Daniel Brown is a Professor in the School of Natural Resources and Environment. 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.
Allison Steiner is an Associate Professor of Climate and Space Sciences and Engineering. Her research focus is on the relationship between the atmosphere and the terrestrial biosphere to help understand the bigger question: how will the Earth respond to climate change? Her research integrates gas and particulate matter, including anthropogenic aerosols and natural aerosols such as pollen, into high-resolution models. She and her research group then compare these results with observations to develop a comprehensive understanding of regional scale climate and atmospheric chemistry.
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.
Rafael Meza is an Assistant Professor in the Department of Epidemiology, School of Public Health, and an Honorary Professor at the Mexico National Institute of Public Health (INSP). Dr. Meza’s is interested in cancer risk assessment and the analysis of cancer epidemiology data using mechanistic models of carcinogenesis. He is also interested in the mathematical modeling of chronic and infectious disease dynamics and its applications in public health policy design.
Dr. Meza is Coordinating Principal Investigator of the Cancer Intervention and Surveillance Modeling Network (CISNET) lung group, Co-Leader of the Cancer Epidemiology and Prevention Program of the Cancer Prevention and Control Program at the University of Michigan Comprehensive Cancer Center (UMCCC), and member of the UM Tobacco Research Network.
Currently, Dr. Meza is developing models to evaluate the impact of screening and smoking cessation on lung cancer risk. Additional projects include the development of methodologies to investigate the effects of infectious disease dynamics on the risk of cancers with infectious disease etiology, modeling the impact of policies on cigarette and smokeless tobacco use, and modeling the impact of diabetes prevention strategies in Mexico.
Ruiwei Jiang is an Assistant Professor in the department of Industrial & Operations Engineering. Prof. Jiang’s research focuses on stochastic optimization and integer programming. He aims to develop data-enabled stochastic optimization (DESO) models and solution methodology that bring together data analytics, integer programming, stochastic programming, and robust optimization. Together with his collaborators, Prof. Jiang applies DESO approaches to various engineering problems, including power and water system operations, renewable energy integration, and healthcare resource scheduling.
His research makes use of rich information contained in the spectrally resolve observations (chiefly from space) to probe the climate system and gauge the performance of climate models. Topics of his ongoing projects include formulation and design of climate monitoring system based on accurate in-flight calibration system, spectrally resolved radiation budget and radiative feedbacks, detecting spectral signals of climate changes, and model evaluations using spectral data set. In the course of such studies, huge amount of data sets from observations or climate model simulations are fed into radiative transfer model to general spectral radiances at thousands of channels for each grid on the globe and for each time interval. To accurately and efficiently carry out such calculation is only possible with massive high performance computing and, as of today, such task is still computationally challenging.
His research group investigates simulation-based and data-driven computational synthesis of for mechanical, industrial and biomedical systems. The target systems are modeled by utilizing tools and algorithms in computational mechanics, geometric reasoning, image recognition, statistical data processing, and optimized by numerical optimization algorithms. Recent application domains includes lightweight automotive structures, intelligent transportation systems, water desalination systems, energy-efficient production systems, biomedical deformable image registration, and statistical protein energy potentials.
His research targets spatially-explicit interactions and feedbacks among components of environmental systems and builds on the development of and experimentation with physics/process-oriented models of water, energy, and element cycles at the plant, hillslope, catchment, and larger scales and the integration of observational data and models. Specific topics include high-resolution flood forecasting using coupled hydrologic-hydrodynamic modeling; assessment of climate impacts on watershed systems; simulation-based studies of ecohydrology of vegetation life-cycle processes and land-surface feedbacks; plant-scale modeling of water uptake and transpiration processes; and modeling of erosion and sediment transport.
Professor Flanner’s research ambitions lie in understanding large-scale energy transport in Earth’s climate system, with particular focus on the roles of the cryosphere (including seasonal snow cover, glaciers, and sea-ice) and atmospheric aerosols. To approach these topics, his research group applies and develops computationally demanding models of Earth’s global climate system. The team also analyzes large datasets generated by climate models and satellite measurements, spanning numerous dimensions of space, time, spectrum, and state. Flanner’s group strives to improve climate models by developing numerically efficient algorithms for microphysical processes that occur on scales too small to represent explicitly in global climate models, such as crystal growth in snowpack and interaction of sunlight with aerosols and ice crystals. The group also informs climate mitigation discussions by applying climate models to estimate the perturbations to Earth’s radiation field caused by emissions of short-lived pollutants from different regions and sectors.