Director of the Methodologic Core

Lloyd J. Edwards, PhD, is a Professor of Biostatisics, UAB School of Public Health. He has over 30 years of experience in the planning and analysis of clinical trials, observational studies, and community-based projects. He has an extensive background in collaborating with researchers in a broad range of areas in biomedical research, including cardiovascular disease, cystic fibrosis, cancer, aging, pediatrics, and minority health. His primary area of applied statistical research relates to the analysis of longitudinal data. Specifically, his statistical research includes derivation of techniques for computation of power, control of Type I error, and measuring model fit in linear and generalized linear mixed models. He will help provide project support and leadership in study design, statistical analyses, data management, and development of manuscripts and presentations.

Director of the Methodologic Core

Richard Reynolds, PhD, is an Assistant Professor of Medicine in the Division of Clinical Immunology and Rheumatology. His current research interests include genetic epidemiology of gout and associated comorbidities and the genetic and epigenetic causes of gout flares. Dr. Reynolds earned his Ph.D. from the University of Maryland College Park's Department of Biology in 2008. He completed a post doctoral fellowship in UAB's Department of Biostatistics, section on Statistical Genetics in 2010 and thereafter joined the faculty at UAB.

Director of Integration of the DCI and Methodology Cores

John Osborne, PhD, is an Assistant Professor of Medicine at UAB and a member of the UAB Informatics Institute. He is the lead developer for PheDRS, a flexible natural language processing (NLP) pipeline used for case/phenotype identification for multiple types of disease in service of various research initiatives in various stages of planning and funding. Dr. Osborne provides substantial bioinformatics expertise to the team. He will work collaboratively with the Methodology Core in service to our investigators, helping ensure that the software needed for data analysis will be available to investigators when their findings are moved to our secure storage and analytics servers. In addition, he will help investigators extract findings from our enterprise data warehouse using NLP (e.g., analysis of pathology, radiology or clinical free text notes, facilitated by web-based annotation tools already housed on PEER servers [ref]) and his PheDRS software, enhancing case finding beyond what can be accomplished through querying diagnostic codes and other discrete data in our EHR.

Kimberly B. Garza, PharmD, MBA, PhD, is an Associate Professor of Health Outcomes Research and Policy at the Auburn University Harrison School of Pharmacy. She specializes in the application of behavioral economics concepts to the development of interventions to improve health behaviors, including diet, physical activity, medication adherence, and immunization. Her work focuses on patients in a cardiac rehabilitation program, where she is assessing the utility of a brief measure of delay discounting delivered at the point of care to predict health behaviors and clinical outcomes. She also studies the acceptability and effectiveness of various incentive structures (both financial and social) to improve medication adherence in patients with cardiovascular disease using both cross-sectional and randomized, controlled trial designs.

Nengjun Yi, PhD, is Professor in the Department of Biostatistics, Section on Statistical Genetics, at UAB. His broad research interests include statistical genetics, Bayesian statistics, Markov chain Monte Carlo (MCMC) algorithms, and shrinkage prediction models. He is internationally known for his Bayesian methods in analyzing gene by gene and gene by environment interactions. His shrinkage based disease prediction models can accommodate huge number of predictors that are commonly seen in the genomic studies and health record analyses. Another current interest of Dr. Yi’s is developing analysis models for complex microbiome studies, such as longitudinal microbiome studies and correlated microbiome studies. He will be guiding the design and analyses of these types of studies.

Chengcui Zhang, PhD, is a Professor in the Department of Computer Science and Director of the Knowledge Discovery and Data Mining Lab. She provides her expertise to BIGDATA users with the development of pattern matching algorithms, image analysis using Deep Learning, and data visualization methods.