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.
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.
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.
Daniel Brown is a Professor in the School for Environment and Sustainability. He is the Director of the Environmental Spatial Analysis Laboratory, and a Research Professor in the Survey Research Center, Institute for Social Research. His research focuses on linking landscape patterns with ecological and social processes. Professor Brown has studied vegetation types and patterns, land use and changes, climate changes and effect for over 25 years. His recent research focuses on the social and ecological aspects of land use in China, Mongolia and Africa, as well as land-use in South East Michigan and in the US Great Plains. His research requires the use of multiple methods, including Geographic information systems (GIS), computer modeling, remote sensing, social surveys and statistics.
Marisa Eisenberg is an assistant 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.
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 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).
Pascal Van Hentenryck is the Seth Bonder Collegiate Professor of Industrial & Operations Engineering.
Prof. Van Hentenryck’s research is currently at the intersection of data science and optimization with a focus on risk and resilience, energy systems, transportation, and logistics, marketing, and social networks. Most of these applications require predictive models and optimization over complex infrastructures, natural phenomena, and human behavior.
Seth Guikema is an Associate Professor of Industrial & Operations Engineering and Civil and Environmental Engineering. Prof. Guikema’s research is focused on and grounded in risk analysis, statistical learning theory, Bayesian probability, stochastic simulation, decision analysis, and agent-based modeling. The issues studied are related to climate adaptation and the sustainability of cities and infrastructure, disaster risk analysis, critical infrastructure modeling, natural hazards, and terrorism risk. Current projects include large-scale agent-based simulation models of evolution of regions in response to repeated climate-related events under different policy scenarios, data-driven evaluation of urban renewal and sustainability, and data-driven predictive modeling of the impacts of storms on power systems.
Cong Shi is an Assistant Professor in the Department of Industrial and Operations Engineering.
Professor Shi’s current research is focused on the design and performance analysis of efficient algorithms for stochastic optimization models, arising in the context of inventory and supply chain management, revenue management, as well as logistics. These stochastic optimization problems involve sequential decision-making under highly evolving or poorly understood environments, which are typically hard to solve to optimality. He constructs efficient heuristics that admit worst-case or average-case performance guarantees, and in doing so he develops novel analytical and computational techniques that are applicable to a broad class of models.