daniel B. Neill is the Dean's Career Development Professor and Associate Professor of Information Systems at Carnegie Mellon University's H.J. Heinz III College, where he directs the Event and Pattern Detection Laboratory and the Joint Ph.D. Program in Machine Learning and Public Policy. He also holds courtesy appointments in the Machine Learning Department and Robotics Institute at CMU's School of Computer Science, and an adjunct appointment in the University of Pittsburgh's Department of Biomedical Informatics. He received his Ph.D. in Computer Science from CMU in 2006. Before that, he received his B.S.E. from Duke University, M.Phil. from Cambridge University, and M.S. from Carnegie Mellon. Prof. Neill's research focuses on novel statistical and computational methods for discovery of emerging events and other relevant patterns in complex and massive datasets, applied to real-world policy problems ranging from medicine and public health to law enforcement and security. Application areas include disease surveillance (e.g., using electronically available public health data such as hospital visits and medication sales to automatically identify and characterize emerging outbreaks), law enforcement (e.g., detection and prediction of crime patterns using offense reports and 911 calls), health care (e.g., detecting anomalous patterns of care which significantly impact patient outcomes), and urban analytics (e.g., helping city governments to predict and proactively respond to emerging patterns of citizen needs). He has pioneered the use of "fast subset scan" methods to efficiently and accurately detect anomalous patterns in massive, complex datasets, as well as the use of "Bayesian spatial scan" approaches to detect and characterize events (such as disease outbreaks) in space-time data. His research has been supported by the National Science Foundation, MacArthur Foundation, Richard King Mellon Foundation, and many others, and has been published in top journals such as the Journal of the Royal Statistical Society, Journal of Machine Learning Research, Machine Learning Journal, Journal of Computational and Graphical Statistics, Statistics in Medicine, and Big Data, along with top machine learning and data mining conferences such as NIPS, ICML, KDD, AISTATS, and ICDM. He has received best paper recognition from the Journal of Computational and Graphical Statistics and the National Syndromic Surveillance Conference. He currently serves as an advisor to the board of directors for the International Society for Disease Surveillance, and Associate Editor and "AI and Health" Department Editor of IEEE Intelligent Systems. Prof. Neill has served as scientific program chair of the International Society for Disease Surveillance Annual Conference and co-chaired the last two International Conferences on Smart Health. He is the recipient of a National Science Foundation CAREER award and was recently named one of the top ten "researchers to watch" in the artificial intelligence field. Prof. Neill has been actively involved in curriculum development and teaching at the intersection of machine learning and public policy. He is the developer and coordinator of CMU's Joint Ph.D. Program in Machine Learning and Policy, jointly administered by the Machine Learning Department (School of Computer Science) and Heinz College. He has developed an introductory course in "Large Scale Data Analysis for Policy"(90-866) for the MSPPM program, a Ph.D. Research Seminar in Machine Learning and Policy (90-904/10-830), and a series of courses, "Special Topics in Machine Learning and Policy" (90-921/10-831), with topics including "Event and Pattern Detection", "Machine Learning for the Developing World", and "Harnessing the Wisdom of Crowds". He also teaches the core statistics course for the MISM program (95-796, "Statistics for IT Managers").