Weng-Keen Wong is an associate professor of computer science at Oregon State University. He received his Ph.D. (2004) and M.S. (2001) in computer science at Carnegie Mellon University, and his B.Sc. (1997) from the University of British Columbia. His research areas are in data mining and machine learning, with specific interests in anomaly detection, surveillance algorithms, and mining large scale datasets. His Ph.D. thesis was entitled "Data Mining Algorithms for the Early Detection of Disease Outbreaks" and he is involved in the field of disease outbreak surveillance. Research Areas Artificial intelligence, machine learning, data mining, disease outbreak surveillance Research Description Weng-Keen Wong’s research interests in data mining lie primarily in the area of anomaly detection. While much of data mining is currently concerned with discovering patterns in the data, there is also a growing interest in finding anomalies. These anomalies play a significant role in scientific discovery and also in surveillance systems. Surveillance systems have traditionally played an important role in domains such as fraud detection and computer security. An emerging field for the application of surveillance algorithms is syndromic surveillance, which has the goal of detecting disease outbreaks as early as possible by monitoring pre-diagnosis health-care data. Present challenges for anomaly detection algorithms include detecting anomalies in spatial and spatio-temporal domains, finding meaningful anomalies, and dealing with massive data sets. Dr. Wong is also interested in Bayesian network structure learning, hierarchical Bayesian approaches and clustering.