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My Ph.D. advisor:  Demosthenis Teneketzis, Elec. Engr: Systems, University of Michigan

Research Interests

The larger goal of my research to develop innovative operational structures and policies, innovating mathematical models, and powerful new methods for the analysis and design of healthcare, medical, and production/service operations. Working with doctoral students is the high point of my work, and I’ve had the good fortune to get to know and to work with some wonderful students as well as colleagues.

Generally, my disciplinary interests lie in Operations Management and Operations Research with particular emphasis on applied probability and stochastic processes, Markov decision processes, and the use of optimization methods to address important applications while generating new methods. Predictive analytics and machine learning is now an important dimension to my data-driven research in medical decision making (disease management) and healthcare delivery.

In healthcare, I often use the tools of operations research and systems theory. Prediction and machine learning are also an increasingly important area for developing new innovative methods that address interesting challenges. I am working on new methods to allow patients to receive appointment dates that are aligned with the urgency of their need for a visit (e.g., get the sickest patients seen more quickly without excessive strain on the other patients. Recent research has modeled and optimized admission control considering patient flow in hospitals and personalized “complexity” information, OR scheduling, operating protocols and bed management policies. Another area is next generation planning and scheduling methods for the integration of clinical research into clinical care operations. With collaborators at Mayo Clinic, we have addressed appointment scheduling methods for rapid access to visits (in the operating rooms and in outpatient networks), and breast cancer patient itinerary completion at destination hospitals. 

In the supply chain area, some of my work targets flexible supply chains.  This the joint use of operational flexibility and hedging to achieve better financial performance and to successfully weather supply disruptions.

In the past, my research has considered how flexibility can be achieved through cross-trained workers, flexible machines/robots, or flexible organization and management of operations. There is significant opportunity for performance improvement by reengineering traditional specialized/inflexible systems to achieve an agile system architecture. By agility, I mean multi-functional machines, multi-skilled workers, and system designs that enhance flexibility so the operation/firm can better adapt to changes and uncertainty.