Runze Li Named Fellow of AAAS

November 20, 2017:RLi Fellow

Congratulations to Methodology Center Investigator Runze Li for being named a 2017 Fellow of the American Association for the Advancement of Science (AAAS). AAS Fellows are recognized for their contributions to science and technology. Runze is one of only seven statisticians recognized by AAAS this year. AAAS is the world’s largest general scientific society and publisher of the journal Science.

Runze’s research focuses on methods for the analysis of intensive longitudinal data and for variable selection and variable screening. His work led directly to the development of our SAS macro for time-varying effect modeling (TVEM), and his current work will enable scientists to identify which genetic, individual, and social factors predict drug abuse, HIV-risk behavior, and related health behaviors.

Read more about Runze.

Runze Li Receives Distinguished Achievement Award from ICSA

September 14, 2017:rli17
Congratulations to Methodology Center Principal Investigator Runze Li for winning the 2017 Distinguished Achievement Award from The International Chinese Statistical Association (ICSA). ICSA is recognizing Runze for his research on variable selection, nonparametric and semiparametric modeling, and modeling for computer experiments. ICSA also specifically noted his interdisciplinary research—including his work here at The Methodology Center—and professional service.

Runze Li is Verne M. Willaman Professor of Statistics at Penn State. The Distinguished Achievement Award joins a long list of accolades he has earned, including the 2016 Canadian Journal of Statistics award, the 2012 United Nations’ World Meteorological Organization Gerbier-Mumm International Award, and being named a “Highly Cited Researcher” every year since 2014. In his current research on variable selection and variable screening, he is developing methods to analyze genetic data with intensive longitudinal data. This work will allow scientists to identify which genetic, individual, and social factors predict drug abuse, HIV-risk behavior, and related health behaviors.