Our faculty are consistently contributing to the national conversation through their research.

Faculty and students in the Division of Biostatistics are active in many areas of methodological and collaborative research, and regularly publish in high-impact statistics, biostatistics, and bio-medical journals.

Bayesian analysis: James Hodges, Joseph Koopmeiners, Thomas Murray, Cavan Reilly, Lianne Siegel, Eric Lock, Lin Zhang

Causal inference: Jared Huling, Joseph Koopmeiners, Chap Le, Thomas Murray, Wei Pan, David Vock, Julian Wolfson, Baolin Wu, Tianzhong Yang

Clinical trials: Anne Eaton, Lynn Eberly, Birgit Grund, Erika Helgeson, James Hodges, Joseph Koopmeiners, Chap Le, Xianghua Luo, Andrew Mugglin, Thomas Murray, James Neaton, Cavan Reilly, Lianne Siegel, Kyle Rudser, David Vock

Statistical genetics & computational biology: Saonli Basu, Weihua Guan, Eric LockWei Pan, Cavan Reilly, Sandra Safo, Baolin Wu, Tianzhong Yang, Lin Zhang

Screening & diagnostic testing: Lynn Eberly, Joseph Koopmeiners, Chap Le

Machine learning: Saonli Basu, Lynn Eberly, Erika Helgeson, Jared HulingEric LockWei Pan, Ashley Petersen, Cavan Reilly, Sandra Safo, David Vock, Julian Wolfson

Medical imaging: Lynn Eberly, Mark Fiecas, Joseph Koopmeiners, Baolin Wu, Lin Zhang

Meta-analysis: James Hodges, Lianne Siegel, Tianzhong Yang

Spatial statistics: Mark Fiecas, James Hodges, Lin Zhang

Survival analysis: Anne Eaton, Chap Le, Xianghua Luo, Thomas Murray, Kyle Rudser, David Vock

We pride ourselves on getting students involved in research early and often. Masters students complete a research-oriented Plan B project in collaboration with a faculty adviser. PhD students are involved in research assistantships starting from their first semester, and typically identify a dissertation adviser during the second year of the program. Dissertations follow the 3-paper model, so our students often graduate with multiple peer-reviewed publications.

Recent Student Project and Dissertation Titles:

  • Grace Lyden, “Policy-Relevant Causal Effect Estimators for Heterogeneous Treatments and Exposures”
  • Chuyu Deng, “Innovative Methods for Treatment Effect Heterogeneity & Calibration”
  • Yuan Zhang, “Modifications of Q-learning to Optimize Dynamic Treatment Regimes”
  • Bin Guo, “Integrative Statistical Methods in Genomics and Neuroimaging”
  • Shannon McKearnan, “Statistical Methods for Organ Transplant”
  • Charles Cain, “Statistical Considerations for Clinical Trials Aiming to Identify Individualized Treatment Rules”
  • Andrew DiLernia, “New Estimation and Inferential Methods for Functional Connectivity Analysis”
  • Alex Knutson, “Integrating Summarized Imaging and Genomic Data with GWAS for Powerful Endophenotype Association Testing in Alzheimer’s Disease”
  • Lianne Siegel, “Estimating the Reference Range from a Meta-Analysis using Aggregate or Individual Participant Data”
  • Adam Kaplan, “Context-Driven Prior Distributions in Genome–Wide Association Studies, Medical Device Adaptive Clinical Trials, and Genetic Fine-Mapping”

Biostatistics working groups are intended for University of Minnesota students and faculty and focus on various areas of faculty research.  This is an opportunity for collaboration, peer-to-peer mentoring, and networking between student cohorts. If you are interested in participating in any of these working groups, please contact the working group leads below.

Active Working Groups

Title: Biostats for High-Dimensional Data Working Group

Faculty Lead(s): Ashley Petersen and Sandra Safo

Tentative meeting time: Second and fourth Fridays of every month, 1:15-2:15 pm

Description: The purpose of this working group is to discuss biostatistical methods for analyzing high-dimensional data (i.e., a large number of variables relative to the number of observations). During each meeting, one or two student members present on their own ongoing research or a journal article in this area. We spend the last meeting of the semester discussing a career development book chosen by the group.

