Biostatistics Seminars
The Department of Biostatistics at the University of Michigan is proud to invite leading scholars from around the world to visit Ann Arbor to share their expertise, wisdom and experience. All are welcome to attend these seminars, which are held in-person.
Erin Craig, PhD & Andrew Whiteman, PhD
Erin Craig
Assistant Professor of Biostatistics
University of Michigan
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Andrew Whiteman
Research Assistant Professor of Biostatistics
University of Michigan
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DATE: Thursday, September 18, 2025
TIME: 3:30 p.m.
LOCATION: 1690 SPH I
TITLE: Meet the New Michigan Biostatistics Faculty (Part 1)
Erin Craig's work centers on developing interpretable statistical and machine learning methods to advance cancer research and precision medicine. She has introduced innovative approaches such as survival stacking—recasting survival analysis as a classification problem—and has developed software tools for pretraining methods and treatment effect assessment. Her research applies these techniques to areas including CAR-T therapy, immune repertoire analysis, and clinical trial data, with the goal of improving predictive modeling and clinical decision-making. Dr. Craig earned her PhD in Biomedical Data Science from Stanford University in 2025 after completing both an MS in Data Science and a BA in Mathematics at the New College of Florida.
Andrew Whiteman specializes in Bayesian sparse-learning methods for high-dimensional data, particularly applied to structural and functional MRI analyses. His research encompasses imaging statistics, computational methodologies, and simulation modeling, including contributions to kidney paired donation systems. Prior to joining the department, Dr. Whiteman was a clinical data scientist at Gilead Sciences after earning his PhD ('22) and MS ('18) in Biostatistics here at Michigan. He completed a BA in Neuroscience at Boston University in 2012, where he continued on as a lab manager for the Cognitive Neuroimaging Laboratory.
Fan Li, PhD
Professor of Statistical Science
Professor of Biostatistics & Bioinformatics
University of California, Irvine
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DATE: Thursday, September 25, 2025
TIME: 3:30 p.m.
LOCATION: 1690 SPH I
TITLE: Sample size and power calculations for causal inference in observational studies
ABSTRACT: This paper investigates the theoretical foundation and develops analytical formulas for sample size and power calculations for causal inference with observational data. By analysing the variance of the inverse probability weighting estimator of the average treatment effect, we decompose the power calculations into three components: propensity score distribution, potential outcome distribution, and their correlation. We show that to determine the minimal sample size of an observational study, it is sufficient under mild conditions to have two parameters additional to the standard inputs in the power calculation of randomised trials, which quantify the strength of the confounder-treatment and the confounder-outcome association, respectively. For the former, we propose using the Bhattacharyya coefficient, which measures the covariate overlap and, together with the treatment proportion, leads to a uniquely identifiable and easily computable propensity score distribution. For the latter, we propose a sensitivity parameter bounded by the R-squared statistic of the regression of the outcome on covariates. Utilising the Lyapunov Central Limit Theorem on the linear combination of covariates, our procedure does not require distributional assumptions on the multivariate covariates. We develop an associated R package PSpower.
TOPICS: Causal Inference
Bret Hanlon, PhD & Emily Hector, PhD
Bret Hanlon
Associate Director of Clinical Trials, SABER
Research Assiociate Professor of Biostatistics
University of Michigan
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Emily Hector
Associate Professor of Biostatistics
University of Michigan
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DATE: Thursday, October 2, 2025
TIME: 3:30 p.m.
LOCATION: 1690 SPH I
TITLE: Meet the New Michigan Biostatistics Faculty (Part 2)
Bret Hanlon develops methods for high-dimensional inference, robust estimation, and complex longitudinal data, addressing challenges like outcome heterogeneity and informative visitation. He applies these tools to clinical trial design—particularly cluster and stepped-wedge trials—and analytic support for multi-site studies. His work spans oncology, cardiology, bariatric surgery, and critical care, with recent projects using machine learning for risk prediction and supporting shared decision-making in surgical care.
Emily Hector's research interests lie in distributed estimation and inference for large-scale biomedical data, emphasizing methods for correlated data, data integration, and high-dimensional analysis. She applies these methodologies to areas such as brain imaging, metabolomics, spatial data, and wearable device analytics.
Shuangge (Steven) Ma, PhD
Department Chair and Professor of Biostatistics
Director, Biostatistics and Bioinformatics Shared Resource
Yale University
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DATE: Thursday, October 9, 2025
TIME: 3:30 p.m.
LOCATION: 1690 SPH I
TITLE: TBD
ABSTRACT: TBD
TOPICS: TBD
Ying Yuan, PhD '05
Bettyann Asche Murray Distinguished Professor of Biostatistics
Chair of Biostatistics, Division of Discovery Science
Deputy Chair of Biostatistics
Chief, Section of Adaptive Clinical Trials
University of Texas MD Anderson Cancer Center
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DATE: Thursday, October 16, 2025
TIME: 3:30 p.m.
LOCATION: 1690 SPH I
TITLE: TBD
ABSTRACT: TBD
TOPICS: TBD
Gabriel Chen, PhD
Associate Professor, Statistics & Data Science
Hope College
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DATE: Thursday, October 30, 2025
TIME: 3:30 p.m.
LOCATION: 1690 SPH I
TITLE: TBD
ABSTRACT: TBD
TOPICS: TBD
Yingqi Zhao, PhD
Professor, Biostatistics
Member, Translational Data Science Integrated Research Center (TDS IRC)
Fred Hutchinson Cancer Center
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DATE: Thursday, November 13, 2025
TIME: 3:30 p.m.
LOCATION: 1690 SPH I
TITLE: TBD
ABSTRACT: TBD
TOPICS: TBD
Zhonghua Liu, ScD
Assistant Professor of Biostatistics
Columbia University
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DATE: Thursday, November 20, 2025
TIME: 3:30 p.m.
LOCATION: 1690 SPH I
TITLE: TBD
ABSTRACT: TBD
TOPICS: TBD