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BERD Co-Investigators

  • For a brief summary of each expert’s qualifications & background, or for their contact information, please see below.

BERD Activities by Quarter, Q1 21 – Q2 22

Category21 Q 121 Q 221 Q 321 Q 422 Q 122 Q 2
Clinic Hours364188200144208176
Consulting Hours40129815312884287
Total Hours765486353272292463
Clinic Customers513744304540
Consulting Customers283024131627
Total Customers796768436167
Projects Begun29191691324
Online Articles101014
Campus Talks755741
Talk Participants1891261451427260


Chi-Yang Chiu, PhD
Assistant Professor, Preventive Medicine
Dr. Chiu is interested in both methodological and applied work in the fields of statistical genetics, mixed effects models and nonparametric modeling. He also enjoys collaborative work with biomedical researchers on various topics. Dr. Chiu obtained his PhD in Statistics from the University of California, Santa Barbara and received post-doctoral training at the Eunice Kennedy Shriver National Institute of Child Health and Human Development. Dr. Chiu is also a guest researcher in Statistical Genetics Section at the National Human Genome Research Institute.

Hyo Young Choi, PhD
Assistant Professor, Preventive Medicine
Hyo Young Choi, PhD, is an assistant professor in the Department of Preventive Medicine at the University Tennessee Health Science Center (UTHSC). She is also a research collaborator at the Department of Computational Biology at St. Jude Children’s Research Hospital. Her research areas include statistical genomics, machine learning, and high dimensional data analysis. She is interested in the development and application of statistical methods to reveal novel scientific findings in modern biological datasets. She was involved in several cancer genomics projects including the Cancer Genome Atlas (TCGA) project primarily working on high-throughput sequencing data by developing and applying statistical methods. She earned her PhD in Statistics and Operations Research from the University of North Carolina at Chapel Hill in 2018, and completed her postdoctoral fellowship at the Department of Medicine at UTHSC.

Jay Fowke, PhD
Chief of Epidemiology, Preventive Medicine
Dr. Fowke is an epidemiologist with a broad research program that combines prostate disease etiology, molecular epidemiology, obesity and metabolism, racial disparities research, measurement error and bias in prostate cancer screening methods, prostate cancer outcomes research, and the measurement of lower urinary tract symptoms. Publications include areas of race and prostate cancer screening and prostate cancer risk, and translational research investigating environmental factors affecting prostate tissue inflammation. Funding sources include the NCI, NIDDK, NINR, Department of Defense, American Institute of Cancer Research, and the Prostate Cancer Foundation. He was recruited to the Department in 2017, and now serves as Chief of the Division of Epidemiology and Interim Chair for the Department of Preventive Medicine at UTHSC.

Feng Liu-Smith, PhD
Assistant Professor of Epidemiology
Dr. Feng Liu-Smith is a molecular epidemiologist with a broad research interest in skin cancer and other cancer types. A focus of her current study is on the sex disparity in cutaneous melanoma incidence and survival using both large existing datasets, as well as data collected with collaborators at UTHSC. Publications include a series analysis on sex differences of melanoma incidence regarding age, body site location and race; molecular studies including a mechanistic study of natural compounds in melanoma and normal melanocytes in the laboratory. Funding sources include NIH/NCI, Melanoma Research Alliance and a UT New Grant Award. Dr. Liu-Smith became a faculty of Preventive Medicine in May, 2020 and has been a member of the BERD Clinic since April, 2021.

Śaunak Sen, PhD
Professor and Chief of Biostatistics, Preventive Medicine
Śaunak Sen, PhD, is Professor and Chief of Biostatistics in the Department of Preventive Medicine at the University of Tennessee Health Science Center. His specialties are statistical genetics and the analysis of high-dimensional data. He has contributed to statistical and computational methods for genetic analysis in model organism and admixed populations. He obtained his PhD from the University of Chicago; after postdoctoral stints at Stanford University and the Jackson Laboratory, he spent 12 years on the faculty at UCSF, before joining UTHSC in 2015. He has served on grant review panels for NIH and other agencies; he is an associate editor for the journal Genetics.

