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.
Weining Shen
Associate Professor of Statistics
University of California, Irvine
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DATE: Thursday, September 10, 2026
TIME: 3:30 p.m.
LOCATION: SPH I, Room 1690
TITLE: Statistical Analysis of 3D Path Data in Minecraft
ABSTRACT: Understanding spatial navigation and memory formation is critical to exploring how humans learn and adapt in complex environments. To investigate these processes, we conducted an experiment using the Minecraft Memory and Navigation Task, collecting detailed 3D path data in a virtual open-world setting. Statistically, we developed a novel methodology to convert complex high-dimensional 3D movement data into functional representations, enabling standardized comparisons and analyses across participants and environments. We applied techniques such as functional clustering and regression to identify navigation patterns and their relationships with cognitive map development and memory retention. Our analysis uncovered two significant insights: first, participants who adopted moderately exploratory behaviors during training demonstrated superior retention of object locations; second, inefficient navigation strategies were strongly linked to poorer spatial memory and navigation performance. These findings highlight the effectiveness of our methodology in advancing the study of navigation behaviors and cognitive processes in dynamic 3D environments.
TOPICS: Artificial Intelligence, Computational Statistics, High-Dimensional Data, Longitudinal / Correlated Data, Machine Learning, Predictive Modeling
Dacheng Liu
Biostatistician
Boehringer Ingelheim
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DATE: Thursday, September 24, 2026
TIME: 3:30 p.m.
LOCATION: SPH I, Room 1690
TITLE: Clinical drug development: challenges and data science applications
ABSTRACT: Drug development is a highly complex process involving substantial investment and long cycle time in a complex regulatory and commercial environment. Pharma R&D has been facing major challenges in the past decades, due to e.g. substantial increase of drug development cost, high failure rate, high health economics pressures and regulatory hurdles. With the advancement of AI and machine learning, coupled with the availability of multiple sources of data including real world data, AI and data science have the potential of improving the productivity of Pharma R&D, particularly in evidence generation of clinical drug development. In this talk we will go through the challenges of clinical drug development and provide some examples of data science applications, such as treatment compliance, disease modeling, patient screening etc.
TOPICS: Artificial Intelligence, Clinical Trials, Data Integration, Machine Learning, Clinical Drug Development
Zhenhua Lin
Associate Professor and Presidential Young Professor of Statistics and Data Science
National University of Singapore
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DATE: Thursday, October 15, 2026
TIME: 3:30 p.m.
LOCATION: SPH I, Room 1690
TITLE: Regression with Complex Data Objects: Geometry and Biomedical Applications
ABSTRACT: Modern biomedical studies increasingly generate complex data objects such as covariance and connectivity matrices, probability distributions, and other observations that naturally reside in nonlinear metric spaces, where standard regression methods based on vector operations are no longer directly applicable. In this talk, I will consider regression from two complementary perspectives. First, when the response is a metric-space-valued object indexed by a scalar predictor such as time, I will introduce total-variation-regularized Fréchet regression, which provides an intrinsic way to recover piecewise-constant structure while respecting the geometry of the response space; an application to task-related fMRI data illustrates its use for detecting changes in dynamic functional connectivity. I will then consider the reverse setting, where the response is binary but the covariate is a metric-space-valued object. Motivated by classical logistic regression, we construct a geometrically defined logistic regression model using distances and angles between geodesics, and apply it to functional-connectivity matrices from the Human Connectome Project to distinguish motor and language-processing tasks. Together, these results illustrate how familiar regression ideas can be extended beyond Euclidean spaces while retaining interpretability and relevance to complex biomedical data.
TOPICS: Imaging, Longitudinal / Correlated Data, Nonparametric / Semiparametric Modeling, Predictive Modeling, Stochastic Models, Brain Functional Connectivity, Complex Data Analysis
Rhonda Bacher
Associate Professor of Biostatistics
University of Florida
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DATE: Thursday, October 22, 2026
TIME: 3:30 p.m.
LOCATION: SPH I, Room 1690
TITLE: To be announced
ABSTRACT: To be announced
TOPICS: To be announced
Rob Tibshirani
Professor of Biomedical Data Science and Statistics
Stanford University
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DATE: Thursday, October 29, 2026
TIME: 3:30 p.m.
LOCATION: SPH I, Room 1690
TITLE: UniLasso: a review and some new developments
ABSTRACT: To be announced
TOPICS: Cancer Research, Computational Statistics, Genetics Research, Genomics Research
Yufeng Liu
John D MacArthur Professor and Professor of Statistics
University of Michigan
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DATE: Thursday, November 05, 2026
TIME: 3:30 p.m.
LOCATION: SPH I, Room 1690
TITLE: To be announced
ABSTRACT: To be announced
TOPICS: To be announced
Matthew McCall
Associate Professor of Biostatstics, Computational Biology, and Biomedical Genetics
University of Rochester Medicine
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DATE: Thursday, November 12, 2026
TIME: 3:30 p.m.
LOCATION: SPH I, Room 1690
TITLE: Statistical methods for microRNA sequencing data
ABSTRACT: MicroRNAs are a class of small (18-24 nucleotide) RNAs that are essential regulators of gene expression, which act within the RNA-induced silencing complex (RISC) to bind mRNAs and suppress translation. Alterations in microRNA expression have been shown to disrupt entire cellular pathways, substantially contributing to a variety of human diseases. Despite nearly 25 years of research, microRNAs remain difficult to measure due to their short length, relatively small number, sequence similarity, and difficulty to isolate from other small RNA fragments. The majority of recent studies use small RNA-seq (also called microRNA-seq) to quantify microRNA expression because it allows for the quantification of isomiRs (microRNA isoforms) and the possibility of identifying novel microRNAs. Statistical analyses of microRNA-seq data are typically performed using methods developed for mRNA-seq data despite the fact that microRNA-seq data violate several of the assumptions of these methods. We propose new statistical methods for preprocessing and analysis of microRNA-seq data that are tailored to the specific complexities of these data.
TOPICS: Bayesian Statistics, Bioinformatics, Genomics Research, High-Dimensional Data, Machine Learning
Rebecca Andridge
Professor of Biostatistics; Associate Dean for Undergraduate Studies at the College
of Public Health
The Ohio State University
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DATE: Thursday, December 03, 2026
TIME: 3:30 p.m.
LOCATION: SPH I, Room 1690
TITLE: To be announced
ABSTRACT: To be announced
TOPICS: To be announced