CAREER: III: Modeling the Heterogeneity of Heterogeneity: Algorithms, Theories and Applications

Nowadays, as an intrinsic property of big data, data heterogeneity can be seen in a variety of real world applications, ranging from security to manufacturing, from healthcare to crowdsourcing. Many high-impact data mining applications exhibit the co-existence of multiple types of heterogeneity, such as different classification tasks, different data sources, and different labeling oracles. This project aims to answer two fundamental questions: how to jointly model multiple types of heterogeneity, and how to theoretically characterize the model performance? It is expected to advance the algorithmic and theoretical foundations of state-of-the-art data mining techniques limited to a single type of heterogeneity and the sparse literature on modeling dual heterogeneity. The resulting algorithms and theories will be assimilated into new curriculum development and multiple K-12 outreach activities. It could benefit various real applications where multiple types of heterogeneity co-exist. A close collaboration with industrial partners promises timely and measurable impacts on two application domains, including security and manufacturing.