CAREER: Scalable Bayesian learning for multi-source and multi-aspect data

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Data of growing complexity come from multiple sources with multiple aspects. These data present us with unprecedented opportunities to integrate them with predictive models to extract complex relationships among natural and man-made objects.The PI brings together models and techniques encountered in various areas, such as Bayesian statistics, computational science, and systems biology, to develop new methodologies and tools for multi-source and multi-aspect data analysis. The intellectual merit includes (i) new constrained sparse Bayesian models to make interpretable predictions in their application domains, (ii) nonparametric multi-view and multi-way models to reveal unknown complex relationships between different data sources and aspects, and (iii) scalable inference to make advanced Bayesian methods practical data analysis tools.The PI collaborates with domain experts to model online user behavior, facilitate neurologists to elucidate brain functions and help pharmaceutical researchers identify key biomarkers for drug discovery. The PI incorporates the research results into new courses he teaches, organizes workshops, and recruits graduate and undergraduate students to conduct research for this project.For further information see the project web site at the URL: http://www.cs.purdue.edu/~alanqi/projects/learning-multi-source-aspect-data