Conference on Nonparametric Statistics for Big Data, June 4-6, 2014

威斯康星大学麦迪逊分校统计系将举办大数据非参数统计研讨会(2014年6月4日至6日，威斯康星州麦迪逊)。非参数统计是统计学的一个基本领域，是数学、统计学、数据挖掘、工程学和计算机科学的交汇点。大数据的复杂性和规模给知识发现带来了巨大的挑战，同时也要求更强大、更灵活的分析技术。近年来，非参数统计领域在理论、方法和计算方面都有了长足的发展，以解决大数据分析中出现的问题。非参数理论的新突破拓宽了经典大样本渐近推断的视野，以适应高维和超高维的情况。在大数据可视化、几何表示、降维以及建模和推理方面，创造了各种尖端的统计方法和最先进的计算算法。这些工具对科学、工程和工业产生了重大影响。研讨会将涵盖广泛的主题，包括稀疏非参数回归、正则化和特征选择、高维推理和理论、空间和环境统计学、图像分析、函数数据分析，以及统计机器学习中的相关主题，如监督学习、聚类、网络分析、大规模优化、计算生物学和生物信息学。由于最近的技术进步，大数据在许多科学研究中被普遍收集，如生物、基因组、医学、气候、社会和环境科学。考虑到要测量的系统的复杂性和巨大范围，大量新的“非参数”工具已经出现，这些工具几乎不需要假设，并且适应大数据中的模式。本次研讨会将汇集来自数学、统计学、计算机科学、机器学习、工程学和生物医学研究的广泛跨学科专业知识，以突出国内外知名学者和研究人员的前沿研究。研讨会将利用国家科学基金会为旅行奖励提供的资金来吸引研究生、学生和年轻的研究人员，特别关注女性和代表性不足的少数群体。它将为年轻研究人员创造一个与领先科学家互动的独特机会。通过30次全体会议(说明性、中级和高级)、公开讨论和两个海报部分，研讨会将促进新的联系和合作。此外，本研讨会提供了一个重要的回顾，将重点介绍未来大数据分析的非参数统计研究方向。研讨会网站：http://www.stat.wisc.edu/workshop-npbigdata

The Department of Statistics at the University of Wisconsin at Madison will host a Workshop on Nonparametric Statistics for Big Data (June 4-6, 2014, Madison, WI). Nonparametric statistics is a fundamental area of statistics, at the interface of mathematics, statistics, data mining, engineering, and computer science. The complexity and scale of big data impose tremendous challenges for knowledge discovery; they meanwhile demand more powerful and flexible analysis techniques. In recent years, the field of nonparametric statistics has seen significant development in theory, methods, and computation to address emerging issues in big data analysis. New breakthroughs in nonparametric theory have broadened the horizon of classical large-sample asymptotic inferences to accommodate high and ultra-high dimensional situations. A variety of cutting-edge statistical methodologies and state-of-art computational algorithms have been created for big data visualization, geometric representation, dimension reduction, and modeling and inference. These tools have made significant impacts on sciences, engineering, and industry. A broad range of topics will be covered in the workshop, including sparse nonparametric regression, regularization and feature selection, high-dimensional inference and theory, spatial and environmental statistics, image analysis, functional data analysis, as well as related topics in statistical machine learning such as supervised learning, clustering, network analysis, large-scale optimization, computational biology and bioinformatics.Due to recent technology advances, Big Data are collected ubiquitously in many scientific investigations, such as in biological, genomic, medical, climate, social, and environmental sciences. Given the complexity and huge range of systems being measured, a wealth of new "nonparametric" tools have been emerging that require few assumptions and that adapt to the patterns found in big data. This workshop will bring together broad interdisciplinary expertise from mathematics, statistics, computer science, machine learning, engineering, and biomedical research to highlight cutting-edge research from nationally and internationally renowned scholars and researchers. The workshop will use NSF funding for travel awards to attract graduates students and young researchers, with special attention to women and underrepresented minorites. It will create a unique opportunity for young researchers to interact with leading scientists. Through 30 plenary talks (expository, intermediate, and advanced), open floor discussions, and two poster sections, the workshop will promote new connections and collaborations. Further, this workshop provides an important review that will highlight future research directions nonparametric statistics for big data analysis.Workshop web site: http://www.stat.wisc.edu/workshop-npbigdata