Antonios M. Koumpias, Ph.D., is Assistant Professor of Economics in the department of Social Sciences at the University of Michigan, Dearborn. Prof. Koumpias is an applied microeconomist with research interests in public economics, with an emphasis on behavioral tax compliance, and health economics. In his research, he employs quasi-experimental methods to disentangle the causal impact of policy interventions that occur at the aggregate (e.g. states) or the individual (e.g. taxpayers) level in a comparative case study setting. Namely, he relies on regression discontinuity designs, regression kink designs, matching methods, and synthetic control methods to perform program evaluation that estimates the causal treatment effect of the policy in question. Examples include the use of a regression discontinuity design to estimate the impact of a tax compliance reminders on payments of overdue income tax liabilities in Greece, matching methods to measure the influence of mass media campaigns in Pakistan on income tax filing and the synthetic control method to evaluate the long-term effect of state Medicaid expansions on mortality.
Dr. Yi Lu Murphey is an Associate Dean for Graduate Education and Research, a Professor of the ECE(Electrical and Computer Engineering) department and the director of the Intelligent Systems Lab at the University of Michigan, Dearborn. She received a M.S. degree in computer science from Wayne State University, Detroit, Michigan, in 1983, and a Ph.D degree with a major in Computer Engineering and a minor in Control Engineering from the University of Michigan, Ann Arbor, Michigan, in 1989. Her current research interests are in the areas of machine learning, pattern recognition, computer vision and intelligent systems with applications to automated and connected vehicles, optimal vehicle power management, data analytics, and robotic vision systems. She has authored over 130 publications in refereed journals and conference proceedings. She is an editor for the Journal of Pattern Recognition, a senior life member of AAAI and a fellow of IEEE.
Lu Wei, DSc, is Assistant Professor in the Department of Electrical and Computer Engineering at the University of Michigan, Dearborn.
Prof. Wei studies the analytical properties of interacting particle systems relevant to both classical and quantum information theory.
My current data science research interest lies in the broad area of supply chain and its management. I am particularly interested in using longitudinal data set to identify early signals (or warning) and to draw causal inferences pertaining to supply chain security and product quality and safety. I am also interested in developing experiments to capture the behavioral side of decision makings to be complementary to secondary data analysis. Industry setting wise, I have based my research on the auto industry, and will expand my auto-industry centered research into a broader, transportation industry oriented context. I am also interested in food and agricultural products, pharmaceutical, and medical devices industries where product quality and safety have significant implications to human life and society as a whole.
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
Keshav Pokhrel, PhD, is Assistant Professor of Statistics at the University of Michigan, Dearborn.
Prof. Pokhrel’s research interests include the epidemiology of cancer, time series forecasting, quantile regression and functional data analysis. The skewed and non-normal data are increasingly more frequent than ever before. The data in the extreme ends are of their own importance. Hence the importance of quantile regression. The availability of the information is increasingly functional. My current work is gearing towards functional data analysis techniques such as principal differential analysis which can estimate a system of differential equations to reveal the dynamics of real data.
Luis Ortiz, PhD, is Assistant Professor of Computer and Information Science, College of Engineering and Computer Science, The University of Michigan, Dearborn
The study of large complex systems of structured strategic interaction, such as economic, social, biological, financial, or large computer networks, provides substantial opportunities for fundamental computational and scientific contributions. Luis’ research focuses on problems emerging from the study of systems involving the interaction of a large number of “entities,” which is my way of abstractly and generally capturing individuals, institutions, corporations, biological organisms, or even the individual chemical components of which they are made (e.g., proteins and DNA). Current technology has facilitated the collection and public availability of vasts amounts of data, particularly capturing system behavior at fine levels of granularity. In Luis’ group, they study behavioral data of strategic nature at big data levels. One of their main objectives is to develop computational tools for data science, and in particular learning large-population models from such big sources of behavioral data that we can later use to study, analyze, predict and alter future system behavior at a variety of scales, and thus improve the overall efficiency of real-world complex systems (e.g., the smart grid, social and political networks, independent security and defense systems, and microfinance markets, to name a few).