Dr. Amanda Kowalski

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Professor Kowalski’s recent research analyzes experiments and clinical trials with the goal of designing policies to target insurance expansions and medical treatments to individuals who stand to benefit from them the most. Her research has also explored the impact of previous Medicaid expansions, the Affordable Care Act, the Massachusetts health reform of 2006, and employer-sponsored health insurance plans. She has also used cutting-edge techniques to estimate the value of medical spending on at-risk newborns.



Lorraine Buis

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I conduct research on the use of consumer-facing technologies for chronic disease self management. My work predominantly centers on the use of mobile applications that collect and manage patient generated health data overt time.

Robert Ploutz-Snyder

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My work falls into three general application areas. I am an applied (accredited) biostatistician with a strong team science motivation and I collaborate with scientists in primarily the biomedical sciences, contributing expertise in experimental design, statistical analysis/modeling, and data visualization. I have held faculty appointments in Schools of Medicine and Nursing, and also worked as a senior scientist in the Human Research Program at the NASA Johnson Space Center. I currently direct an Applied Biostatistics Laboratory and Data Management Core within the UM School of Nursing, and maintain several collaborative research programs within the School, at NASA, and with collaborators elsewhere.

Jessica Golbus

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I am interested in using digital health technology for the treatment of cardiovascular disease with a particular emphasis on its application to patients with heart failure. More specific, my interests include (1) using non-invasive sensors and digital health technology to improve the delivery of cardiovascular care and (2) optimizing treatment for patients with advanced systolic heart failure through novel statistical tools and risk-modeling

Aditi Misra

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Transportation is the backbone of the urban mobility system and is one of the greatest sources of environmental emissions and pollutions. Making urban transportation efficient, equitable and sustainable is the main focus of my research. My students and I analyze small scale survey data as well as large scale spatiotemporal data to identify travel behavior trends and patterns at a disaggregate level using econometric methods, which we then scale up to the population level through predictive and statistical modeling. We also design our own data collection methods and instruments, be it a network of smart devices or stated preference experiments. Our expertise lies in identifying latent constructs that influence decisions and choices, which in turn dictate demands on the systems and subsystems. We use our expertise to design incentives and policy suggestions that can help promote sustainable and equitable multimodal transportation systems. Our team also uses data analytics, particularly classification and pattern recognition algorithms, to analyze crash context data and develop safety-critical scenarios for automated and connected vehicle (CAV) deployment. We have developed an online game based on such scenarios to promote safe shared mobility among teenagers and young adults and plan to expand research in that area. We are also currently expanding our research to explore the use of NN in context information synthesis.

This is a project where we used classification and Bayesian models to identify scenarios that are risky for pedestrians and bicyclists. We then developed an online game based on those scenarios for middle schoolers so that they are better prepared for shared road conflicts.

Nicole Seiberlich

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My research involves developing novel data collection strategies and image reconstruction techniques for Magnetic Resonance Imaging. In order to accelerate data collection, we take advantage of features of MRI data, including sparsity, spatiotemporal correlations, and adherence to underlying physics; each of these properties can be leveraged to reduce the amount of data required to generate an image and thus speed up imaging time. We also seek to understand what image information is essential for radiologists in order to optimize MRI data collection and personalize the imaging protocol for each patient. We deploy machine learning algorithms and optimization techniques in each of these projects. In some of our work, we can generate the data that we need to train and test our algorithms using numerical simulations. In other portions, we seek to utilize clinical images, prospectively collected MRI data, or MRI protocol information in order to refine our techniques.

We seek to develop technologies like cardiac Magnetic Resonance Fingerprinting (cMRF), which can be used to efficiently collect multiple forms of information to distinguish healthy and diseased tissue using MRI. By using rapid methods like cMRF, quantitative data describing disease processes can be gathered quickly, enabling more and sicker patients can be assessed via MRI. These data, collected from many patients over time, can also be used to further refine MRI technologies for the assessment of specific diseases in a tailored, patient-specific manner.

Kathleen Sienko

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Age- and sensory-related deficits in balance function drastically impact quality of life and present long-term care challenges. Successful fall prevention programs include balance exercise regimes, designed to recover, retrain, or develop new sensorimotor strategies to facilitate functional mobility. Effective balance-training programs require frequent visits to the clinic and/or the supervision of a physical therapist; however, one-on-one guided training with a physical therapist is not scalable for long-term balance training preventative and therapeutic programs. To enable preventative and therapeutic at-home balance training, we aim to develop models for automatically 1) evaluating balance and, 2) delivering personalized training guidance for community dwelling OA and people with sensory disabilities.

Smart Phone Balance Trainer

Shu-Fang Shih

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Shu-Fang Shih, Ph.D., has a diverse background in public health, business administration, risk management and insurance, and actuarial science. Her research has focused on design, implementation, and evaluation of theory-based health programs for children, adolescents, pregnant women, and older adults in various settings. In addition, she used econometric methods, psychometric, and other statistical methods to examine various health issues among children, adolescent, emerging adulthood, pregnant women, and the older adults. She is particularly interested in designing effective ways to align public health, social services, and healthcare to achieve the goal of family-centered and integrated/coordinated care for the family.

Veera Baladandayuthapani

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Dr. Veera Baladandayuthapani is currently a Professor in the Department of Biostatistics at University of Michigan (UM), where he is also the Associate Director of the Center for Cancer Biostatistics. He joined UM in Fall 2018 after spending 13 years in the Department of Biostatistics at University of Texas MD Anderson Cancer Center, Houston, Texas, where was a Professor and Institute Faculty Scholar and held adjunct appointments at Rice University, Texas A&M University and UT School of Public Health. His research interests are mainly in high-dimensional data modeling and Bayesian inference. This includes functional data analyses, Bayesian graphical models, Bayesian semi-/non-parametric models and Bayesian machine learning. These methods are motivated by large and complex datasets (a.k.a. Big Data) such as high-throughput genomics, epigenomics, transcriptomics and proteomics as well as high-resolution neuro- and cancer- imaging. His work has been published in top statistical/biostatistical/bioinformatics and biomedical/oncology journals. He has also co-authored a book on Bayesian analysis of gene expression data. He currently holds multiple PI-level grants from NIH and NSF to develop innovative and advanced biostatistical and bioinformatics methods for big datasets in oncology. He has also served as the Director of the Biostatistics and Bioinformatics Cores for the Specialized Programs of Research Excellence (SPOREs) in Multiple Myeloma and Lung Cancer and Biostatistics&Bioinformatics platform leader for the Myeloma and Melanoma Moonshot Programs at MD Anderson. He is a fellow of the American Statistical Association and an elected member of the International Statistical Institute. He currently serves as an Associate Editor for Journal of American Statistical Association, Biometrics and Sankhya.


An example of horizontal (across cancers) and vertical (across multiple molecular platforms) data integration. Image from Ha et al (Nature Scientific Reports, 2018; https://www.nature.com/articles/s41598-018-32682-x)