Romesh Saigal

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Professor Saigal has held faculty positions at the Haas School of Business, Berkeley and the department of Industrial Engineering and Management Sciences at Northwestern University, has been a researcher at the Bell Telephone Laboratories and numerous short term visiting positions. He currently teaches courses in Financial Engineering. In the recent past he taught courses in optimization, and Management Science. His current research involves data based studies of operational problems in the areas of Finance, Transportation, Renewable Energy and Healthcare, with an emphasis on the management and pricing of risks. This involves the use of data analytics, optimization, stochastic processes and financial engineering tools. His earlier research involved theoretical investigation into interior point methods, large scale optimization and software development for mathematical programming. He is an author of two books on optimization and large set of publications in top refereed journals. He has been an associate editor of Management Science and is a member of SIAM, AMS and AAAS. He has served as the Director of the interdisciplinary Financial Engineering Program and as the Director of Interdisciplinary Professional Programs (now Integrative Design + Systems) at the College of Engineering.

Michelle Aebersold

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Michelle Aebersold, PhD, is Clinical Associate Professor of Nursing, School of Nursing, at the University of Michigan, Ann Arbor.

Dr. Aebersold’s professional and academic career is focused on advancing the science of learning applied in simulation to align clinician and student practice behaviors with research evidence to improve learner and health outcomes.  She focuses her scholarship in both high fidelity and virtual reality simulation and is a national leader and expert in simulation. Her scholarship has culminated in developing the Simulation Model to Improve Learner and Health Outcomes (SMILHO).

Current Research Grants and Programs:

  • Closing the loop: new data tools for measuring change in the quality for nursing education and the value of new approaches to instruction (PI) University of Michigan School of Nursing.
  • Interactive anatomy-augmented virtual simulation training (PI with Voepel-Lewis) Archie MD Award Number 045889

Zhenke Wu

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Zhenke Wu is an Assistant Professor of Biostatistics, and Research Assistant Professor in Michigan Institute of Data Science (MIDAS). He received his Ph.D. in Biostatistics from the Johns Hopkins University in 2014 and then stayed at Hopkins for his postdoctoral training before joining the University of Michigan. Dr. Wu’s research focuses on the design and application of statistical methods that inform health decisions made by individuals, or precision medicine. The original methods and software developed by Dr. Wu are now used by investigators from research institutes such as CDC and Johns Hopkins, as well as site investigators from developing countries, e.g., Kenya, South Africa, Gambia, Mali, Zambia, Thailand and Bangladesh.

Yang Chen

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Yang Chen received her Ph.D. (2017) in Statistics from Harvard University and then joined the University of Michigan as an Assistant Professor of Statistics and Research Assistant Professor at the Michigan Institute of Data Science (MIDAS). She received her B.A. in Mathematics and Applied Mathematics from the University of Science and Technology of China. Research interests include computational algorithms in statistical inference and applied statistics in the field of biology and astronomy.

Lawrence Seiford

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Lawrence Seiford, PhD, is Professor of Industrial and Operations Engineering in the College of Engineering and the Goff Smith Co-Director of the Tauber Institute for Global Operations at the University of Michigan, Ann Arbor.

Prof. Seiford’s research interests include:

  • Analytics
    Data Analytics
  • Applications
    Healthcare Quality Improvement
    Banking & Finance
    Service Systems
  • Industrial Operations
    Distribution & Logistics
    Inventory Control
    Production Scheduling
    Supply-Chain Management
  • Operations Research Tools
    Data Envelopment Analysis
    Game Theory
    Math Modeling
    Performance Measurement
    Productivity And Efficiency Analysis
  • Quality and Applied Statistics
    Statistical Quality Control
    Exploratory Data Analysis
  • Risk Management
    Risk Analysis

Jun Li

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Jun Li, PhD, is Professor and Chair for Research in the department of Computational Medicine and Bioinformatics and Professor of Human Genetics in the Medical School at the University of Michigan, Ann Arbor.

 Prof. Li’s areas of interest include genetic and genomic analyses of complex phenotypes, including bipolar disorder, cancer, blood clotting disease, and traits involving animal models and human microbiomes. Our approach emphasizes statistical analysis of genome-scale datasets (e.g, gene expression and genotyping data, results from next-generation sequencing), evolutionary history, bioinformatics, and pattern recognition.

Kathleen M Bergen

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Kathleen M Bergen, PhD, is Associate Research Scientist in the School for Environment and Sustainability at the University of Michigan, Ann Arbor. Dr. Bergen currently has interim administrative oversight of the SEAS Environmental Spatial Analysis Laboratory (ESALab) and is interim Director of the campus-wide Graduate Certificate Program in Spatial Analysis.

Prof. Bergen works in the areas of human dimensions of environmental change; remote sensing, GIS and biodiversity informatics; and environmental health and informatics. Her focus is on combining field and geospatial data and methods to study the pattern and process of ecological systems, biodiversity and health. She also strives to build bridges between science and social science to understand the implications of human actions on the social and natural systems of which we are a part. She teaches courses in Remote Sensing and Geographic Information Systems. Formerly she served as a founding member of the UM LIbrary’s MIRLYN implementation team, directed the University Map Collection, and set up the M-Link reference information network.

Danny Forger

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Daniel Forger is a Professor in the Department of Mathematics. He is devoted to understanding biological clocks. He uses techniques from many fields, including computer simulation, detailed mathematical modeling and mathematical analysis, to understand biological timekeeping. His research aims to generate predictions that can be experimentally verified.

Brenda Gillespie

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Brenda Gillespie, PhD, is Associate Director in Consulting for Statistics, Computing and Analytics Research (CSCAR) with a secondary appointment as Associate Research Professor in the department of Biostatistics in the School of Public Health at the University of Michigan, Ann Arbor. She provides statistical collaboration and support for numerous research projects at the University of Michigan. She teaches Biostatistics courses as well as CSCAR short courses in survival analysis, regression analysis, sample size calculation, generalized linear models, meta-analysis, and statistical ethics. Her major areas of expertise are clinical trials and survival analysis.

Prof. Gillespie’s research interests are in the area of censored data and clinical trials. One research interest concerns the application of categorical regression models to the case of censored survival data. This technique is useful in modeling the hazard function (instead of treating it as a nuisance parameter, as in Cox proportional hazards regression), or in the situation where time-related interactions (i.e., non-proportional hazards) are present. An investigation comparing various categorical modeling strategies is currently in progress.

Another area of interest is the analysis of cross-over trials with censored data. Brenda has developed (with M. Feingold) a set of nonparametric methods for testing and estimation in this setting. Our methods out-perform previous methods in most cases.

Marcelline Harris

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Marcelline Harris, Ph.D., R.N., is Associate Professor of Systems, Populations and Leadership in the School of Nursing at the University of Michigan, Ann Arbor.

Dr. Harris’s research interests focus on what is being labeled the “continuous use” of clinical data (the use of clinical data for one or more purposes), computable knowledge representation strategies, and the use of electronic clinical data for practice and research.  Her research has been funded by NIH, AHRQ, RWJF, and PCORI.  Harris also has extensive enterprise level experience, having served in both scientific and operational positions that address the development and governance of systems that support the capture, storage, indexing, and retrieval of clinical data.  At Michigan, she retains this translational perspective, emphasizing clinical data for patient-centered research, clinical surveillance and predictive analytics.