Energy and Electrical Grids

Energy and environment research spans a wide range of areas, including grid design and optimization, natural and carbon hazard mitigation, climate dynamics, energy efficiency and storage, environmental chemistry, combustion and fuels, nuclear fusion, ocean-atmosphere dynamics, public policy, solar and wind energy, sustainable cities, and water resources. This work integrates mathematical and computational modeling with experimental research and strong collaboration with industry.

Current ISE Focus

Three ISE faculty have active research programs in energy and environment. Their work includes data-driven modeling and decision-making for complex systems like distributed electricity grids, with a focus on building smarter, more resilient cities. Other efforts involve energy-aware manufacturing, transportation planning, and sustainable material handling systems. Additional research covers data analytics for systems operating under uncertainty, with applications in wind energy, smart grids, and connected communities. Faculty also lead initiatives such as industrial energy audits in partnership with the Department of Energy.

Opportunities for Interdisciplinary Collaboration

The interdisciplinary nature of the Energy and Electricity cluster makes it possible for ISE faculty to play a key role at the College of Engineering level. There is tremendous opportunity to leverage the tools of mathematical modeling, predictive analytics, optimization, and simulation in advancing the College’s focus on Energy and Environment through collaborations with MAE, ECE, and CAE.

Faculty Participants

  • 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. Her research focuses on integrating simulation data with machine learning to enhance the efficiency and reliability of digital twin technologies.
  • Ramin Moghaddass
    • Associate Professor in the Department of Industrial & Systems Engineering and Director of the Industrial Assessment Lab. His research center focuses on using data to improve the performance, reliability, and sustainability of complex energy systems. This work integrates advanced analytics—such as stochastic modeling, control theory, and machine learning—to enable predictive insights and real-time decisions. The lab applies sensor data, graph-based modeling, and AI to address challenges in energy efficiency, fault detection, and system optimization across industrial, commercial, and residential settings.
  • 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 explores complex system behaviors through data fusion, nonlinear dynamics, and network theory. The lab's research spans energy systems optimization, smart health, healthcare solutions, and bioinformatics. It focuses on large-scale data analytics for decision optimization and resource allocation, as well as mining dynamic recurrences in complex systems for feature extraction, monitoring, and anomaly detection. The work supports sensor-driven modeling, nonlinear system design, and the development of predictive and diagnostic tools for real-world applications.
  • Murat Erkoc
    • Professor in the Department of Industrial & Systems Engineering. His research in energy analytics centers on modeling and optimizing energy demand and supply systems, with an emphasis on cogeneration, co-opetitive energy production, and distributed energy management. He develops data-driven and mathematical models to support decision-making in renewable energy integration and resilient infrastructure design, with the goal of improving efficiency, resilience, and collaboration across energy stakeholders.

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