Professor Jennifer Neville, Associate Professor and Miller Family Chair of Computer Science and Statistics at Purdue University, talked about the history and future of artificial intelligence and about machine dialog systems called “Chatbots.” Professor Neville focuses her research on machine learning and data mining.
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. In particular, She focuses on the development and analysis of algorithms for relational domains, including social, information, and communication networks, as well as physical networks and distributed systems.
Her work can be broadly categorized into:
(1) design and implementation of machine learning and data mining techniques,
(2) discovery of, and adjustment for, statistical biases due to networks data characteristics, and
(3) application to real-world tasks.
Selected Publications
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