Conference on Nonparametric Statistics for Big Data

and Celebration to Honor Professor Grace Wahba

June 4-6, 2014
Madison, Wisconsin

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The Department of Statistics at the University of Wisconsin at Madison hosted a Workshop on Nonparametric Statistics for Big Data , June 4-6, 2014 in Madison, WI at the H.F. Deluca Forum, Wisconsin Institute for Discovery. 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 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 minorities. 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.

Professor Grace Wahba has played a fundamental role in the development of theory and practice of nonparametric statistics in Big Data. Much work in this field derives directly from her contributions, and would not have been possible (or would look very different) without her insight and involvement. Along with the primary scientific focus of this conference, we honor the many contributions of Professor Grace Wahba on the occasion of her 80th birthday.

There will be a social gathering to begin the conference event on Tuesday, June 3 from 7:00 to 9:00 pm at Union South more information to follow.
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Confirmed Speakers

Confirmed Speakers

Committees

Honorary Chair: Grace Wahba

Program Committee:

Fund & Travel Support Committee:

Xiaotong Shen, Chair
Brian Yandell
Hao (Helen) Zhang

Contact Us:
Questions about the Symposium can be directed to Ming Yuan myuan@stat.wisc.edu

 

Local Organizing Committee:

Sijian Wang
Brian Yandell
Ming Yuan