Aihua Bian's research interests include: time to event analysis, clinical trials, competing risk event analysis, categorical data analysis, repeated measurements analysis, and observational studies.

Joshua DeClercq works with the Vanderbilt Specialty Pharmacy and the Department of Physical Medicine and Rehabilitation. His current projects include developing a standard measure of medication adherence and a clinical trial for rotator cuff tear treatment. 

Tan Ding's research interest include: time to event analysis, mixed effects model, categorical data, power analysis and sample size calculation.

Henry Domenico has maintained and grown a successful collaboration with the Vanderbilt Department of Quality, Safety and Risk Prevention and the Center for Patient and Professional Advocacy. He is an expert in using predictive modeling, machine learning, and artificial intelligence to improve care for patients. He has conducted several pragmatic randomized controlled trials as part of Vanderbilt's Learning Healthcare System.

Sarah Feng’s research focuses on longitudinal data analysis, survival data analysis, categorical data, statistical analysis with missing data and mixed effects model. Her primary collaboration is with the Department of Anesthesiology.

Wu Gong's research focuses on clinical, epidemiological, and health policy outcomes; study design for clinical trial and cluster randomized trials; survey design; data analysis and causal inference for observational study.

Kim Hart is a biostatistician at Vanderbilt University Medical Center in Nashville, TN. She supports a number of research studies, primarily in emergency medicine and critical care settings. She has extensive expertise in study design and data management for large clinical trials and for FDA regulated research. 

Ethan Huang applies statistical learning methods to solve clinical trials and problems, such as variable selection using the shrinkage methods (LASSO, elastic net and ridge regression). He is also interested in mixed effect model, survival data analysis, and longitudinal data analysis.

Cathy Jenkins collaborates with physician researchers in Infectious Disease and Emergency Medicine.  Her work with Infectious Disease is focused on understanding the HIV epidemic using traditional methods of analysis for observational data as well as more current methods such as marginal structural models.  Her work with Emergency Medicine is focused primarily on heart failure using prediction models to aid physicians in its management in acute care settings.

Omair Khan is a biostatistician at Vanderbilt University Medical Center in Nashville, TN. His primary collaboration is with the Vanderbilt Memory & Alzheimer's Center, a talented team of clinicians and scientists who help advance the understanding of risk factors, early diagnostic markers, and prevention and treatment methods for memory loss and unhealthy cognitive aging.  Omair is particularly interested in developing solutions to minimize the challenges associated with collaborations between statisticians and other scientists.
 

Ahra Kim is currently involved in the following collaborations: Carribbean, Central, and South America network for HIV Epidemiology (CCASAnet); Department of Psychiatry at VUMC; and the Center for Biomedical Ethics and Society at VUMC.
 

Olivia Mason joined the Vanderbilt University Medical Center as a biostatistician in 2018 and is currently collaborating with researchers in the Vanderbilt Center for Kidney Disease. With a background in mathematics and statistics, she enjoys applying her skills to medicine and health sciences, helping clinicians and researchers understand their data to improve practice.

Elizabeth McNeer is a biostatistician collaborating with researchers in the Vanderbilt Center for Child Health Policy.

Jennifer Morse is a biostatistician at Vanderbilt University Medical Center supporting various research projects. Her interests include health policy, survival analysis and reproducible research. Currently her collaborations include the Vanderbilt Center for Kidney Disease and Anesthesiology.

Samuel Nwosu is a biostatistician with a primary collaboration on the International Study of Comparative Health Effectiveness with Medical and Invasive Approaches (ISCHEMIA) clinical trial group.  His research interests includes: observational studies, time to event analysis, clinical trials, competing risk event analysis and Categorical data analysis.

Molly Olson is a biostatistician on the American Hernia Society Quality Collaborative (AHSQC) Biostatistics and Data Analytics team that supplies crucial information and analyses that answer the wide range of questions raised by its stakeholders and for quality improvement. 

Onur M. Orun is a biostatistician at Vanderbilt University Medical Center (VUMC). Onur collaborates with the VUMC Critical Illness, Brain Dysfunction and Survivorship (CIBS) Center, a top-notch multidisciplinary research team that aims to positively impact the lives of critically ill patients and their families through rigorous, cutting-edge and reproducible clinical research.

Yaping Shi's research interests include: prediction model, categorical data, mixed effects model, time to event data, repeated measures and longitudinal data.

Li Wang is interested in clinical prediction models, pragmatic clinical trials, statistical computing, mixed-effects models, and time-to-event analysis. Her current research role is to work as a member of the Vanderbilt Institute for Clinical and Translational Research (VICTR) to provide statistical expertise with respect to study design, data collection, implementation, analysis, and publication of diverse research projects.

Siwei Zhang is interested in high dimensional data analysis, especially in applying the statistical models and machine learning algorithms to genetic and EHR research. Specifically, she now focuses on GWAS, PheWAS studies and analyzing copy number variation using SNP Array data.

Alex Zhao is a senior biostatistician in the Department of Biostatistics at Vanderbilt University Medical Center. He has expertise in the following areas: prediction modeling, linear regression, generalized linear regression, categorical data analyses, repeated measures, mixed-effects models, survival analysis, clinical trials, power and sample size justification, statistical computing, microarray data analysis, polymerase chain reaction (PCR) data analysis, machine learning, and data mining. In addition, Alex is proficient in the use of R and SAS and has considerable experience in conducting and analyzing data from molecular and genetic epidemiology studies of cancer. He has been working on genetic studies from large consortia including Breast Cancer Association Consortium, Ovarian Cancer Association Consortium, and Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome.