Stephen Smith

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Stephen Smith is an Assistant Professor in the Department of Ecology and Evolutionary Biology. The Smith lab group is primarily interested in examining evolutionary processes using new data sources and analysis techniques. They develop methods to address questions about the rates and modes of evolution using the large data sources (e.g., genomes and transcriptomes) that have become more common in the biological disciplines over the last ten years. In particular, they use DNA sequence data to construct phylogenetic trees and conduct analyses about processes that shape the evolution of lineages and their genomes using these trees. In addition to this research program, they also address how new data sources can facilitate new research in evolutionary biology. To this end, they sequence transcriptomes, primarily in plants, with the goal of better understanding where, within the genome and within the phylogeny, processes like gene duplication and loss, horizontal gene transfer, and increased rates of molecular evolution occur.

Alex Gorodetsky

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Alex Gorodetsky is an Assistant Professor in the Department of Aerospace Engineering. His research includes using applied mathematics and computational science to enhance autonomous decision making under uncertainty. His research has an emphasis on controlling systems that must act in complex environments that are often represented through expensive computational simulations. His research uses tools from wide ranging areas including uncertainty quantification, statistical inference, machine learning, numerical analysis, function approximation, control, and optimization. Several of the key areas he focuses on are: optimal planning by solving large scale Markov decision processes, fast Bayesian estimation for nonlinear dynamical systems, high-dimensional compression and approximation of physical quantities of interest, and fusion of information from varying simulation fidelities and data through multi-fidelity modeling.

Bryan Goldsmith

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Bryan Goldsmith is an Assistant Professor in the Department of Chemical Engineering. His works focus on the development of novel catalysts and materials. The world is facing a growing population, mass consumerism, and rising greenhouse gas levels, all the while people strive to increase their standard of living. Computational modeling of catalysts and materials, and making use of its synergy with experiments, facilitates the process to design new systems since it provides a valuable way to test hypotheses and understand design criteria. His research team focuses on obtaining a deep understanding of catalytic systems and advanced materials for use in sustainable chemical production, pollution abatement, and energy generation. They use first-principles modeling (e.g., density-functional theory and wave function based methods), molecular simulation, and data analytics tools (e.g., statistical learning and data mining) to extract key insights of catalysts and materials under realistic conditions, and to help create a platform for their design.

A computational prediction for a group of gold nanoclusters (global model) could miss patterns unique to nonplaner clusters (subgroup 1) or planar clusters (subgroup 2)

A computational prediction for a group of gold nanoclusters (global model) could miss patterns unique to nonplaner clusters (subgroup 1) or planar clusters (subgroup 2)

Matthew Kay

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Matthew Kay is an Assistant Professor in the School of Information. His research focus in on human–computer interaction and information visualization. He tackles problems using a multi-faceted approach, including qualitative and quantitative analysis of behavior, building and evaluating interactive systems, and designing and testing visualization techniques.

Laura Balzano

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Laura Balzano is an Assistant Professor in Electrical Engineering and Computer Science at the University of Michigan. She is an Intel Early Career Faculty Honor Fellow and received an NSF BRIGE award. She received all her degrees in Electrical Engineering: BS from Rice University, MS from the University of California in Los Angeles, and PhD from the University of Wisconsin. She received the Outstanding MS Degree of the year award from the UCLA EE Department, and the Best Dissertation award from the University of Wisconsin ECE Department. Her main research focus is on modeling with highly incomplete or corrupted data, and its applications in networks, environmental monitoring, and computer vision. Her expertise is in statistical signal processing, matrix factorization, and optimization.

Jon Zelner

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Jon Zelner is an Assistant Professor in the Dept. of Epidemiology and Center for Social Epidemiology and Population Health in the UM School of Public Health. His work focuses on understanding the joint contributions of social, biological, and environmental factors to infectious disease transmission dynamics, with a particular focus on Tuberculosis (TB) transmission in high-burden contexts.

To do this, Jon uses mathematical and individual-based models to guide the design of studies and statistical tools for extracting information on infectious disease transmission from real-world spatiotemporal data. This ranges from small-scale simulation of household and community-based transmission to large-scale individual-based models of infectious disease transmission in megacities. A recurring methodological theme of this work is the challenge in navigating the tradeoff between fidelity to real-world processes and the need for parsimonious explanation of observable phenomena.

Tuberculosis hotspot in Lima, Peru

A hotspot of elevated incidence of multi-drug resistant tuberculosis (MDR-TB) in Lima, Peru is shown in red. Points indicate the location of TB cases; those marked ‘x’ are MDR-TB cases.



Ming Xu

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Ming Xu is an Associate Professor in the School for Environment and Sustainability, and in the Department of Civil and Environmental Engineering. The focus of his research is to understand the interaction between industrial systems and the biophysical environment. His goal is to provide an understanding of driving forces of environmental pressures and to help find an alternative pathway to reduce these pressures. Prof. Xu inherently interdisciplinary research combines data science, complex systems modeling and industrial ecology.


Marisa Eisenberg

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Marisa Eisenberg is an Associate Professor in the Department of Epidemiology, and in the Department of Mathematics. Her research revolves around mathematical epidemiology, focus on using and developing parameter estimation and identifiability techniques to model disease dynamics. Her group builds multi-scale models of infectious disease, including HPV, cholera and other environmentally driven diseases.


Likelihood surface exhibiting issues of unidentifiability—colors indicate goodness-of-fit, and the white line shows the values taken by an optimization algorithm as it navigates the surface.

Mosharaf Chowdhury

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Mosharaf Chowdhury is an Assistant Professor in Electrical Engineering and Computer Science, Computer Science and Engineering Division. Prof. Chowdhury works on topics in networked systems, networking, and big data. He is part of the Software Systems Laboratory, a multidisciplinary group conducting research in software systems. His research focus is on increasing application-infrastructure symbiosis across different layers of software and hardware stacks.

Judy Jin

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Judy Jin is a Professor in the Department of Industrial & Operations Engineering and the Director of the Manufacturing Engineering Program of the Integrative Systems and Design Division. Her research focuses on data fusion and system informatics for better comprehension and operation of engineering systems and decision-making for quality and reliability assurance. Her research is applied in several fields, including energy, manufacturing, medical decision making, telecommunications, transportation and unmanned ground vehicle (UGV).

Jody Jin