Workshop on Frontiers of Statistics: Nonparametric Modeling of Complex Data

Prop ID：dms-0531839 PI：FAN，建清研究所：普林斯顿大学标题：统计前沿研讨会：复杂数据的非参数建模这是一个构思极其精良的Worshop，它将汇集一些动态和充满活力的统计学前沿。这项提议的目的是组织一次关于涉及大型和复杂数据的统计前沿的研讨会。会议将聚焦于不同学科领域的数据分析非参数技术，如计算生物学、金融计量经济学、机器学习和工业工程。它还将包括有关统计理论和方法的各种主题。这次会议的一个突出特点是，它讨论了科学的各个前沿领域出现的新的统计问题。这项建议的目的是聚集顶尖和初级的研究人员来定义和扩大这些统计学的研究前沿。研讨会为顶尖和初级的研究人员提供了一个集中的场所，让他们收集、交流和展示他们的新研究成果，讨论和概述他们领域中出现的问题，并为未来富有成效的合作奠定基础。此外，它将为有兴趣寻找感兴趣的领域的研究生提供一个概述。研讨会的重点是科学和工程各个前沿领域的各种非参数统计问题，如计算生物学、金融计量经济学、机器学习和工业工程。这将非常有助于塑造未来统计研究的方向，解决具有高度社会影响的问题。

Prop ID: DMS-0531839 PI: Fan, Jianqing Institution: Princeton University Title: Workshop on Frontiers of Statistics: Nonparametric Modeling of Complex Data This is an extremely well conceived worshop that will bring together some of the The frontiers of statistics are dynamic and vibrant. The aim of thisproposal is to organize a workshop on the frontiers of statistics thatinvolve large and complex data. The conference will focus on data-analyticnonparametric techniques in diverse fields of disciplines such ascomputational biology, financial econometrics, machine learning, andindustrial engineering. It will also include various topics onstatistical theory and methods. A distinguished feature of the conferenceis that it addresses emerging statistical problems that arise from variousfrontiers of science. It will be very helpful for shaping the futuredirections of statistical research.The purpose of this proposal is to bring together top and juniorresearchers to define and expand these research frontiers of statistics.The workshop provides a focal venue for top and junior researchers togather, interact and present their new research findings, to discuss andoutline emerging problems in their fields, and to lay the groundwork forfruitful future collaborations. Moreover, it will provide an overview forgraduate students who are interested in seeking for an interesting area towork. The workshop focuses on various nonparametric statistical problemsfrom various frontiers of science and engineering such as computationalbiology, financial econometrics, machine learning, and industrialengineering. It will be very helpful for shaping the future directions ofstatistical research that addresses problems of high societal impact.