πŸ‘‹πŸΌ Hi there, I’m Gang!

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πŸ‘¨πŸ»β€πŸ’» I'm a fourth-year PhD candidate at The University of Utah, expecting to graduate in
June 2026.
I am open to work, including AP track, PostDoc, and industry research positions.
Please feel free to reach out!
πŸ“ My research interests:
- πŸ€– AI & LLM for Building Science
- βš™οΈ Physics-Informed & Automated Modeling
- πŸ™ (Urban) Building Sustainability & Resilience
πŸ“Œ I'm developing auto-building energy modeling (ABEM) using large language models
(LLMs) to improve modeling accessibility & scalability.
gang@home:~$

πŸ–‡ Open-Source Contributions


EPlus-LLMv1/v2: LLM-driven automatic building energy modeling through natural language.

Prompting LLMs for ABEM: A comprehensive guideline for prompt engineering of LLMs in auto-building energy modeling.
Illustration of LLM for Auto-building modeling
Figure: LLM-Powered Auto-Building Modeling Workflow

πŸ”¬ Experience

πŸš€ Currently, I am collaborating with Dr. Shandian Zhe (School of Computing, University of Utah) on NSF projects focused on improving LLMs’ accuracy, computational efficiency, and robustness.

πŸ§ͺ As part of my PhD journey, I am working with Dr. Jianli Chen on NSF-funded projects focused on Building Energy Modeling, Calibration, Optimization, and AI Applications in Buildings.

🧫 During my Master’s degree, at Tianjin University, I collaborated with Dr. Zhe Tian on NSF-China projects related to Building Energy System Simulation and Building Fault Detection & Diagnosis.

✍️ I have completed internships at Amazon AWS, where I have gained experience in designing and operating data centers with a focus on enhancing resilience and scalability, and at SUNAC, where I worked in real estate management.

πŸŽ‰ News

πŸš€ My first-authored research paper, Prompt Engineering to Inform Large Language Models in Automated Building Energy Modeling, (Energy, 2025), has been recognized as a πŸ† Top 1% Highly Cited Paper by ESI.

πŸš€ My first-authored research paper, EPlus-LLM: A Large Language Model-Based Computing Platform for Automated Building Energy Modeling, (Applied Energy, 2024), has also been selected as a πŸ† Top 1% Highly Cited Paper by ESI.

πŸ“„ Dec. 2025 – First-authored paper, Benchmarking Knowledge and Capability of Large Language Models in Building Science Domain, has been published in Energy Use.

πŸ“’ Feb. 2026 – I’m excited to attend the ASHRAE Winter Conference in Las Vegas, NV! Looking forward to connecting with you there! πŸŒ†
I’ll be giving two presentations:
(1) Large language models for automated building energy modeling (Invited Talk)
(2) Real-world applications of the EPlus-LLM Platform (Paper Session, Poster)

🎀 Oct. 2025 – Online talk on BuildNext: Toward Automated Building Energy Modeling with Large Language Models Slides

🎀 Aug. 2025 – I was invited to give a speech at ASHRAE CIDCO Conference in Denver, CO! Topic: Automating Building Energy Modeling from Natural Language

πŸ“’ Jun. 2025 – I will be attending the ASHRAE Annual Conference in Phoenix, Arizona. I am happy to engage in discussions and make connections!

πŸ“„ Apr. 2025 – The paper related to the EPlus-LLMv2 platform, has been accepted for publication in Automation in Construction.

πŸ“„ Jan. 2025 – Our review paper, A Review of Physics-Informed Machine Learning for Building Energy Modeling, has been published in Applied Energy.

πŸ“„ Jan. 2025 – My first-authored paper, Prompt Engineering to Inform Large Language Models in Automated Building Energy Modeling, has been published in Energy.

πŸ“„ Jun. 2024 – My first-authored paper, A Deep Learning-Based Bayesian Framework for High-Resolution Calibration of Building Energy Models, has been published in Energy & Buildings.

🎀 Jun. 2024 – I will be speaking about Natural Language Auto-Modeling via Fine-tuning LLMs at the ASHRAE Annual Conference in Indianapolis, Indiana.

πŸ“„ May. 2024 – My first-authored paper, EPlus-LLM: A Large Language Model-Based Computing Platform for Automated Building Energy Modeling, has been published in Applied Energy.