Efficient, low regret contextual multi-armed bandit approaches for real time learning including Thompson sampling, UCB, and knowledge gradient descent. Integration of optimization and predictive analytics for determining the time to next measurement, which modality to use, and the optimal control of risk factors to manage chronic disease. Integration of soft voting ensemble classifiers and multiple models Kalman filters for disease state prediction, Real-time (online) contextual multi-armed bandits integrated with optimization of hospital bed type dynamic control decisions for reducing 30-day readmission rates in hospitals. Robustness in system optimization when the system model is uncertain with emphasis on quantile regression forests, sample average approximation, robust optimization and distributionally robust optimization. Health care delivery systems models with prediction and control for inpatient and outpatient. Work has been done on Emergency Department redesign for improved patient flow; Capacity management and planning and scheduling for outpatient care, including integrated services networks; admission control with machine learning to ICUs, stepdown, and regular care units Surgical planning and scheduling for access delay control; Planning and scheduling for Clinical Research Units.
S. Sriram, PhD, is Associate Professor of Marketing in the University of Michigan Ross School of Business, Ann Arbor.
Prof. Sriram’s research interests are in the areas of brand and product portfolio management, multi-sided platforms, healthcare policy, and online education. His research uses state of the art econometric methods to answer important managerial and policy-relevant questions. He has studied topics such as measuring and tracking brand equity and optimal allocation of resources to maintain long-term brand profitability, cannibalization, consumer adoption of technology products, and strategies for multi-sided platforms. Substantively, his research has spanned several industries including consumer packaged goods, technology products and services, retailing, news media, the interface of healthcare and marketing, and MOOCs.
My research focuses on the intended and unintended consequences of language in financial markets. I examine this relationship across a number of contexts, such as the Federal Reserve, initial public offerings, and mergers and acquisitions. More broadly, my work aims to develop new theoretical and methodological approaches to understand the role of language in society.
Professor Seiford’s research interests are primarily in the areas of quality engineering, productivity analysis, process improvement, multiple-criteria decision making, and performance measurement. In addition, he is recognized as one of the world’s experts in the methodology of Data Envelopment Analysis. His current research involves the development of benchmarking models for identifying best-practice in manufacturing and service systems. He has written and co-authored four books and over one hundred articles in the areas of quality, productivity, operations management, process improvement, decision analysis, and decision support systems.
Edward G. Happ is an Executive Fellow at the University of Michigan School of Information, where he is teaching and conducting research. He is also the Co-Founder and former Chairman of NetHope (www.nethope.org), a U.S. based consortium of 50+ leading international relief, development and conservation nonprofits focused on information and communications technology (ICT) and collaboration.
Hyun-soo Ahn is an Associate Professor of Operations and Management Science at the Michigan Business School. He joined Michigan in 2003 from the department of Industrial Engineering and Operations Research at UC Berkeley. In his research, Hyun-soo develops and analyzes mathematical models related to supply chain management, dynamic pricing and revenue management, workforce agility, and resource allocation. He is also interested in modeling the customer’s behavior (such as subscription) and how it affects the firm’s profit. He has worked with more than 20 companies and his research has been funded by several organizations including National Science Foundation. His papers appear in leading journals in the field, including Operations Research, M&SOM, and Journal of Applied Probability.
At Ross, he teaches supply chain analytics and business statistics to MBAs, Exec. MBAs, MSCM, and BBAs. He has won a number of teaching awards voted by students, including 2012 Exec MBA teaching excellence award, 2012 Global MBA teaching excellence award, and 2006 BBA teaching excellence award.
My current research interest is focused on improving efficiency and utilization of outpatient clinics, using data mining techniques such as decision tree analysis, Bayesian networks, neural networks, and similar techniques. While our previous and continuing research have been focused on using some of these techniques to develop more sophisticated methods of patients scheduling within physical therapy clinics, we can see the applicability of the techniques to other types of health services providers. There is also applicability to university administration in developing predictive models using data mining techniques for assessing student success.
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
Dr. Nalliah’s research expertise is process evaluation. He has studied various healthcare processes, educational processes and healthcare economics. Dr. Nalliah’s research studies were the first time nationwide data was used to highlight emergency room resource utilization for managing dental conditions in the United States. Dr. Nalliah is internationally recognized as a pioneer in the field of nationwide hospital dataset research for dental conditions and has numerous publications in peer reviewed journals. After completing a masters degree at Harvard School of Public Health, Dr. Nalliah’s interests have expanded and he has studied various public health issues including sports injuries, poisoning, child abuse, motor vehicle accidents and surgical processes (like stem cell transplants, cardiac valve surgery and fracture reduction). National recognition of his expertise in these broader topics of medicine have given rise to opportunities to lecture to medical residents, nurse practitioners, students in medical, pharmacy and nursing programs about oral health. This is his passion- that his research should inform an evolution of health education curriculum and practice.
Dr. Nalliah’s professional mission is to improve healthcare delivery systems and he is interested in improving processes, minimizing inefficiencies, reducing healthcare bottlenecks, increasing quality, and increase task sharing which will lead to a patient-centered, coherent healthcare system. Dr. Nalliah’s research has identified systems constraints and his goal is to influence policy and planning to break those constraints and improve healthcare delivery.
Professor Owen-Smith conducts research on the collective dynamics of large scale networks and their implications for scientific and technological innovation and surgical care. He is the executive director of the Institution for Research on Innovation and Science (IRIS, http://iris.isr.umich.edu). IRIS is a national consortium of research universities who share data and support infrastructure designed to support research to understand, explain, and eventually improve the public value of academic research and research training.