Kevin’s research is focused on to identifying and interpreting the mechanisms responsible for the complex dynamics we observe in ecological and epidemiological systems using data science and modeling approaches. He is primarily interested in emerging and endemic pathogens, such as SARS-CoV-2, influenza, vampire bat rabies, and a host of childhood infectious diseases such as chickenpox. He uses statistical and mechanistic models to fit, forecast, and occasionally back-cast expected disease dynamics under a host of conditions, such as vaccination or other control mechanisms.
Biodiversity in nature can be puzzlingly high in the light of competition between species, which arguably should eventually result in a single winner. The coexistence mechanisms that allow for this biodiversity shape the dynamics of communities and ecosystems. My research focuses on understanding the mechanisms of competitive coexistence, how competition influences community structure and diversity, and what insights observed patterns of community structure might provide about competitive coexistence.
I am interested in the use and development of data science approaches to draw insights regarding coexistence mechanisms from the structural patterns of ecological communities with respect to species’ functional traits, relative abundance, spatial distribution, and phylogenetic relatedness, through as community dynamics proceed. I am also interested in the use of Maximum Likelihood and Bayesian approaches for fitting demographic models to forest census data sets, demographic models that can then be used to quantitatively assess the role of different competitive coexistence mechanisms.
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.
I am interested in how governance, communities, and inequality emerge in sociotechnical systems, and how the structure of sociotechnical systems encodes and reinforces these processes. To those ends, I develop empirical data and computational methods, focusing on latent variable models; statistical inference in networks; empirical design to study governance in organizations, platforms, and computational social systems; and causal inference and measurement in observational data.
Several sample projects:
> developing empirical populations of networks to infer social and ecological processes encoded in networks
> using probabilistic methods to infer the structure and dynamics of the illicit wildlife trade
> building from theory from political science, statistics, and education to disentangle issues of “bias” in computational systems
My research focuses on how environmental change, including climate, invasion and habitat destruction influences freshwater ecological communities across space and time. I am involved in a collaborative interdisciplinary project funded by a MIDAS Propelling Original Data Science (PODS) Grant: CHANGES: Collections, Heterogeneous data, And Next Generation Ecological Studies.We are developing protocols for integrating heterogeneous natural science datasets to investigate the impacts of environmental changes on species. Our project focuses on climate change impacts on inland lake fish communities across Michigan, drawing on more than a century’s worth of data and specimens archived at the University of Michigan Museum of Zoology (UMMZ) and the Institute for Fisheries Research (IFR), which is a cooperative unit of the Michigan Department of Natural Resources (DNR) Fisheries Division and the University of Michigan.
I am interested in the evolutionary processes that originate “mega-diverse” biotic assemblages and the role of ecology in shaping the evolution of diversity. My program studies the evolution of Neotropical freshwater fishes, the most diverse freshwater fish fauna on earth, with an estimate exceeding 7,000 species. My lab combines molecular phylogenetics and phylogeny-based comparative methods to integrate ecology, functional morphology, life histories and geography into analyses of macroevolutionary patterns of freshwater fish diversification. We are also comparing patterns of diversification across major Neotropical fish clades. Relying on fieldwork and natural history collections, we use methods that span
Carter’s research combines quantitative, theoretical, and field approaches to address challenging local to global wildlife conservation issues in the Anthropocene. His work includes projects on endangered species conservation in human-dominated areas of Nepal, post-war recovery of wildlife in Mozambique, human-wildlife coexistence in the American West, and the effects of artificial lights and human-made noise on wildlife habitat across the contiguous US. Research methods focus on: (1) spatializing both human and wildlife processes, (2) probabilistic methods to infer human-wildlife interactions (3) simulation models of coupled natural-human systems, and (4) forecasting and decision-support tools.
Andrea Thomer is an assistant professor of information at the University of Michigan School of Information. She conducts research in the areas of data curation, museum informatics, earth science and biodiversity informatics, information organization, and computer supported cooperative work. She is especially interested in how people use and create data and metadata; the impact of information organization on information use; issues of data provenance, reproducibility, and integration; and long-term data curation and infrastructure sustainability. She is studying a number of these issues through the “Migrating Research Data Collections” project – a recently awarded Laura Bush 21st Century Librarianship Early Career Research Grant from the Institute of Museum and Library Services. Dr. Thomer received her doctorate in Library and Information Science from the School of Information Sciences at the University of Illinois at Urbana‐Champaign in 2017.
Jeffrey Regier received a PhD in statistics from UC Berkeley (2016) and joined the University of Michigan as an assistant professor. His research interests include graphical models, Bayesian inference, high-performance computing, deep learning, astronomy, and genomics.
The long temporal and large spatial scales of ecological systems make controlled experimentation difficult and the amassing of informative data challenging and expensive. The resulting sparsity and noise are major impediments to scientific progress in ecology, which therefore depends on efficient use of data. In this context, it has in recent years been recognized that the onetime playthings of theoretical ecologists, mathematical models of ecological processes, are no longer exclusively the stuff of thought experiments, but have great utility in the context of causal inference. Specifically, because they embody scientific questions about ecological processes in sharpest form—making precise, quantitative, testable predictions—the rigorous confrontation of process-based models with data accelerates the development of ecological understanding. This is the central premise of my research program and the common thread of the work that goes on in my laboratory.