Flexible Statistical Modeling

研究者在各种应用情况下研究统计模型，这些情况需要对标准技术进行创新修改。文档分类在极高维度的特征空间中构建模型，用基因表达阵列进行推理和预测的模型也是如此。物种发生和丰度模型处理大量物种，通常具有许多特征。在每一种设置中，不同的上下文导致研究人员开发特殊的正则化形式，允许人们利用数据中的结构。在这个先进的技术时代，我们面临着分析大量数据的问题。这个研究人员处理的几个例子中的两个是基因表达测量（每个人类样本4万次测量）和在线文档分类（通常网络是来源）。标准的统计工具在这些情况下不能很好地工作。本研究者研究这些工具的创新适应性，旨在克服这些问题带来的挑战。

The investigator studies statistical models in a variety of appliedsituations which require innovative modifications of the standardtechnology. Document classification builds models in extremelyhigh-dimensional feature spaces, as do models for inference andprediction with gene expression arrays. Species occurrence andabundance models deal with large numbers of species, often sharingmany characteristics. In each of these settings, the differentcontexts have led the researcher to develop special forms ofregularization that allow one to exploit the structure in the data.In this advanced technological age, we are faced with analyzing extremely largevolumes of data. Two of the several examples this researcher deals with are geneexpression measurements (40 thousand measurements per human sample), andonline document classification (often the web is the source). Standardstatistical tools do not work well in these situations. This investigatorstudies innovative adaptations of these tools designed to defeat thechallenges these problems pose.