Yunjeong (Leah) Mo

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Bio

Dr. Mo is an assistant professor in the Department of Civil, Construction and Environmental Engineering. Dr. Mo’s core research area comprises Humans in Built Environments focusing on Energy and Occupant Behavior, AEC Work Process Automation, Construction Safety, and Diverse Occupants. Her core research areas have expanded to Digital Twin-Based Building Management Systems, and Emerging Technologies for AEC Courses by incorporating Machine Learning (ML), Natural Language Processing (NLP), Building Information Modeling (BIM), Virtual Reality (VR), Augmented Reality (AR), etc. Before joining Iowa State University, Dr. Mo worked as an assistant professor at University of North Florida. Dr. Mo also has industry work experience as a system analyst and programmer at LG CNS in Seoul, Korea. Dr. Mo is a LEED AP BD+C (Building Design and Construction).

Education

• Ph.D. Construction Management, Michigan State University, 2018
• M.S. Building Performance and Diagnostics, Carnegie Mellon University, 2016
• M.S. Construction Management, Michigan State University, 2012
• B.S. Housing and Interior Architecture, Yonsei University (Seoul, Korea), 2002

Research Interests

• Smart Building Energy Management and Occupant Behavior
• Sustainable Built Environment
• Smart City and Infrastructure
• BIM, VR/AR, Digital Twin
• Data Analytics and Machine Learning
• Construction Work Process Automation

Publications

For a full list of publications, please visit her Google Scholar page.

Selected Publications

Shu, L., Mo, Y., & Zhao, D. (2024). Energy retrofits for smart and connected communities: Scopes and technologies. Renewable and Sustainable Energy Reviews, 199, 114510.

Kang, T. W., & Mo, Y. (2023). A comprehensive digital twin framework for building environment monitoring with emphasis on real-time data connectivity and predictability. Developments in the Built Environment, 17, 100309.

Zhao, D. & Mo, Y. (2023). Build New or Retrofit? Leverage Cost Benefits for Building Energy Efficiency. Buildings, Special Issue: Special Issue Building Energy Retrofit for Sustainable Urban Renewal.

Jiang, A., Mo, Y., & Kalasapudi, V. (2022). Status quo and challenges and future development of fire emergency evacuation research and application in built environment. Journal of Information Technology in Construction (ITcon), 27(38), 781-801.

Park, J. Y., Mistur, E., Kim, D., Mo, Y., & Hoefer, R. (2021). Toward human-centric urban infrastructure: Text mining for social media data to identify the public perception of COVID-19 policy in transportation hubs. Sustainable Cities and Society, 103524.

Mo, Y. & Zhao, D. (2022). Spatial Analysis on Routine Occupant Behavior Patterns and Associated Factors in Residential Buildings, Construction Research Congress (CRC) 2022 in Arlington, VA, March 9-12, 2022.

Mo, Y. & Zhao, D. (2021). Effective Factors for Residential Building Energy Modeling using Feature Engineering. Journal of Building Engineering, 102891.

Mo, Y. & Zhao, D. (2021). Application of Occupant Behavior Prediction Model on Residential Big Data Analysis, In Proceedings of The 8th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys), in Coimbra, Portugal, November 17-18, 2021.

Mo, Y., Zhao, D., Du, J., Syal, M., Aziz, A., & Li, H. (2020). Automated staff assignment for building maintenance using natural language processing. Automation in Construction, 113, 103150.

Mo, Y., Dhara, A., Liu, W., & Zhao, D. (2018). Data-driven Approach to Scenario Determination for VR-based Construction Safety Training. In Proceedings of Construction Research Congress (CRC) 2018 (pp. 116-125) in New Orleans, LA, April 2-4, 2018.

Zhao, D., McCoy, A. P., Agee, P., Mo, Y., Reichard, G., & Paige, F. (2018). Time effects of green buildings on energy use for low-income households: A longitudinal study in the United States. Sustainable Cities and Society.

Primary Strategic Research Area

Energy Systems

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