Principal Investigators’ New Websites

May 6, 2020:

Stephanie Lanza, Bethany Bray, Linda Collins, Susan Murphy, Runze LiAs previously announced, substantial changes lie ahead for The Methodology Center. Over the coming months, we will stop updating this website. Resources will remain available for at least a year, but in order to provide the latest information, each of our investigators will maintain her or his own website. These sites will include content developed at The Methodology Center and new resources related to the researcher’s future work.

Stephanie Lanza and Ashley Linden-Carmichael built a website for the Addictions and Innovative Methods (AIM) lab. Their great new site, https://aimlab.psu.edu/, describes their research and includes the content about time-varying effect modeling (TVEM) from The Methodology Center’s website.

Susan Murphy has incorporated The Methodology Center’s content on just-in-time adaptive interventions in her website, http://people.seas.harvard.edu/~samurphy/. The site also includes workshop materials and other resources.

Bethany Bray‘s new site at https://bcbray.com/ will include The Methodology Center’s resources for latent class analysis (LCA) and latent transition analysis (LTA). Bethany has concrete plans for new LCA and LTA resources, so stay tuned.

Runze Li will update his page at http://personal.psu.edu/ril4/ to incorporate Methodology Center resources on variable screening and variable selection for high-dimensional data analysis.

Linda Collins will build a new website to house The Methodology Center’s content on the multiphase optimization strategy (MOST) for optimizing interventions after she moves to New York University. In the meantime, follow Linda on Twitter, @collins_most.

Daniel Almirall and Inbal “Billie” Nahum-Shani’s informative website, https://d3lab.isr.umich.edu, will soon incorporate The Methodology Center’s resources for the sequential, multiple assignment, randomized trial (SMART).

More information will follow in June or July. Thank you for staying connected to our research! We are all proud of our time at The Methodology Center and very excited about the future.

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.