Brady T. West serves as a Research Associate Professor in the Survey Methodology Program at the University of Michigan’s Survey Research Center, part of the Institute for Social Research. He earned his PhD in Survey Methodology from Michigan in 2011, following an MA in Applied Statistics in 2002 and a BS in Statistics with Highest Honors in 2001, both from the same institution. His research focuses on the implications of measurement error in auxiliary variables and survey paradata for survey estimation, along with survey nonresponse, interviewer effects, and multilevel regression models for clustered and longitudinal data. West is the lead author of Linear Mixed Models: A Practical Guide using Statistical Software, Second Edition (2014, Chapman Hall/CRC Press) and co-author of Applied Survey Data Analysis (2017, Chapman Hill) with Steven Heeringa and Pat Berglund. Residing in Dexter, MI, he enjoys family life with his wife, Laura, their children, Carter and Everleigh, and their American Cocker Spaniel, Bailey.

James Wagner, Ph.D., is a Research Professor at the University of Michigan's Survey Research Center (UM-SRC). His expertise centers on survey methodology, particularly addressing nonresponse issues during data collection. Dr. Wagner has advanced the field with his work on responsive and adaptive survey designs, which aim to enhance data quality by mitigating nonresponse biases. He has also contributed to statistical decision rules that guide these methodologies.His scholarly work spans a range of prestigious journals, such as Public Opinion Quarterly, Statistics in Medicine, and Journal of the Royal Statistical Society. Dr. Wagner co-authored the book Adaptive Survey Design (2017), which provides an in-depth exploration of innovative survey methodologies.In addition to his research, Dr. Wagner has over 20 years of practical experience in sample design, having worked on diverse and complex sampling projects. He teaches courses in statistics, sampling, and methods to address nonresponse as part of the Michigan Program in Survey and Data Science and the Joint Program in Survey Methodology. His expertise has been sought after by several federal statistical agencies, where he has served as a consultant on strategies to improve data quality.

Jinseok Kim, Ph.D. is a Research Assistant Professor in the Survey Research Center at the Institute for Social Research, and also in the U-M School of Information (by Courtesy). Dr. Kim has studied how data quality can affect knowledge discovery from big scholarly data and how to improve data quality control in digital libraries through machine learning. Especially, Dr. Kim has worked on innovating machine learning methods and procedures for author name disambiguation which is one of the most challenging data curation problems in digital libraries. His proposed methods for stratifying name disambiguation procedure and automating large-scale data labeling through record linkage have been funded by the National Science Foundation and being used in several other funded research projects. Dr. Kim received his doctorate degree from the School of Information Sciences at the University of Illinois Urbana-Champaign in 2017. He has published papers on machine learning for author name disambiguation, impact of data quality on research findings, and network measurements in computer and information science journals and conferences. He has also taught courses on text mining of social media data using natural language processing and network analysis.

Trent D. Buskirk, PhD is the Novak Family Distinguished Professor of Data Science and the Chair of the Applied Statistics and Operations Research Department at Bowling Green State University. Prior to his post at BGSU, Trent served as the Director for the Center for Survey Research at UMASS Boston and served as the Vice President for Statistics and Methodology at the Marketing Systems Group (MSG) and was tenured in the department of Biostatistics in the School of Public Health at Saint Louis University. Dr. Buskirk is a Fellow of the American Statistical Association and his research interests include Mobile and Smartphone Survey Designs, methods for calibrating and weighting nonprobability samples, and in the use of big data and machine learning methods for health, social and survey science design and analysis. His work has been published in leading survey, statistics and health related journals such as Cancer, Health Services Research, Social Science Computer Review, Journal of Official Statistics, Public Opinion Quarterly and the Journal of Survey Statistics and Methodology, among others. Recently, Trent served as the President of the Midwest Association for Public Opinion Research in 2016, the Conference Chair for AAPOR in 2018 and is currently part of the scientific committee for the BigSurv18 and BigSurv20 conferences. When Trent is not working or thinking about surveys or machine learning, you can find him playing an action-packed game of pickleball!