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.
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 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.
I try to understand how our tools for determining what is valuable, worthwhile, or good are implicated in patterns of inequality with an acute concern for racial inequality. Often, this means my work investigates the functioning and consequences of a range of scores or ratings, from the less complex government credit ratings to the extremely complex algorithmic scores like consumer credit scores.
In related work, as a part of a multi-university team of researchers, I am using administrative credit report data from one of the largest credit reporting agencies to study credit and debt outcomes for millions of consumers in the United States.
My research focus the application and development of new algorithms for solving complex business analytics problems. Applications vary from revenue management, dynamic pricing, marketing analytics, to retail logistics. In terms of methodology, I use a combination of operations research and machine learning/online optimization techniques.
Dr. Brian Lin has 12 years of experience in automotive research at UMTRI after his Ph.D. His current research is focused on mining naturalistic driving data, evaluating driver assistance systems, modeling driver performance and behavior, and estimating driver distraction and workload, using statistical methods, classification, clustering, and survival analysis. His most recent work includes classifying human driver’s decision for a discretionary lane change and traversal at unsignalized intersections, driver’s response to lead vehicle’s movement, and subjective acceptance on automated lane change feature. Dr. Lin also has much experience applying data analytic methods to evaluate automotive system prototypes, including auto-braking, lane departure, driver-state monitoring, electronic head units, car-following and curve-assist systems on level-2 automation, and lane-change and intersection assist on L3 automation on public roads, test tracks, or driving simulators. He is also familiar with the human factors methods to investigate driver distraction, workload, and human-machine interaction with in-vehicle technologies and safety features. He serves as a peer reviewer for Applied Ergonomics, Behavior Research Methods, IEEE Transactions on Intelligent Transportation Systems, IEEE Transactions on Intelligent Vehicles and Transportation Research Part F.
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.
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.
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.