Professor of Computer Science
Professor of Statistics
Joined department: Fall 2006
Professor Neville's research focuses on data mining and machine learning techniques for relational data. In relational domains such as social network analysis, citation analysis, epidemiology, fraud detection, and web analytics, there is often limited information about any one entity in isolation, instead it is the connections among entities that are of crucial importance to pattern discovery. Relational data mining techniques move beyond the conventional analysis of entities in isolation to analyze networks of interconnected entities, exploiting the connections among entities to improve both descriptive and predictive models. Professor Neville's research interests lie in the development and analysis of relational learning algorithms and the application of those algorithms to real-world tasks.
H. Eldardiry and J. Neville, "Across-Model Collective Ensemble Classification", Proceedings of the 25th Conference on Artificial Intelligence (AAAI), 2011
A. Kuwadekar and J. Neville, "Relational Active Learning for Joint Collective Classification Models", Proceedings of the 28th International Conference on Machine Learning (ICML), 2011
R. Xiang and J. Neville, "Relational Learning with One Network: An Asymptotic Analysis", Proceedings of the 14th International Conference on Artificial Intelligence and Statistics (AISTAT), 2011