What is the role of biostatistics in an increasingly big data/data science focused world. There is so much attention these days on machine learning and AI in biomedical research. What are the opportunities for synergies between biostatistics and other data science disciplines?
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Xihong Lin, PhD, | is Professor and former Chair of Biostatistics, Coordinating Director of the Program in Quantitative Genomics at the Harvard T. H. Chan School of Public Health, and Professor of Statistics of Harvard University. Dr. Lin’s research interests lie in development and application of scalable statistical and machine learning methods for analysis of massive high-throughput data from genome, exposome and phenome, and complex epidemiological, biobank and health data. She is a recipient of the Outstanding Investigator Award (OIA) (R35) (2015-2029) from the National Cancer Institute (NCI). Dr. Lin is an elected member of the National Academy of Medicine. She received the 2002 Mortimer Spiegelman Award from the American Public Health Association, the 2006 Presidents’ Award of the Committee of Presidents of Statistical Societies (COPSS), and the 2022 Marvin Zelen Leadership in Statistical Science Award. She is an elected fellow of American Statistical Association, Institute of Mathematical Statistics, and International Statistical Institute. Dr. Lin is the former Chair of the COPSS (2010-2012) and a former member of the Committee of Applied and Theoretical Statistics of the National Academy of Science. She is the former Coordinating Editor of Biometrics and the founding co-editor of Statistics in Biosciences. She has served on many NIH and NSF review panels.
Jeff Goldsmith, PhD, | Dr. Jeff Goldsmith is an associate professor in Biostatistics at the Columbia University Mailman School of Public Health. His work advances the state-of-the-art in functional data analysis by developing methods for understanding patterns in large, complex datasets in neuroscience, physical activity monitoring, and other areas. He works closely with clinicians and neuroscientists around the world to develop methods that improve the understanding skilled movements and motor control. In parallel, he has proposed methods for wearable device research, especially focusing on accelerometers. Dr. Goldsmith's work incorporates data science techniques for transparency and reproducibility, and methods are accompanied by robust, publicly available software.
Lance A. Waller, PhD, | Dr. Lance A. Waller is Professor in the Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University. He received his B.S. in Mathematics from New Mexico State University (1986), and his Ph.D. in Operations Research from Cornell University (1991). Prof. Waller leads the Woodruff Health Science Center's Strategic Initiative in Data Science and the co-Director of Emory’s new Center for AI Learning. He is currently a member of the U.S. National Academy of Science Board on Mathematical Science and Analytics and serves as co-Chair of the Committee on Applied and Theoretical Statistics. Prof. Waller is serving a three-year term on the US Census Bureau's Scientific Advisory Committee.
His research involves the development and application of statistical methods for spatially referenced data including applications in environmental justice, neurology, epidemiology, disease surveillance, conservation biology, and disease ecology. He has published in a variety of biostatistical, statistical, environmental health, and ecology journals and is co-author with Carol Gotway of the text Applied Spatial Statistics for Public Health Data (2004, Wiley). Areas of interest include: Biostatistics, Data Science, Disease Surveillance, Infectious Disease, Public Health Preparedness and Response, Spatial Analysis/GIS, and Statistical Modeling.
Yu Shyr, PhD, Dr(hc), FASA, FAAAS, FAACR, Harold L. Moses Chair in Cancer Research, Chair, Department of Biostatistics, Director, Vanderbilt Center for Quantitative Sciences, Director, Vanderbilt Technologies for Advanced Genomics Analysis and Research Design (VANGARD), Professor of Biostatistics, Biomedical Informatics, and Health Policy, Vanderbilt University Medical Center.
Yu Shyr received his PhD in biostatistics from the University of Michigan in 1994 and subsequently joined the faculty at Vanderbilt University School of Medicine, where he won the Hawiger Award for Excellence in Teaching in 2012 and was inducted into the Academy for Excellence in Education in 2013. Renowned for his development of integrative methods and high-impact applications of those methods, Shyr is an elected fellow of the American Statistical Association (ASA), the American Association for the Advancement of Science (AAAS), and the American Association for Cancer Research (AACR), and he holds an honorary doctorate from National Cheng Kung University. He has published more than 500 peer-reviewed papers (h-index = 116) and serves as an associate editor for JAMA Oncology and Journal of Thoracic Oncology. Shyr has presented more than 250 invited talks, workshops, and courses around the world and currently co-directs the BMSF-AACR Design and Implementation of Clinical Trials Workshop, part of the Robert A. Winn Diversity in Clinical Trials Award Program established by the Bristol Myers Squibb Foundation.
In demand as an expert on external advisory boards and data safety monitoring committees across the United States, Shyr was a voting member of the US Food and Drug Administration (FDA) Anti-Infective Drugs Advisory Committee and has served on dozens of National Cancer Institute (NCI) study sections and panels. As principal investigator of the Barrett’s Esophagus Translational Research Network Coordinating Center (BETRNetCC), funded by an NCI U24 grant, Shyr led a team that provided crucial support for the collection, management, analysis, and dissemination of data among multiple research centers. His current projects include developing novel statistical and bioinformatic tools for biomedical research and improving experiment and clinical trial design, particularly in the areas of proteomics and genomics, with special focus on RNA sequencing.
Moderator, David Benkeser, PhD | is an Assistant Professor in the Department of Biostatistics and Bioinformatics at Emory University. Dr. Benkeser’s methodological research focuses on the theory and applications of machine learning in causal inference. Specific areas of interest include competing risks, complex longitudinal data, and theory of robust nonparametric statistical inference. His collaborative research includes work on preventive vaccines and HIV prevention.
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