Healthcare Models and Applications

Current ISE Focus

Almost all ISE faculty have shown interest or conducted research in healthcare models and applications, focusing on using mathematical modeling, stochastic processes, dynamic programming, and simulations to address healthcare challenges. Some faculty study the connection between technology and aging, human factors, and workplace aging, developing integrated systems with wearable sensors, edge computing, and cloud platforms for continuous health monitoring. Other research areas include data-driven analytics, optimization, nonlinear dynamics, transfer learning, explainable modeling, and complex network theory to monitor and control large healthcare systems. There is also growing work on using predictive analytics to forecast post-discharge readmissions, emergency visits, population health, and mortality.

Opportunities for Interdisciplinary Collaboration

There is tremendous opportunity to leverage the tools of predictive analytics, optimization, modeling, and simulation to the CoE’s thrust area of Healthcare Engineering through collaborations with ECE, BME, Miller School of Medicine, and the Business School. Currently, some of our faculty members hold secondary appointments with the Business School.

• Cheng-Bang Chen
  • Assistant Professor in the Department of Industrial & Systems Engineering and the Director of the Complex Systems Behavior Analytics and Optimization Lab (CBlab). CBlab studies intricate system behaviors with a focus on data fusion sensing systems, nonlinear dynamics, and network theory. Their research applies to fields like advanced bioinformatics, smart health and healthcare solutions, and energy systems optimization. The lab’s mission is to develop innovative data-driven analytical methods that address nonlinear dynamics to improve decision-making and system performance. Specifically, CBlab focuses on (a) large-scale data analytics for decision optimization and resource allocation, and (b) mining dynamic recurrences in complex systems for feature extraction, monitoring, and anomaly detection. Their work supports sensor-driven modeling, novel nonlinear system design, and predictive/diagnostic tools for real-world processes.
• Adam Meyers
  • Assistant Professor in the Department of Industrial & Systems Engineering. His research develops fundamental, data-driven methodologies to enable intelligent capabilities in modern complex systems. He aims to extract meaningful information from sensor data to gain scientific insights, enhance automation, monitor and control processes, and optimize system performance. His work spans healthcare, environmental and earth systems science, transportation, and manufacturing.
• Nurcin Celik
  • Professor of Industrial & Systems Engineering and Director of SimLab. She develops AI-augmented optimization methods for real-time decision-making in complex systems, including energy systems, solid waste management and recycling, cyber-physical systems, and healthcare applications. The research focuses on integrating simulation data with machine learning to improve the efficiency and reliability of digital twin technologies.
• Murat Erkoc
  • Professor in the Department of Industrial & Systems Engineering. His healthcare operations research emphasizes the design and optimization of telemedicine systems, focusing on capacity management, physician scheduling, and workforce planning. He studies predictive analytics models to anticipate demand, improve service levels, and ensure timely access to care across distributed networks. This work supports efficient resource allocation, better provider utilization, and stronger operational resilience in healthcare delivery.
• Vincent Omachonu
  • Professor and Chair of the Department of Industrial & Systems Engineering. His research interests include healthcare survey science, patient experience studies, and healthcare lean and quality management.