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Wed, February 18, 2015

The American Statistical Association has selected Han Liu as the winner of this year’s Noether Young Scholar Award for for outstanding early career contributions to nonparametric statistics.

The Noether Young Scholar Award is given each year to an accomplished young researcher. This award is made to foster, encourage, and support both research and teaching in nonparametric statistics. The Noether Young Scholar will deliver an invited lecture the year after the award and be asked to report on research performed since receiving the award.

Wed, February 18, 2015

Prof. Han Liu has received a National Science Foundation (NSF) CAREER Award, effective September, 1, 2015. The title of his project is "An Integrated Inferential Framework for Big Data Research and Education". Prof. Liu aims to develop a new generation of inferential tools for assessing uncertainty of complex statistical machine learning methods unique to Big Data analysis. The CAREER Award is the most prestigious award from the NSF in support of junior faculty who exemplify the role of teacher-scholars through outstanding research, excellent education and the integration of education and research within the context of the mission of their organizations.

Thu, January 22, 2015

Zhaoran Wang, a PhD student in the StatLab, was awarded a distinguished 2015 Microsoft Research PhD Fellowship. Out of the 169 applicants in north America, Zhaoran is one of only 12 winners.

The Microsoft Research PhD fellowship honors the best young minds in Computer Science, Electrical Engineering, and Mathematics. Zhaoran, under the supervision of Professor Han Liu, is developing fundamental theoretical guarantees (both computational and statistical) for solving large-scale nonconvex learning problems. The two-year fellowship includes tuition, fees, and a yearly stipend.

Congratulations Zhaoran!

Fri, March 7, 2014

Jianqing Fan has been awarded the Guy Medal in Silver of Royal Statistical Society for 2014 for his path-breaking research in high-dimensional statistical learning and inferences, important contributions to non-parametric and semi-parametric statistics, and his service to the international statistical community.

Fri, March 7, 2014

Princeton S. S. Wilks Memorial Lecture Series will feature two speakers per year, one from statistics (Spring term) and one from probability (Fall term).
This lecture series is funded by the S. S. Wilks Memorial Fund endowed in honor of the late Princeton Professor of Statistics Samuel Stanley Wilks.

The year, the speaker will be Peter J. Bickel from UC Berkeley.

Wednesday March 26, 2014, 5:00pm
Computer Science 104

Title: From Fisher to “Big Data”: Continuities and discontinuities
Abstract: In two major papers in 1922 and 1925 Fisher introduced many of the ideas ,parameters , sufficiency, efficiency, maximum likelihood, which when coupled with Wald’s decision theoretic point of view of 1950 have underlain the structure of statistics until the 1980’s.That period coincided ,not accidentally,with the beginnings of the widespread introduction of computers and our ability to use them to gather “big data” and implement methods to analyze such data .In this lecture I will try to see how the Fisherian concepts have evolved in response to the new environment and to isolate and study new ideas that have been brought in and where they have come from. Thus,I will argue that “sufficiency” has evolved to “data compression”,”efficiency” has had to include computational considerations, and issues of scale ,“parameters” and procedures such as “maximum likelihood” have had to be considered in the context of larger semi and nonparametric models and in robustness. The steady rise in computational capability during the last 30-40 years has enabled the implementation of the older Bayesian point of view .computer intensive methods such as Efron’s “bootstrap” as well as the introduction of the “machine learning” point of view and methods from computer science.I will try to support my argument from the literature ,some of my own work and my experience with ENCODE .a “Big Data” project in biology.

Fri, January 31, 2014

DataFest is a data analysis competition where teams of up to five undergraduates have a weekend to attack a large and complex dataset. Your job is to find and communicate insights into these data. The teams that impress the judges will win prizes as well as glory. Everyone else will have a great experience, lots of food, and fun!
All Princeton undergrads are welcome to join the competition on Mar. 28-30. Registration is free.
Visit the link below for more information.