Satya Surbhi, PhD
Assistant Professor, Center for Health System Improvement
Dr. Sayta Surbhi is a health services researcher whose work focuses on developing and evaluating strategies to improve health care quality, reduce health care costs, and improve health outcomes among medically underserved patients with multiple chronic conditions. She is an Assistant Professor at the Center for Health System Improvement, College of Medicine at the University of Tennessee Health Science Center. Her current research focuses on addressing social and system-level barriers affecting optimal adherence to essential chronic disease medications in vulnerable high-need, high-cost patients. Dr. Surbhi received the 2019 Pharmaceutical Research and Manufacturers of America (PhRMA) Foundation Research Starter Grant in Health Outcomes. This grant-funded study aims to address the major financial, transportation, and system-level barriers to medication adherence among high-need, high-cost Medicaid patients. She also serves as co-investigator for the Patient-Centered Outcomes Research Institute-funded Management Of Diabetes in Everyday Life (MODEL) study and the University-supported Collaborative Research Network (CORNET) award in health disparities. Additionally, she manages the Diabetes, Wellness, and Prevention Coalition (DWPC) Registry, which serves as a specialized registry to help improve care for people with diabetes and other chronic diseases in the Mid-South region. Dr. Surbhi has successfully published her research papers in several peer-reviewed journals, such as Journal of General Internal Medicine, Journal of Managed Care & Specialty Pharmacy, and Journal of the American Pharmacists Association. She currently serves as the academic editor for the journal PLOS ONE and invited peer-reviewer for more than 15 national and international journals.

Fridtjof Thomas, PhD
Professor of Biostatistics, Preventive Medicine
Dr. Thomas’ special research interests lie in statistical aspects of causality in the health sciences and the design of observational studies (also including secondary data analyses and chart reviews). Dr. Thomas is the co-investigator/biostatistician on several NIH funded studies. He is a member of the Biostatistics Collaborative Core at the Southeast Regional Center of the NHLBI-funded Women’s Health Initiative (WHI) study that has recruited over 160,000 women in over 40 clinical centers nationwide and is a member of the Design and Analysis Committee of the NIH-funded EARLY trials. He has served as grant reviewer for the Department of Defense’s Congressionally Directed Medical Research Program (CDMRP) and for the NIH Neurological, Aging and Musculoskeletal Epidemiology Study Section (NAME). He is Vice President of the Western Tennessee Chapter of the American Statistical Association (WTASA) and is Associate Editor of the Journal of Statistical Computation and Simulation (JSCS).

Elizabeth Tolley, PhD
Professor of Biostatistics, Preventive Medicine
Dr. Tolley holds a doctorate from Virginia Tech, Blacksburg, VA, and received post-doctoral training in building statistical models at North Carolina State University, Raleigh, NC. As a faculty member at UTHSC, she is course director of the two-semester graduate-level course in Biostatistics for the Health Sciences I and II and for the graduate-level course in Linear Regression Models offered in the Masters of Epidemiology degree program. She has mentored numerous graduate students at the masters and doctoral levels. Dr. Tolley has served as a biostatistician, co-investigator, and consultant on many NIH grants. She also provides biostatistical consulting and collaboration services for many basic science and clinical investigators as part of her assigned faculty duties. Throughout her career, Dr. Tolley has worked with clinical investigators to develop mechanistic or diagnostic models of disease outcomes and predictive models of such outcomes.

Jim Wan, PhD
Professor of Biostatistics, Preventive Medicine
Dr. Jim Y. Wan received his BS in Mathematics from the Chinese University of Hong Kong in 1981, & his PhD in Statistics from Yale University in 1987. He collaborates with faculty across the whole campus on statistical methods for clinical and epidemiologic data and health services research. These collaborations have resulted in more than 130 peered-reviewed articles and close to 200 abstracts presented in major national and international scientific conferences. His research interest has been devoted to the analysis of failure time data. Competing risks must be taken into account in the study of how risk factors affect a specific cause of failure. In the past he studied two generalized Cox regression models in the competing risks setting. Another research interest is the use of Poisson regression and logistic regression in epidemiologic studies. This has resulted in a publication on Poisson regression.

