Dan Rabosky

Dan Rabosky

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The Rabosky lab seeks to understand how and why life on Earth became so diverse. We focus primarily on large-scale patterns of species diversification (speciation and extinction) and on the tempo and mode of phenotypic evolution, to better understand what regulates the “amount” of biodiversity through Deep Time. To this end, we develop theoretical frameworks and computational tools for studying evolutionary dynamics using DNA-sequence-based evolutionary trees (phylogenies), the fossil record, as well as phenotypic data from present-day species (morphology, ecology). We develop and apply a range of methods involving supervised and unsupervised learning, including Markov chain Monte Carlo, hierarchical mixture models, hidden Markov models, latent feature models, and more. We are increasingly interested in complex morphological and ecological traits, which – due to a rapidly expanding data universe – represent a tremendous opportunity for the field to answer long-standing questions about how organisms evolve. At these same time, we are embracing the analytical challenges of these data, because fully realizing their potential requires the development of new analytical paradigms that go beyond the limitations of traditional parametric models for low-dimensional data.

Automatic feature identification from a large-scale evolutionary tree (phylogeny) using a compound model of the generating process (speciation, extinction) developed in the Rabosky lab. Colors correspond to distinct evolutionary rate regimes as estimated using Markov chain Monte Carlo. This method revealed widespread heterogeneity in the rate of species formation during 350 million years of ray-finned fish evolution. Warm colors = fast rates; cool colors = slow rates.

Automatic feature identification from a large-scale evolutionary tree (phylogeny) using a compound model of the generating process (speciation, extinction) developed in the Rabosky lab. Colors correspond to distinct evolutionary rate regimes as estimated using Markov chain Monte Carlo. This method revealed widespread heterogeneity in the rate of species formation during 350 million years of ray-finned fish evolution. Warm colors = fast rates; cool colors = slow rates.

Photograph of Nate Sanders

Nate Sanders

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My research interests are broad, but generally center on the causes and consequences of biodiversity loss at local, regional, and global scales with an explicit focus on global change drivers. Our work has been published in Science, Nature, Science Advances, Global Change Biology, PNAS, AREES, TREE, and Ecology Letters among other journals. We are especially interested in using AI and machine learning to explore broad-scale patterns of biodiversity and phenotypic variation, mostly in ants.

Xiaoquan William Wen

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Xiaoquan (William) Wen is an Associate Professor of Biostatistics. He received his PhD in Statistics from the University of Chicago in 2011 and joined the faculty at the University of Michigan in the same year. His research centers on developing Bayesian and computational statistical methods to answer interesting scientific questions arising from genetics and genomics.

In the applied field,  he is  particularly interested in seeking statistically sound and computationally efficient solutions to scientific problems in the areas of genetics and functional genomics.
Quantifying tissue-specific expression quantitative trait loci (eQTLs) via Bayesian model comparison

Quantifying tissue-specific expression quantitative trait loci (eQTLs) via Bayesian model comparison

Brendan Kochunas

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Dr. Kochunas’s research focus is on the next generation of numerical methods and parallel algorithms for high fidelity computational reactor physics and how to leverage these capabilities to develop digital twins. His group’s areas of expertise include neutron transport, nuclide transmutation, multi-physics, parallel programming, and HPC architectures. The Nuclear Reactor Analysis and Methods (NURAM) group is also developing techniques that integrate data-driven methods with conventional approaches in numerical analysis to produce “hybrid models” for accurate, real-time modeling applications. This is embodied by his recent efforts to combine high-fidelity simulation results simulation models in virtual reality through the Virtual Ford Nuclear Reactor.

Relationship of concepts for the Digital Model, Digital Shadow, Digital Twin, and the Physical Asset using images and models of the Ford Nuclear Reactor as an example. Large arrows represent automated information exchange and small arrows represent manual data exchange.

Ivy F. Tso

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My lab researches how the human brain processes social and affective information and how these processes are affected in psychiatric disorders, especially schizophrenia and bipolar disorder. We use behavioral, electrophysiological (EEG), neuroimaging (functional MRI), eye tracking, brain stimulation (TMS, tACS), and computational methods in our studies. One main focus of our work is building and validating computational models based on intensive, high-dimensional subject-level behavior and brain data to explain clinical phenomena, parse mechanisms, and predict patient outcome. The goal is to improve diagnostic and prognostic assessment, and to develop personalized treatments.

Brain activation (in parcellated map) during social and face processing.

Meha Jain

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​I am an Assistant Professor in the School for Environment and Sustainability at the University of Michigan and am part of the Sustainable Food Systems Initiative. My research examines the impacts of environmental change on agricultural production, and how farmers may adapt to reduce negative impacts. I also examine ways that we can sustainably enhance agricultural production. To do this work, I combine remote sensing and geospatial analyses with household-level and census datasets to examine farmer decision-making and agricultural production across large spatial and temporal scales.

Conducting wheat crop cuts to measure yield in India, which we use to train algorithms that map yield using satellite data

Wenbo Sun

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Uncertainty quantification and decision making are increasingly demanded with the development of future technology in engineering and transportation systems. Among the uncertainty quantification problems, Dr. Wenbo Sun is particularly interested in statistical modelling of engineering system responses with considering the high dimensionality and complicated correlation structure, as well as quantifying the uncertainty from a variety of sources simultaneously, such as the inexactness of large-scale computer experiments, process variations, and measurement noises. He is also interested in data-driven decision making that is robust to the uncertainty. Specifically, he delivers methodologies for anomaly detection and system design optimization, which can be applied to manufacturing process monitoring, distracted driving detection, out-of-distribution object identification, vehicle safety design optimization, etc.

J.J. Prescott

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Broadly, I study legal decision making, including decisions related to crime and employment. I typically use large social science data bases, but also collect my own data using technology or surveys.

Edgar Franco-Vivanco

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Edgar Franco-Vivanco is an Assistant Professor of Political Science and a faculty associate at the Center for Political Studies. His research interests include Latin American politics, historical political economy, criminal violence, and indigenous politics.

Prof. Franco-Vivanco is interested in implementing machine learning tools to improve the analysis of historical data, in particular handwritten documents. He is also working in the application of text analysis to study indigenous languages. In a parallel research agenda, he explores how marginalized communities interact with criminal organizations and abusive policing in Latin America. As part of this research, he is using NLP tools to identify different types of criminal behavior.

Examples of the digitization process of handwritten documents from colonial Mexico.

Matthew VanEseltine

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Dr. VanEseltine is a sociologist and data scientist working with large-scale administrative data for causal and policy analysis. His interests include studying the effects of scientific infrastructure, training, and initiatives, as well as the development of open, sustainable, and replicable systems for data construction, curation, and dissemination. As part of the Institute for Research on Innovation and Science (IRIS), he contributes to record linkage and data improvements in the research community releases of UMETRICS, a data system built from integrated records on federal award funding and spending from dozens of American universities. Dr. VanEseltine’s recent work includes studying the impacts of COVID-19 on academic research activity.