👋🏼 Hello there, I’m Gang!
👨🏻💻 I’m a third-year PhD candidate at The University of Utah, expecting to graduate in June 2026.
📍 My research interests:
- 🤖 AI & LLM for Building
- ⚙️ Physics-Informed Modeling
- 🏙 Building Energy Modeling & Calibration.
📌 I’m currently developing auto-building energy modeling (ABEM) using LLMs to improve modeling accessibility & scalability.
🖇 Open-Source Contributions
EPlus-LLM: A large-scale LLM for auto-building energy modeling.
Prompting LLMs for ABEM: A comprehensive guideline for prompt engineering of LLMs in building modeling.
🔬 Experience
🧪 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, 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.
🎉 News
📢 Jun. 2025 – I will be attending the ASHRAE Annual Conference in Phoenix, Arizona. I am happy to engage in discussions and make connections!
📄 Jan. 2025 – Our review paper, A Review of Physics-Informed Machine Learning for Building Energy Modeling, has been published in Applied Energy.
📄 Jan. 2025 – My first-authored paper, Prompt Engineering to Inform Large Language Models in Automated Building Energy Modeling, has been published in Energy.
📄 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.
📄 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.