Our BIOR lab aims at developing sustainable and scalable technologies and computational tools to make building, district, and urban energy systems smart, low-carbon, energy-efficient, energy-flexible, climate-resilient, and equitable using optimization, learning, and control.

Our interdisciplinary research is at the interface of Building Science, Computer Science, and Control Engineering.

We employ a multifaceted approach that encompasses data analytics & machine learning, physics-based modeling & simulation, optimization & model-based optimal controls, as well as experiments. These approaches have been deployed across a spectrum of scales, spanning from equipment- through building- and community- to city-scale.

Specifically, our research interests include:

• Flexible, Resilient, Efficient, and Equitable (FREE) multi-scale energy systems with Distributed Energy Resources (DERs)
• Optimal and Learning-based Control
• Explainable AI for Building and Urban Science and Engineering (XABUSE)
• Digital twin-enabled Energy Management Information System (DEMIS)
• Urban Microclimate and Building Energy Modeling (UMBEM)

• Thrust 1: Flexible, Resilient, Efficient, and Equitable (FREE) multi-scale energy systems with Distributed Energy Resources (DERs)

• Thrust 2: Optimal and Learning-based Control

• Thrust 3: Explainable AI for Building and Urban Science and Engineering (XABUSE)

• Thrust 4: Digital twin-enabled Energy Management Information System (DEMIS)

• Thrust 5: Urban Microclimate and Building Energy Modeling (UMBEM)