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

日益复杂的数据来自多个来源，具有多个方面。这些数据为我们提供了前所未有的机会，将其与预测模型相结合，以提取自然和人造对象之间的复杂关系。PI汇集了贝叶斯统计、计算科学和系统生物学等各个领域遇到的模型和技术，以开发用于多源和多方面数据分析的新方法和工具。其智力优势包括（i）新的约束稀疏贝叶斯模型，以在其应用领域中进行可解释的预测，（ii）非参数多视图和多方式模型，以揭示不同数据源和方面之间的未知复杂关系，以及（iii）可扩展的推理，使高级贝叶斯方法成为实用的数据分析工具。PI与领域专家合作，对在线用户行为进行建模，促进神经学家阐明大脑功能，并帮助制药研究人员确定药物发现的关键生物标志物。PI将研究结果融入他教授的新课程，组织研讨会，并招募研究生和本科生为该项目进行研究。欲了解更多信息，请访问该项目的网站：http://www.cs.purdue.edu/~alanqi/projects/learning-multi-source-aspect-data

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