Lizbeth ‘Libby’ Benson, PhD, is a Research Assistant Professor in the Data Science for Dynamic Intervention Decision Making Center (d3c) at the University of Michigan’s Survey Research Center and Institute for Social Research. Before moving to Michigan, Libby completed a Postdoctoral Fellowship at the TSET Health Promotion Research Center within the NCI-designated Stephenson Cancer Center and University of Oklahoma Health Sciences Center. She received her PhD from the Pennsylvania State University in the department of Human Development and Family Studies and her BA in Psychology from the University of Wisconsin-Madison.

Libby’s research program is focused on intensive longitudinal, computational, and machine learning methods for examining temporal dynamics of affective, social and health behavior experiences using ecological momentary assessment and sensor-based data collected from individuals in their daily lives. Her goals are to understand how behavioral processes unfold across multiple time-scales and contexts, and how this knowledge can be used to build personalized interventions to facilitate health behavior change. Data visualization is also an important component of her work as a way to better understand complex behavioral processes, to generate new ideas, and to use as a tool for scientific communication. Currently, Libby is writing a NIH K01 focused on developing a reinforcement learning algorithm for personalizing intervention content in a smoking cessation just-in-time adaptive intervention.

Sunghee Lee is a Research Professor in the Survey Methodology Program at the Survey Research Center, Institute for Social Research, where she directs the Program in Survey and Data Science. She earned her PhD from the Joint Program in Survey Methodology at the University of Maryland. Before joining the University of Michigan, she served as a Survey Methodologist for the California Health Interview Survey and as an Adjunct Assistant Professor of Biostatistics at UCLA.

As a methodologist, her research advances the study of data quality through a focus on inclusivity, with important implications for equity in social programs and policy decisions. Her work addresses two central dimensions of data quality: representation and measurement. She identifies and examines sources of error that can compromise inclusivity—including coverage, nonresponse, translation, question order, and response style—often at the intersection of survey methodology and cultural norms. She leads the sampling component of the Health and Retirement Study and serves as principal investigator on multiple studies that use respondent-driven sampling to recruit hard-to-reach population subgroups, including immigrant populations. Her work also explores the potential of emerging technologies to improve data inclusivity and strengthen data quality.