Anne Fernandez

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Dr. Fernandez is a clinical psychologist with extensive training in both addiction and behavioral medicine. She is the Clinical Program Director at the University of Michigan Addiction Treatment Service. Her research focuses on the intersection of addiction and health across two main themes: 1) Expanding access to substance use disorder treatment and prevention services particularly in healthcare settings and; 2) applying precision health approaches to addiction-related healthcare questions. Her current grant-funded research includes an NIH-funded randomized controlled pilot trial of a preoperative alcohol intervention, an NIH-funded precision health study to leverage electronic health records to identify high-risk alcohol use at the time of surgery using natural language processing and other machine-learning based approaches, a University of Michigan funded precision health award to understand and prevent new persistent opioid use after surgery using prediction modeling, and a federally-funded evaluation of the state of Michigan’s substance use disorder treatment expansion.

Andrew Brouwer

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Andrew uses mathematical and statistical modeling to address public health problems. As a mathematical epidemiologist, he works on a wide range of topics (mostly related to infectious diseases and cancer prevention and survival) using an array of computational and statistical tools, including mechanistic differential equations and multistate stochastic processes. Rigorous consideration of parameter identifiability, parameter estimation, and uncertainty quantification are underlying themes in Andrew’s work.

Nicholas Henderson

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My research primarily focuses on the following main themes: 1) development of methods for risk prediction and analyzing treatment effect heterogeneity, 2) Bayesian nonparametrics and Bayesian machine learning methods with a particular emphasis on the use of these methods in the context of survival analysis, 3) statistical methods for analyzing heterogeneity in risk-benefit profiles and for supporting individualized treatment decisions, and 4) development of empirical Bayes and shrinkage methods for high-dimensional statistical applications. I am also broadly interested in collaborative work in biomedical research with a focus on the application of statistics in cancer research.

Rahul Ladhania

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Rahul Ladhania is an Assistant Professor of Health Informatics in the Department of Health Management & Policy at the University of Michigan School of Public Health. He also has a secondary (courtesy) appointment with the Department of Biostatistics at SPH. Rahul’s research is in the area of causal inference and machine learning in public and behavioral health. A large body of his work focuses on estimating personalized treatment rules and heterogeneous effects of policy, digital and behavioral interventions on human behavior and health outcomes in complex experimental and observational settings using statistical machine learning methods.

Rahul co-leads the Machine Learning team at the Behavior Change For Good Initiative (Penn), where he is working on two `mega-studies’ (very large multi-arm randomized trials): one in partnership with a national fitness chain, to estimate the effects of behavioral interventions on promoting gym visit habit formation; and the other in partnership with two large Mid-Atlantic health systems and a national pharmacy chain, to estimate the effects of text-based interventions on increasing flu shot vaccination rates. His other projects involve partnerships with step-counting apps and mobile-based games to learn user behavior patterns, and design and evaluate interventions and their heterogeneous effects on user behavior.

Gary Luker

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We use a variety of quantitative imaging methods, ranging from single cells to clinical studies, to investigate cancer signaling and response to therapy over space and time. We develop image analysis methods to extract data from thousands of single cells over time and voxel-wise measurements of imaging parameters. We also use bulk and single-cell RNA sequencing to investigate heterogeneity among cancer cells and changes induced by intercellular interactions. A current goal of our ongoing work is to merge RNA sequencing and imaging data to understand cell decision making in cancer. We collaborate with investigators using machine learning and computational modeling approaches to inform cell signaling and resultant behaviors in tumor growth and metastasis.

Bogdan I. Epureanu

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• Computational dynamics focused on nonlinear dynamics and finite elements (e.g., a new approach for forecasting bifurcations/tipping points in aeroelastic and ecological systems, new finite element methods for thin walled beams that leads to novel reduced order models).
• Modeling nonlinear phenomena and mechano-chemical processes in molecular motor dynamics, such as motor proteins, toward early detection of neurodegenerative diseases.
• Computational methods for robotics, manufacturing, modeling multi-body dynamics, developed methods for identifying limit cycle oscillations in large-dimensional (fluid) systems.
• Turbomachinery and aeroelasticity providing a better understanding of fundamental complex fluid dynamics and cutting-edge models for predicting, identifying and characterizing the response of blisks and flade systems through integrated experimental & computational approaches.
• Structural health monitoring & sensing providing increased sensibility / capabilities by the discovery, characterization and exploitation of sensitivity vector fields, smart system interrogation through nonlinear feedback excitation, nonlinear minimal rank perturbation and system augmentation, pattern recognition for attractors, damage detection using bifurcation morphing.

Thomas Schmidt

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The current goal of our research is to learn enough about the physiology and ecology of microbes and microbial communities in the gut that we are able to engineer the gut microbiome to improve human health. The first target of our engineering is the production of butyrate – a common fermentation product of some gut microbes that is essential for human health. Butyrate is the preferred energy source for mitochondria in the epithelial cells lining the gut and it also regulates their gene expression.

One of the most effective ways to influence the composition and metabolism of the gut microbiota is through diet. In an interventional study, we have tracked responses in the composition and fermentative metabolism of the gut microtiota in >800 healthy individuals. Emerging patterns suggest several configurations of the microbiome that can result in increased production of butyrate acid. We have isolated the microbes that form an anaerobic food web to convert dietary fiber to butyrate and continue to make discoveries about their physiology and interactions. Based on these results, we have initiated a clinical trial in which we are hoping to prevent the development of Graft versus Host Disease following bone marrow transplants by managing butyrate production by the gut microbiota.

We are also beginning to track hundreds of other metabolites from the gut microbiome that may influence human health. We use metagenomes and metabolomes to identify patterns that link the microbiota with their metabolites and then test those models in human organoids and gnotobiotic mice colonized with synthetic communities of microbes. This blend of wet-lab research in basic microbiology, data science and in ecology is moving us closer to engineering the gut microbiome to improve human health.

Carlos Aguilar

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The Aguilar group is focused understanding transcriptional and epigenetic mechanisms of skeletal muscle stem cells in diverse contexts such as regeneration after injury and aging. We focus on this area because there are little to no therapies for skeletal muscle after injury or aging. We use various types of in-vivo and in-vitro models in combination with genomic assays and high-throughput sequencing to study these molecular mechanisms.

Xu Shi

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My methodological research focus on developing statistical methods for routinely collected healthcare databases such as electronic health records (EHR) and claims data. I aim to tackle the unique challenges that arise from the secondary use of real-world data for research purposes. Specifically, I develop novel causal inference methods and semiparametric efficiency theory that harness the full potential of EHR data to address comparative effectiveness and safety questions. I develop scalable and automated pipelines for curation and harmonization of EHR data across healthcare systems and coding systems.