Wangda Zuo

Wangda Zuo is an Associate Professor in the Department of Civil, Environmental and Architectural Engineering at the University of Colorado Boulder. His research focuses on building energy systems, renewable energy integration, and machine learning applications for energy optimization across buildings and power grids.

Dr. Zuo's expertise spans building energy modeling and forecasting, renewable resource allocation for resilient communities, and data center energy efficiency. He develops advanced computational frameworks using deep learning and optimization algorithms to solve complex energy challenges, with emphasis on practical implementations for grid stability and energy conservation. His work bridges theoretical innovation with real-world applications through industry and agency collaborations.

Analysis of his 2020 publications reveals dominant themes in machine learning-driven building energy forecasting and renewable energy deployment optimization. His research consistently addresses critical intersections between power systems and built environments, particularly focusing on data center grid services and electricity pricing impacts on commercial building efficiency.

Dr. Zuo's distinguished recognition includes:

• Research Development Award from CU Boulder's Civil Engineering Department (2020)
• ASHRAE Distinguished Service Award (2017)
• Eliahu I. Jury Early Career Research Award from University of Miami Engineering College (2016)
• Four consecutive SEEDS Leadership Awards from University of Miami (2013-2016)
• IBPSA-USA Emerging Professional Award for energy modeling excellence (2016)
• University of Miami Provost Research Award (2014)
• IBPSA-USA Best Poster Award (2009)

He currently mentors two mechanical engineering PhD students and actively recruits additional graduate researchers through his laboratory. While specific grant details aren't provided in the source material, his award portfolio demonstrates sustained success in securing competitive research funding. His laboratory functions as an interdisciplinary hub for energy systems innovation, with ongoing projects focused on building-grid integration and machine learning applications.