Conference Introduction

Statistical learning is an active research area in the era of big data. In this conference we will discuss the opportunities and challenges that confront us today as statisticians. We believe that effective analysis of big data involve mathematical and statistical modeling, data reduction, and data mining tools. At the conference, the participants will bring different perspectives and have engaging discussions among themselves and also with researchers in China. We plan to invite several active researchers in China to join the discussion at the conference.

Among the participants we have experts in adaptive designs (William Rosenberger, Feifang Hu), leading researchers in statistical learning (Xiaotong Shen, Ji Zhu), and experts on longitudinal data analysis (Annie Qu, Xuming He). All the above-mentioned experts are fellows of the American Statistical Association. 

Conference Schedule

Oct 7
14:00-16:00 Annie Qu, University of Illinois
Longitudinal data analysis

Oct 8
9:30-11:30 Xiaotong Shen, University of Minnesota
Feature selection
14:00-16:00 Feifang Hu, University of Virginia
Adaptive designs

October 9
9:00 -9:35 Hongyu Zhao, Yale University
Markov random field models in genomics studies

9:35-10:10 Huazhen Lin, Southwest University of Finance and Economics
Estimating a unitary effect summary based on combined survival and quantitative outcomes

10:30-11:05 Niansheng Tang, Yunan University
Robust Estimation in Semiparametric Estimating Equations with Nonignorably Missing Data

11:05-11:40 Ruosha Li, University of Pittsburgh
Quantile Association Regression Models

14:00-14:35 William Rosenberger , George Mason University
An Introduction to Randomization for Ethics and Optimality in Clinical Trials

14:35-15:10 Feifang Hu, University of Virginia
Adaptive Designs for Personalized Medicine

15:30-16:05 Juan Shen, University of Michigan
Model-based inference for subgroup analysis

Oct 10
9:00 -9:35 Xiaotong Shen, University of Minnesota
Personalized information filtering

9:35-10:10 Jianhua Guo, Northeast Normal University
Imprinting Test of Disease-Associated SNPs under mixture model

10:30-11:05 Guodong Li, University of Hong Kong
Quantile correlation and quantile autoregressive modeling

11:05-11:40 Yuanjia Wang, Columbia University
Using Logic Rules and Statistical Learning to Develop Diagnostic Criteria Set for Psychiatric Disorders

13:30-14:05 Ji Zhu, University of Michigan
On consistency of community detection in networks

14:05-14:40 Annie Qu, University of Illinois
Personalized treatment for longitudinal data

15:00-15:35 Hongjian Zhu, University of Taxes, School of Public Health
Sequential monitoring of covariate adaptive randomization

Oct 11
9:30-11:30 Xuming He, University of Michigan
Quantile regression

14:00-16:00 Hongyu Zhao, Yale University
Graphical models and network