Pao-Lu Hsu Award
Presented every three years, usually at an ICSA conference, to an individual under the age of 50, who makes influential and fundamental contributions to any field of statistics and probability, and exemplifies Hsu's deep involvement in developing statistics and probability research with significant impact on education.
The prize is open to all nationalities. Priorities are given to the candidates whose work contributes greatly to the research and education of Chinese statisticians. The award recipient will speak at an ICSA International Conference. The award includes $3000 in cash prize.
Hsu, who was born in 1910, was a pioneer and founder of the newly formed discipline of statistics and probability in china. Hsu was best known for his rigorous research with depth and breadth, and for his profound impact on younger generations. He became the first professor of statistics and probability, Beijing University, in 1940. In 1948, he was elected to the very first class of Academicians of the Chinese Academy of Sciences. He published about 40 articles; see “Pao-Lu Hsu Memorial Collection” published by Peking University Press for more details.
The first Pao-Lu Hsu Award was announced in 2012 and presented in the 9th ICSA International Conference in Hong Kong, 2013. Professor Jianqing Fan (Princeton), Professor Xiao-Li Meng (Harvard), and Professor Bin Yu (UC Berkeley) shared the first ICSA Pao-Lu Hsu Award.
For the next PL Hsu award which will be given in the 10th ICSA International Conference in Shanghai, P.R. China in 2016, an individual is eligible if he/she was born on or after January 1, 1965.
The ICSA 2016 Pao-Lu Hsu Award
Jun S. Liu, Ph.D.
Department of Statistics Harvard University
For fundamental and pioneering contributions to Monte Carlo methods and Bayesian computation; for successful development of the Gibbs motif sampler for understanding gene regulation; for highly influential contribution in reconstruction of haplotypes from single nucleotide polymorphism data and the inference of epistatic interaction; and for the advancement of statistics in China.
Jun Liu is Professor of Statistics at Harvard University, with a joint appointment in the Harvard School of Public Health. He was a Changjiang Scholar at Peking University, and also Guest Professor at Tsinghua University. Dr. Liu received his BS degree in mathematics in 1985 from Peking University and Ph.D. in statistics in 1991 from the University of Chicago. He held Assistant, Associate, and full professor positions at Stanford University from 1994 to 2003. Dr. Liu won the NSF CAREER Award and the Stanford Terman fellowship in 1995, won the Mitchell Award for the best statistics application paper in 2000. He was a Medallion Lecturer of the Institute of Mathematical Statistics (IMS) in 2002, a Bernoulli Lecturer in 2004, and a Kuwait Lecturer of Cambridge University in 2008. He was elected to Fellow of the IMS in 2004 and Fellow of the American Statistical Association in 2005. He served on numerous grant review panels of the NSF and NIH and editorial boards of numerous leading statistical journals. He was a co-editor of the Journal of the American Statistical Association.
In 2002, he received the prestigious COPSS Presidents' Award (given annually and jointly by five leading statistical associations to one individual under age 40). In 2010, he was awarded the Morningside Gold Medal in Applied Mathematics (honored once every 3 years to an individual of Chinese descent under age 45). In 2012, he was honored with the Outstanding Achievement Award by the International Chinese Statistical Association.
Dr. Liu and his collaborators introduced the statistical missing data formulation and Gibbs sampling strategies for biological sequence analysis in early 1990s. The resulting algorithms for protein sequence analysis, gene regulation analysis, and genetic studies have been adopted by many research groups and become standard tools for computational biologists. Dr. Liu has made fundamental contributions to statistical computing and Bayesian modeling. He pioneered sequential Monte Carlo (SMC) methods invented a few novel Markov chain Monte Carlo (MCMC) techniques. His studies of SMC and MCMC algorithms have had a broad impact on both theoretical understandings and practical applications. Dr. Liu has also pioneered novel Bayesian modeling techniques for discovering subtle interactions and nonlinear relationships in high-dimensional data. Dr. Liu has published one research monograph and more than 200 research articles in leading scientific journals, and is one of the ISI Highly Cited mathematicians.
Title: In Search of Relationships: From R-squared to Semi-parametric Models
Abstract: I will discuss a few recent results from my group in exploring the utility of inverse modeling for detecting nonlinear relationships. Our investigations bring together ideas from the naive Bayes modeling, Fisher’s linear discriminant analysis, and the sliced inverse regression for dimension reductions. These ideas center around the strategies related to ``slicing” (aka, discretization) of the response variable. In one direction, we optimally slice one variable (or the response) to maximize a score function based on the likelihood‐ratio test. The resulting statistic, called the generalized R‐square or G2, gives rise to a relationship measure taking values in [0,1] and can be viewed as a direct extension of the standard R‐square. The G2 statistic is compared with some popular measures such as Distance Correlation, Pearson Correlation, Maximal Information Criterion, etc., on many simulated examples, and found superior for detecting highly nonlinear and non‐smooth relationships. If time permit, we will also discuss some theoretical properties of sliced inverse regression in high dimensions.