Inference and Prediction in a Complex Discovery Process

随着计算机技术的发展，科学和工程研究变得越来越复杂。因此，需要强大的统计技术来利用具有复杂结构的数据。提议的项目将集中在设计和分析涉及复杂统计建模的问题的推理和预测方法，例如，机器学习和数据挖掘。要研究的问题包括建模过程的推理，以解释建模过程中的建模不确定性，以及多类边界分类和半监督学习背景下的预测和推理。该项目的具体目标是由我们的目标应用程序中复杂建模过程的特征所驱动的，重点是(1)开发一种新的推理理论，以及用于比较复杂建模过程的推理程序和计算工具；(2)多类边际分类技术的发展；(3)半监督学习新技术的发展；(4)目标跟踪、癌症基因组分类和文本分类等针对性应用技术的具体发展。对于目标应用，PI将与其他科学家和工程师合作。拟议的教育计划将培养从事统计学研究的研究生。该项目的成功将为基础科学和工程研究，特别是将人的智能与机器的速度相结合的自动机器处理，复杂结构的数据挖掘以及生物医学研究带来巨大的好处。

Abstract With advances of computing technology, scientific and engineering investigation becomes increasingly complex. Powerful statistical techniques therefore are needed to exploit data with complex structure. The proposed project will be centered at design and analysis of inferential and prediction methods for problems involving complex statistical modeling that arise in, for instance, machine learning and data mining. The problems to be investigated include inference about a modeling process to account for modeling uncertainty in a modeling process, as well as prediction and inference in the contexts of multi-class margin classification and semi-supervised learning. The specific aims of the project, motivated by characteristics of complex modeling processes in our targeted applications, are focused on (1) the development of a novel theory of inference, as well as inferential procedures and computational tools, for comparing complex modeling processes; (2) the development of multi-class margin classification techniques; (3) the development of novel techniques for semi-supervised learning; and (4) the specific development of techniques for the targeted applications including object tracking, cancer genomics classification, and text categorization. For the targeted applications, the PI will collaborate with other scientists and engineers. The proposed educational program will train graduate students for research in statistics. Success of this project will bring tremendous benefits to fundamental scientific and engineering research, particularlyin automatic machine processing to combine humans' intelligence with machine's speed, in mining data with complex structure, and in biomedical research.