Title: Comparative Effectiveness and Research Synthesis (CERS)

Faculty Lead(s): Lianne Siegel and James Hodges

Tentative meeting time: Fridays , biweekly 2:30-3:30 pm, starting September 23, 2022

Description: The mission of our working group is to develop and implement statistical methods to aid in research synthesis and comparative effectiveness research and to foster collaboration among UMN students and faculty conducting research in these areas. Our members develop statistical methods across many areas including multivariate and network meta-analysis, meta-analysis of diagnostic tests, causal inference in meta-analysis, and meta-analysis of normative data from both observational studies and randomized clinical trials. We are also interested in bridging the gap between statistical theory and practice (i.e. translational biostatistics) by publishing peer-reviewed high impact manuscripts that translate recent methodological advances to a clinical and epidemiological audience. We meet regularly to share our current work and discuss journal articles.

Title: Innovative Trial Design and Causal Inference Working Group

Faculty Lead(s): Joe Koopmeiners and Jared Huling

Tentative meeting time: Every Thursday, 10-11 am

Description: The purpose of this working group is to discuss recent advancements and ongoing research in the division related to innovative clinical trial design and causal inference. Topics to be discussed include Bayesian adaptive methods in clinical trials, master protocols (platform trials, basket trials, etc.), SMARTs/adaptive intervention designs, and other topics related to innovative clinical trial designs and causal inference.

Title: MERGE (Mobile & E-Health Research Group)

Faculty Lead(s): Julian Wolfson and Marquis Hou

Tentative meeting time: Every two weeks starting Tuesday, Sept 13 at 10 am

Description: The MERGE working group is focused on the applications of statistics to mobile and electronic health data, including data collected from smartphones, wearable sensors, and administrative health databases (EHR). Methods for these data are diverse and developing, but include machine learning, causal inference, and functional data analysis. Group meetings are open to students at any stage of their program, and will include presentation of ongoing projects, discussion of research papers, and tutorials on relevant technologies and skills.

Title: Spatio-temporal Modeling Group

Faculty Lead(s): Mark Fiecas

Tentative meeting time: Will resume in Spring 2023

Description: This group is largely driven by the students, with a student leading each meeting on a topic of their choice. In previous years, students led discussions of preliminary results for ongoing research projects, professional development, and discussions on a paper that they found interesting. The research topic is often in the context of spatial/temporal modeling – last year’s topics varied from brain imaging to art. This group can be a great way for you to be exposed to and connect with others interested in this area of research.

Title: Statistical Genetics/Omics Journal Club

Faculty Lead(s): Saonli Basu

Tentative meeting time: Fridays, biweekly, 3-4 pm, Fall 2022

Description: The focus of the journal club is to introduce you to methodological developments and applications of genomics/omics in different scientific domains.  Each semester we will choose a topic and have presentations from students and faculty about their research,  discuss the University and outside resources to work in that field. This journal club is supported by the training grant `Interdisciplinary Biostatistics Training in Genetics and Genomics’. See the following webpage for more information:

View Omics Journal Club website

Title: Survival Analysis Working Group

Faculty Lead(s): Anne Eaton and Xianghua Luo

Tentative meeting time: 2nd and 4th Fridays, 10-11 am

Description: We will meet to discuss biostatistical methods for time-to-event data, including discussing papers, sharing ongoing research and attending and discussing webinars.

View Survival Analysis Working Group website

Title: Teaching and Communication

Faculty Lead:  Ann Brearley

Tentative Meeting Time: To be determined


This group is primarily student-driven, with a student leading each meeting on a topic of their choice. Meetings will be scheduled for 2022-2023 if there is sufficient student interest in leading the meetings. If you are interested, contact Ann Brearley.

We could focus on a range of topics potentially including teaching methods, anti-racism in teaching, course development, curriculum development, and effective communication with non-statisticians. The focus may shift from semester to semester to follow the interests of the participants. Talks could include reviewing and discussing journal articles, sharing students’ experiences with teaching and outreach, practicing teaching activities, presenting teaching-related research in progress, practicing talks for professional meetings, etc.

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