Qi Zhao, PhD
Assistant Professor of Epidemiology, Preventive Medicine
Dr. Zhao’s research interests include genetic and molecular studies of cardiovascular disease and other aging-related disorders, such as osteoporosis and sarcopenia. She has conducted a significant amount of studies to investigate genetic factors for hypertension and blood pressure phenotypes. She has applied the cutting-edge metabolomics approach in body composition research and identified novel metabolites and metabolic pathways for bone mineral density, muscle mass and strength, and obesity. Her studies have been successfully funded by the American Heart Association and the NIH. She is currently working on the CANDLE study, a birth cohort at Memphis, to identify prenatal factors associated with childhood growth trajectories and obesity risk.

Zhu Wang, PhD
Professor of Biostatistics, Preventive Medicine
Dr. Wang received his BS in Applied Mathematics from Sichuan University, China, MS in Statistics from the University of Toledo, and PhD in Statistics from Southern Methodist University. He received post-doctoral training in biostatistics and predictive modeling at University of Connecticut School of Medicine and Fred Hutchison Cancer Research Center. He has been focusing on methodology development of predictive modeling techniques including variable selection and machine learning, as well as applications in genomic cancer research, biomarker research and healthcare risk profiling. He published variable selection methods in categorical data regression to predict hospital length of stay and doctor office visits. He produced innovative boosting algorithms for regression, classification and time-to-event problems. His methodology research has resulted in multiple statistical R packages in CRAN under machine learning and survival analysis task views. He is the principal investigator of an NIH-funded project to predict clinical outcomes of children with ulcerative colitis using multicenter electronic health records. Dr. Wang actively participates in collaborative research. He has served as a biostatistician and co-investigator on NIH grants. He supported early biomarkers discovery to detect acute kidney injury after cardiac surgery. As an internal reviewer, he has provided critiques for hundreds of biomedical study protocols on research methodology, study design and statistical analysis. He enjoys teaching biostatistics and machine learning courses. He is a recipient of Charles H. Hood Foundation Child Health Research Award.



Trish Goedecke, MS
Staff Statistician, Preventive Medicine
Trish Goedecke, MS, is a staff statistician with the Department of Preventive Medicine at the University of Tennessee Health Science Center. She earned an MS in Actuarial Science from Central Washington University (CWU) and one in Statistics at the University of Memphis (UM), and is currently studying toward a doctorate in Data Science and Engineering with the University of Tennessee at Knoxville (UTK). She had previously conducted analyses with CWU, Milliman Health Consulting and the Institute for Intelligent Systems at UM, specializing in mixed linear regression modeling. At UTHSC, Ms. Goedecke collaborates on research with the ob/gyn department and is providing analysis on a health disparities study. As a staff statistician, she is available for consulting on study design and statistical analyses.

Chris Goodell, MSc
Data Analyst, Center for Health System Improvement
Chris serves as a Data Analyst for the Center for Health System Improvement, College of Medicine at the University of Tennessee Health Science Center. He has significant experience working with Medicare and Medicaid claims data as well as EHR data, and also is familiar with publicly available databases including Medical Expenditure Panel Survey (MEPS) and National Ambulatory Medical Care Survey (NAMCS). He earned an MS in Analytics from Louisiana State University, with focus in multivariate statistics, data mining, and categorical data analysis, and has expertise in using SAS and R. Prior to his joining the Center for Health System Improvement, he worked with the Louisiana Department of Health performing data analysis for epidemiological research and pharmacy rebate program administration.

Tristan Hayes, MSc
BERD Consulting Manager, Preventive Medicine
Tristan Hayes is the Biostatistics, Epidemiology, and Research Design (BERD) consulting manager for TNCTSI at the University of Tennessee Health Science Center. He has a Masters in Epedimiology from the University of Tennessee Health Science Center. Tristan enjoys connecting investigators with the tools and resources they need to succeed in their research. He has extensive experience in litigation and economic consulting and “likes to get his hands dirty with data in SAS”.