Wen Elected to ASHRAE College of Fellows

The American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) recently announced the election of Jin Wen, PhD , professor of civil, architectural and environmental engineering and associate dean for research and innovation, to its College of Fellows . Election to Fellow is one of the highest honors that ASHRAE can bestow upon a member for outstanding achievements and innovation in its related fields.

Of ASHRAE's more than 50,000 members, only about 1.6% are elected to the College of Fellows. As such, it is a meaningful recognition of Wen's significant contributions over her 20+ year career working at the intersection of smart buildings, sustainability, and human health and wellbeing.

"I'm tremendously grateful for this honor from my esteemed ASHRAE colleagues," said Wen. "Having the impact of my research, mentoring, and service contributions acknowledged reinforces my commitment to continue advancing sustainability and resilience in the built environment through innovative technologies and policies."

ASHRAE standards and design guidelines related to energy use and indoor air quality serve as the basis for building codes and sustainability metrics globally. As a newly elected Fellow, Wen will serve as an ambassador helping to enhance ASHRAE's technical reputation through activities including mentoring students and young professionals, sharing knowledge with ASHRAE membership, and supporting governance of the society's research programs.

Wen is the rising vice chair for ASHRAE's Research Administration Committee, overseeing and coordinating all of the organization's research activities. She also currently leads Task C of the International Energy Agency's Energy in Buildings and Communities Programme Annex 81, focused on data-driven smart buildings applications.

Wen has been prolific in smart building, building-grid integration, and occupant wellbeing research areas, with her work supported by funding from the U.S. Department of Energy, National Institute of Standards and Technology, National Science Foundation, and other agencies. She has published over 60 journal papers and her Google Scholar citations number more than 4,400 (based on Google Scholar).

Specifically, Wen has focused her research on developing machine learning based smart building solutions including automated fault detection and diagnosis (FDD) methods, allowing buildings to identify equipment failures early. Her RP 1312 FDD dataset has been widely used by the research community and the industry. She has also pioneered occupant behavior simulation models for machine learning based occupant centric model predictive control. With this substantial body of work, Wen continues advancing data-driven building efficiency, sustainability, and resilience.