Regular biography
Seungjae Lee is an Assistant Professor in the Department of Civil & Mineral Engineering at the University of Toronto. His research focuses on developing scalable Artificial Intelligence (AI) solutions for building energy systems to improve building energy performance, indoor environmental quality, and grid reliability and resilience. His work integrates building science domain knowledge with data to create feasible, effective, and human-understandable solutions using modern probabilistic machine learning, causal inference, and stochastic optimal control technologies. His research interests include optimizing building energy systems, human-building interactions, and IoT technologies for buildings.
Scholar-generated biography
Seungjae Lee is a researcher at the University of Toronto focusing on Building Energy System, Indoor Environment, Human-Building Interaction, and AI/ML. Their work explores data-driven fault detection, occupant thermal preferences, and energy-efficient HVAC control. Lee's research includes Bayesian approaches for occupant behavior inference, self-tuned controllers for thermal comfort, and sensor impact analysis on building performance. They also investigate smart building technologies, such as model predictive control and user-interactive systems for thermal environment management. Their studies emphasize energy optimization, occupant-centric control, and the integration of AI/ML in building operations.