Data Analytics and Artificial Intelligence

Artificial Intelligence (AI) involves creating smart machines or self-learning software that mimic human abilities like reasoning, problem-solving, planning, and decision-making. Its ability to discover knowledge faster than humans has attracted strong interest from business and research worldwide, leading to rapid progress over the past two decades. The Operations Research group in Industrial Engineering leverages its expertise in data and data science to advance in this growing field. With four faculty members working on Operations Research and optimization tools, they are well-positioned to make important contributions to Data Analytics and AI.

Current ISE Focus

Current ISE faculty focus is on developing advanced analytical and data mining models that can be used to transform large-scale datasets into actionable insights and decision-making intelligence in real time. Specifically, some members of the ISE faculty are involved in the areas of Data-driven analytics and Modeling, and Optimization, Sensor-based Process Monitoring and Control, Network Theory and Information Science, and Machine Learning and Artificial Intelligent Optimization. Currently, there’s only one faculty member with AI and ML research expertise (Dr. Chen), and three others with a background that would support this ISE Cluster of Data Analytics and AI.

Opportunities for Interdisciplinary Collaboration

AI-powered solutions have great potential to transform healthcare, including areas like disease diagnosis and monitoring, clinical workflow support, and hospital optimization. This creates strong opportunities for interdisciplinary collaboration. The department is well positioned to contribute to the Data Analytics & AI cluster by advancing AI techniques that improve traditional healthcare systems, such as natural language processing, data analytics, and machine learning.

 

Faculty Participants

  • Nurcin Celik
    • Professor of Industrial & Systems Engineering and Director of SimLab. She is developing 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 models to improve the efficiency and reliability of digital twin technologies.
  • Ye Hu
    • Assistant Professor of Industrial & Systems Engineering. Her research goals are to propose innovative networking solutions for the realization of digital inclusion. Her work spans unmanned aerial vehicle networks, satellite communication, cyber-physical human systems, network security, and distributed machine learning.
  • Cheng-Bang Chen
    • Assistant Professor in the Department of Industrial & Systems Engineering and 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 including advanced bioinformatics, smart health and healthcare solutions, and energy systems optimization. The lab’s mission is to develop innovative data-driven analytical methods addressing 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.
  • Ramin Moghaddass
    • Associate Professor in the Department of Industrial & Systems Engineering and Director of the Industrial Assessment Lab. His research focuses on using data to improve performance, reliability, and sustainability of complex energy systems. Central to this work is integrating advanced analytics, including stochastic modeling, control theory, and machine learning, to enable predictive insights and real-time decision-making. The lab uses sensor data, graph-based modeling, and AI to address energy efficiency, fault detection, and system optimization in industrial, commercial, and residential settings.
  • Adam Meyers
    • Assistant Professor in the Department of Industrial & Systems Engineering. His research develops fundamental, data-driven methods to enable intelligent capabilities in modern complex systems. He aims to extract meaningful information from sensing data to gain scientific insights, improve automation, monitor and control processes, and optimize system performance. His work covers healthcare, environmental and earth systems science, transportation, and manufacturing.
  • Murat Erkoc
    • Professor in the Department of Industrial & Systems Engineering. He leads the Supply Chain Innovation and Resilience Lab (SCIRL), focusing on advancing supply chain management through large-scale mathematical modeling, game theory, and simulation. His work addresses challenges in supply chain coordination, freight consolidation, routing, green logistics, MRO operations planning, and transportation network design. Through SCIRL, he develops data-driven and adaptive strategies to promote efficiency, sustainability, and robustness in supply chains.

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