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Marisa Eisenberg

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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.

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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.

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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.

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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).

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Pascal Van Hetenryck

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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.

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Seth Guikema

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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.

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Cong Shi

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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.

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Rafael Meza

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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, core member 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.

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Smoking prevalence

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Ruiwei Jiang

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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.

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Jihyoun Jeon

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Jihyoun Jeon is an Assistant Research Scientist in the Department of Epidemiology, in the School of Public Health. She is also a member of the University of Michigan Comprehensive Cancer Center (UMCCC), and an affiliate at the Fred Hutchinson Cancer Research Center (FHCRC). Her research interests focus on developing biologically based mathematical models and statistical methods to evaluate the impact of risk factors on various cancers, and the efficacy of screening to reduce cancer incidence and/or mortality. The goal of these modeling efforts is to better understand the underlying mechanism of the natural history of cancer, develop innovative methodologies to solve important public health questions, and assist public health policy makers in their decision process.

She is a core member of large multidisciplinary national consortia: the Lung Cancer group of the NCI consortium ‘Cancer Intervention and Surveillance Modeling Network (CISNET)’, Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO), and Colorectal Transdisciplinary (CORECT) Study in the Genetic Associations and Mechanisms in Oncology (GAME-ON). She is particularly interested in developing mathematical models and simulation tools to investigate the synergistic impacts of tobacco control policies and CT screening on lung cancer risk in the US and in some middle-income countries. And she is also interested in developing risk prediction models for colorectal cancer that incorporate genetic variants identified form GWAS study along with environmental risk factors and modifiable lifestyle factors in population based and prospective studies. These models would provide a more accurate risk stratification of individuals, which would be useful to suggest individually tailored health strategies given the person’s risk profile in terms of genetic variants as well as lifestyle and environmental risk factors collectively.

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Ivo Dinov

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Dr. Ivo Dinov directs the Statistics Online Computational Resource (SOCR), co-directs the multi-institutional Probability Distributome Project, and is an associate director for education of the Michigan Institute for Data Science (MIDAS).

Dr. Dinov is an expert in mathematical modeling, statistical analysis, computational processing and visualization of Big Data. He is involved in longitudinal morphometric studies of human development (e.g., Autism, Schizophrenia), maturation (e.g., depression, pain) and aging (e.g., Alzheimer’s and Parkinson’s diseases). Dr. Dinov is developing, validating and disseminating novel technology-enhanced pedagogical approaches for scientific education and active learning.

 

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Analyzing Big observational data including thousands of Parkinson’s disease patients based on tens-of-thousands signature biomarkers derived from multi-source imaging, genetics, clinical, physiologic, phenomics and demographic data elements is challenging. We are developing Big Data representation strategies, implementing efficient algorithms and introducing software tools for managing, analyzing, modeling and visualizing large, complex, incongruent and heterogeneous data. Such service-oriented platforms and methodological advances enable Big Data Discovery Science and present existing opportunities for learners, educators, researchers, practitioners and policy makers.