Research / Research AreasHealth Analytics
The Center of Engineering and Health's Health Analytics research uses tools from predictive modeling, risk assessment, and decision analytics to drive policy and decision making. Learn more about projects in this area via the links below.
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Daniel Apley Lab
Developing predictive models for strategic management of credit risk based on large-scale customer databases; Bayesian statistical analysis for product and process design optimization based on finite element computer simulations; reliable assessment of disease risk from error-prone electronic medical records; and statistical modeling of microstructure behavior for predictive materials science.
Much of Daniel W. Apley's, PhD, prior research addresses how to transform large amounts of data into useful information. One application area is six sigma for modern manufacturing and service operations that are inundated with complex, high-volume, high-dimensional data, for which the research objective is to develop a methodology for discovering and visualizing pieces of information in these large databases that help process engineers systematically identify and eliminate root causes of variation, inconsistency, and poor quality. He also conducts research on the interface between statistical and engineering modeling and data mining in other application domains, such as business intelligence, healthcare engineering, and product design optimization. Recent projects in these areas include developing predictive models for strategic management of credit risk based on large-scale customer databases; Bayesian statistical analysis for product and process design optimization based on finite element computer simulations; reliable assessment of disease risk from error-prone electronic medical records; and statistical modeling of microstructure behavior for predictive materials science.
Sanjay Mehrotra Lab
Performing advanced statistical analysis, developing large scale optimization models, and developing detailed simulations of large, complex systems, while facilitating process and data collection to map the process.
Sanjay Mehrotra, PhD, is the director of the Center for Engineering and Health. His healthcare research includes topics in predictive modeling, budgeting, hospital operations, and policy modeling using modern operations research tools. This research involves performing advanced statistical analysis, development of large scale optimization models, developing detailed simulations of large complex systems, while facilitating process and data collection to map the process. His activities also involve training of medical professionals in healthcare quality and operations topics.
For his optimization methodology research, Mehrotra is widely known for his predictor-corrector interior point method, and methodology research in optimization that has spanned from linear, convex, mixed integer, stochastic, multi-objective, distributionally robust, and risk adjusted optimization. He was awarded the best applied paper award for the paper “Risk-adjusted budget allocation models with application in homeland security, (co-authored with Jian Hu, Tito Homem-de-Mello) published in IIE-Transactions during 2011-2012. His work is published in journals such as Mathematical Programming, SIAM Journal on Optimization, Mathematics of Operations Research, Operations Research, Optimization Methods and Software, IIE-Transactions, INFORMS Journal on Computing, Journal of Global Optimization, SIAM Journal on Computing, SIAM Journal on Numerical Analysis, Analyst, Bioinformatics, BMC BioInformatics, Annals of Surgery, and Healthcare Management Science. He has received research funding from the United States National Science Foundation, Office of Naval Research, Department of Energy, National Institute of Standards and Technology, and National Institute of Health.