Zhenke Wu is an Assistant Professor of Biostatistics, and Research Assistant Professor in Michigan Institute of Data Science (MIDAS). He received his Ph.D. in Biostatistics from the Johns Hopkins University in 2014 and then stayed at Hopkins for his postdoctoral training before joining the University of Michigan. Dr. Wu’s research focuses on the design and application of statistical methods that inform health decisions made by individuals, or precision medicine. The original methods and software developed by Dr. Wu are now used by investigators from research institutes such as CDC and Johns Hopkins, as well as site investigators from developing countries, e.g., Kenya, South Africa, Gambia, Mali, Zambia, Thailand and Bangladesh.
Marcelline Harris, Ph.D., R.N., is Associate Professor of Systems, Populations and Leadership in the School of Nursing at the University of Michigan, Ann Arbor.
Dr. Harris’s research interests focus on what is being labeled the “continuous use” of clinical data (the use of clinical data for one or more purposes), computable knowledge representation strategies, and the use of electronic clinical data for practice and research. Her research has been funded by NIH, AHRQ, RWJF, and PCORI. Harris also has extensive enterprise level experience, having served in both scientific and operational positions that address the development and governance of systems that support the capture, storage, indexing, and retrieval of clinical data. At Michigan, she retains this translational perspective, emphasizing clinical data for patient-centered research, clinical surveillance and predictive analytics.
V.G.Vinod Vydiswaran, PhD, is Assistant Professor in the Department of Learning Health Sciences with a secondary appointment in the School of Information at the University of Michigan, Ann Arbor.
Dr. Vydiswaran’s research focuses on developing and applying text mining, natural language processing, and machine learning methodologies for extracting relevant information from health-related text corpora. This includes medically relevant information from clinical notes and biomedical literature, and studying the information quality and credibility of online health communication (via health forums and tweets). His previous work includes developing novel information retrieval models to assist clinical decision making, modeling information trustworthiness, and addressing the vocabulary gap between health professionals and laypersons.
Dr. Liu has a broad research interest in the development of statistical models and techniques to address critical issues in health and nursing sciences, computational processing of Big Data in clinical Informatics and Genomics, statistical modeling and assessment of risk factors (e.g. hypertension, diabetes, central obesity, smoking) for adverse cardiovascular and renal outcomes and maternal and child health. His expertise in statistics includes, but is not limited to, repeated measures models with missing data, multilevel models, latent variable models, and Bayesian and computational statistics. Dr. Liu has led and co-led several NIH-funded projects on the quality of care for hypertensive patients.
Kerby Shedden has broad interests involving applied statistics, data science and computing with data. Through his work directing the data science consulting service he has worked in a wide variety of application domains including numerous areas within health science, social science, and transportation research. A current major focus is development of software tools that exploit high performance computing infrastructure for statistical analysis of health records, and sensor data from vehicles and road networks.