Ph.D. in Building Performance and Diagnostics, Carnegie Mellon University, 2024
M.Arch., Washington University, 2020
M.Arch., Tianjin University, 2016
B.E. in Architecture, North China University of Science and Technology, 2014

Bio:

Assistant Professor Tian Li is an educator, building scientist, and architectural designer. His research focuses on AI-driven building performance and carbon emissions analysis. He holds a Ph.D. in Building Performance and Diagnostics from Carnegie Mellon University, two M.Arch. degrees in Architecture from Washington University and Tianjin University, and a B.E. in Architecture from North China University of Science and Technology.

Before joining UNL, Li was an adjunct faculty at Carnegie Mellon University and the University of Pittsburgh, teaching environmental performance simulation, sustainable and comprehensive design studios. He is a LEED Accredited Professional with global experience in the architecture and building science industry at Affiliated Engineers (Madison), SOM (Chicago and DC), Henning Larsen and NORD Architects (Copenhagen), MGA Partners (Philadelphia), and 1895 Design Institute (Tianjin).

Li seeks intelligent interdisciplinary approaches to harmony among architecture, built environments, and humanity. His scholarly works have been published in multiple peer-reviewed journals and conferences, and he actively serves as a leading guest editor for the Special Issue: “Carbon Emissions Analysis by AI Techniques” in the Journal of Information: https://www.mdpi.com/journal/information/special_issues/TT9R353B0G.

Publications (Selected):

Li, T., Bie, H., Lu, Y., Sawyer, A.O., Loftness, V. (2024). MEBA: AI-powered precise building monthly energy benchmarking approach. Applied Energy, 359, 122716. https://doi.org/10.1016/j.apenergy.2024.122716.

Li, T., Liu, T., Sawyer, A.O., Tang, P., Loftness, V., Lu, Y., Xie, J. (2024). Generalized building energy and carbon emissions benchmarking with post-prediction analysis. Developments in the Built Environment, 17, 100320. https://doi.org/10.1016/j.dibe.2024.100320.

Li, T., Xie, J., Liu, T., Lu, Y., Sawyer, A.O. (2023). An Innovative Building Energy Use Analysis by Unsupervised Classification and Supervised Regression Models. ASHRAE 2023 Annual Conference, Tampa, FL. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4730076.

Tian, J., Zhao, T., Li, Z., Li, T., Bie, H., Loftness, V. (2024). VOD: Vision-Based Building Energy Data Outlier Detection. Machine Learning and Knowledge Extraction, 6(2), 965-986. https://doi.org/10.3390/make6020045.

Xie, J., Sawyer, A.O., Ge, S., Li, T. (2022). Subjective impression of an office with biophilic design and blue lighting: A pilot study. Buildings, 13(1), 42. https://doi.org/10.3390/buildings13010042.

Teaching (Selected):

ARCH 5/611: Advanced Architectural Design

ARCH 311: Architectural Design Studio: Situate

ARCH 430: Technological Integration