Charu Chandra

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My research interests are in developing inter-disciplinary knowledge in System Informatics, as the basis for study of complex system problems with the fusion of theory, computation, and application components adopted from Systems and Informatics fields. In this framework, a complex system such as the supply chain is posited as a System-of-Systems; i.e., a collection of individual business entities organized as a composite system with their resources and capabilities pooled to obtain an interoperable and synergistic system, possessing common and shared goals and objectives. Informatics facilitates coordination and integration in the system by processing and sharing information among supply chain entities for improved decision-making.

A common theme of my research is the basic foundation of universality of system and the realization that what makes it unique is its environment. This has enabled to categorize problems, designs, models, methodologies, and solution techniques at macro and micro levels and develop innovative solutions by coordinating these levels in an integrated environment.

My goal is to study the efficacy of the body of knowledge available in Systems Theory, Information Science, Artificial Intelligence & Knowledge Management, Management Science, Industrial Engineering and Operations Research fields; applied uniquely to issues and problems of complex systems in the manufacturing and service sectors.

Theoretical work investigated by me in this research thrust relates to:

  • Developing Generalized System Taxonomies and Ontologies for complex systems management.
  • Experimenting with Problem Taxonomies for design and modeling efficiencies in complex system networks.
  • Developing methodologies, frameworks and reference models for complex systems management.
  • Computation and application development focused on developing algorithms and software development for:
    • Supply chain information system and knowledge library using Web-based technology as a dissemination tool.
    • Integration with Enterprise Resource Planning modules in SAP software.
    • Supply chain management problem-solving through application of problem specific simulation and optimization.

My research has extended to application domains in healthcare, textiles, automotive, and defense sectors. Problems and issues addressed relate to health care management, operationalizing of sustainability, energy conservation, global logistics management, mega-disaster recovery, humanitarian needs management, and entrepreneurship management.

Currently, my application focus is on expanding the breadth and depth of inquiry in the healthcare domain. Among the topics being investigated are: (1) the organization and structure of health care enterprises; and (2) operations and strategies that relate to management of critical success factors, such as costs, quality, innovation and technology adoption by health care providers. Two significant topics that I have chosen to study with regard to care for elderly patients suffering from chronic congestive heart failure and hypertension are: (1) the design of patient-centered health care delivery to improve quality of care; and (2) managing enhanced care costs due to readmission of these patients.

Data science applications: Real-time data processing in supply chains, Knowledge portals for decision-making in supply chains, information sharing for optimizing patient-centered healthcare delivery

Judy Jin

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Judy Jin, PhD, is Professor of Industrial and Operations Engineering in the College of Engineering at the University of Michigan, Ann Arbor.

Prof. Jin’s research focuses on the development of new data fusion methodologies for improving system operation and quality with the emphasis on fusion of data and engineering knowledge collected from disparate sources by integrating multidisciplinary methods. Her research has been widely applied in both manufacturing and service industry by providing techniques for knowledge discovery and risk-informed decision making. Key research issues are being pursued:

  1. Advanced quality control methodologies for system monitoring, diagnosis and control with temporally and spatially dense operational/sensing data.
  2. Multi-scale data transform and high order tensor data analysis for modeling, analysis, classification, and making inferences of multistream sensing signals.
  3. Optimal sensor distribution and hierarchical variable selection methods for system abnormal detection and sensor fusion decisions, which integrates the causal probability network model, statistical change detection, set-covering algorithm, and hierarchical lasso regression.
  4. A unified approach for variation reduction in multistage manufacturing processes (MMPs) using a state space model, which blend the control theory with advanced statistics for MMPs sensing, monitoring, diagnosis and control, integrative design of process tolerance and maintenance policy considering the interaction between product quality and tool reliability.

Data science applications: (a) Smart manufacturing with sensor fusion, process monitoring, diagnosis and control (e.g., metal forming including stamping, forging, casting and rolling), assembly, ultrasonic welding, photovoltaic thin film deposition. (b) Travel time estimation and traffic prediction for intelligent transportation systems. (c) Multi-stream data analysis of human motion/vehicle crash testing data for improving vehicle design and safety. (d) Risk informed decision support for healthcare and clinical decisions. (e) Customer behavior modeling for fraud detection in healthcare and telecommunication. (f) Human decision-making behavior modeling in a dynamic/emergency environment.


Brian Denton

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My interests are in personalized medicine with a focus on optimization of screening and treatment decisions for chronic diseases. This work has been funded by the National Science Foundation, the Agency for Healthcare Research and Quality, and other funding sources. This work involves the use of very large data sets comprising longitudinal data from electronic medical records and healthcare claims data. Screening and treatment optimization models are computationally challenging, owing to large state spaces caused, in part, by the dependence on the patient medical history, the large number of risk factors for chronic diseases, and the growing number of genetic screening tests and medications for treatment. I study several variants of stochastic optimal control problems for screening and treatment of diseases. For instance, in the context of diabetes I study optimal treatment policies for cholesterol, blood pressure, and blood sugar control medications. In the context of prostate cancer I have studied properties of the optimal policy for screening using biomarkers based on partially observable Markov decision processes. My research seeks to establish useful structural properties of optimal policies that can be exploited to achieve computational advantages, and insights that motivate understanding of the clinical decision rules. The computationally challenges that arise has motivated my work on exact and approximation methods for solving multi-stage stochastic programs and stochastic dynamic programs.  Most recently I have been investigating robust optimization approaches that account for uncertainty or ambiguity in model parameters and model assumptions. Several chronic diseases have been test-beds for my research including: bladder cancer, diabetes, heart disease, and prostate cancer. I have selected these because they are among the diseases having the greatest impact on the U.S. population and they bring together characteristics of many chronic diseases creating the opportunity for broader impact through the development of generalizable solution methods.