I am working at the intersection of artificial intelligence, energy systems, and built environments. My research focuses on developing scalable and deployable AI methods for energy applications, including smart buildings, HVAC control, battery systems, and emerging electrified infrastructure.

My recent work explores how foundation models, agentic AI, and learning-based control can be integrated with physical systems for prediction, diagnosis, and control optimization. I am particularly interested in building AI systems that are not only accurate in models and simulations, but also usable in real engineering workflows.

I am involved in collaborative projects with academic, government, and industrial partners in Hong Kong and Japan. Before my PhD, I worked as a machine learning engineer at Huawei, JD.com, and startups in Shanghai.

Brief CV.

News

  • Jul 2026: Delivered a talk at “EMSD x Student Exclusive | AI Energy Innovators Bootcamp”, Shau Kei Wan Government Secondary School, invited by EMSD (機電工程署).
  • May-Jun 2026: Returning as a Visiting Fellow at Prof. Ittetsu Taniguchi’s lab (The University of Osaka, Japan).
  • May 2026: Delivered an guest lecture at the Academy of Interdisciplinary Studies (AIS), HKUST, on AI Agents for Information Retrieval in Building Management Systems.
  • Mar 2026: Our paper “ThermoStill: Distilling Time Series Foundation Model into Thermal Dynamics Model for HVAC Model Predictive Control” is accepted by ACM e-Energy 2026 (Winter round, 18 of 127 submissions were accepted).
  • Jan 2026: Three papers on load prediction interpretability, carbon modeling, foundation model-based carbon forecasting are accepted by ACM e-Energy’26 (Fall round), WWW’26.
  • Oct 2025: Delivered an invited talk titled “Empowering AI Scalability in Building Energy Management Systems” at the Hong Kong Computer Society (香港電脳学会) Artificial Intelligence Seminar: AI in Engineering and Construction (link). Many thanks to Prof. Smason Tai’s invitation.
  • Sep 2025: Three papers on building metadata modeling, carbon modeling, HVAC aggregation control optimization are accepted by Knowledge-Based Systems (KBS), NeurIPS 2025 , ACM BuildSys’25.
  • Aug 2025: Our project on AIoT-based building energy control has been shortlisted for the final assessment in the “PolyU International Future Challenge” entrepreneurship contest.
  • Jul-Aug 2025: I was a Visiting Fellow at Osaka University, Japan, collaborating with Prof. Ittetsu Taniguchi and Dr. Dafang Zhao. During the visit, I led a joint research project among PolyU, Osaka University, and Daikin on optimizing HVAC control.
  • May 2025: Three papers on foundation model and data augmentation got accepted: WeatherFM accepted by IJCAI’25, AugPlug+ accepted by ACM TOSN, FM fine-tuning for building analytics accepted by ICML CO-BUILD’25.
  • Dec 2024: I work as a Postdoctoral Fellow (funded by Research Talent Hub of Innovation and Technology Commission, Hong Kong) in the Department of Computing at PolyU.
  • Nov 2024: Best Ph.D. Forum Presentation Award at ACM BuildSys 2024 in Hangzhou, China! (for my presentation: Improving Cyber-Physical Building Energy System via Large-Scale Machine Learning Evaluation).
  • Oct 2024: Our work AugPlug and two poster/demo are accepted by ACM BuildSys 2024, all related to our BaiTest Project.
  • Sep 2024: I passed my PhD defense! And many thanks to my supervisor Prof. Dan WANG!
  • Aug 2024: Best Presentation Award at the 2nd PolyU Research Student Conference (PRSC 2024).
  • Jun 2024: Best Poster Runner Up at ACM e-Energy 2024 in Singapore!

Selected Publications

  1. ThermoStill: Distilling Time Series Foundation Model into Thermal Dynamics Model for HVAC Model Predictive Control
    Rui Liang, Yang Deng*, Yaohui Liu, Dafang Zhao, Ozan Baris Mulayim, Ittetsu Taniguchi, Dan Wang (*corresponding author)
    ACM International Conference on Future Energy Systems (e-Energy), 2026
  2. MetaCloze: A Schema-guided Automated Building Metadata Model Generation System via Information Extraction
    Fang He, Jiaqi Fan, Yang Deng*, et al. (*corresponding author)
    Knowledge-Based Systems
  3. Smart metering data enhancement in sustainable buildings via knowledge graph-guided graph neural networks
    Fang He, Jiaqi Fan, Yang Deng*, et al. (*corresponding author)
    Knowledge-Based Systems
  4. Concept Drift-aware Time-series Generation for Online Building Load Forecasting: An Automated Data Augmentation Paradigm
    Yang Deng, Rui Liang, Jiaqi Fan, et al.
    ACM Transactions on Sensor Networks (TOSN)
  5. AugPlug: An Automated Data Augmentation Model to Enhance Online Building Load Forecasting
    Yang Deng, Rui Liang, Yaohui Liu, Jiaqi Fan, and Dan Wang
    ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys), Best paper candidate, 2024. [pdf] [slides]
  6. Decomposition-based Data Augmentation for Time-series Building Load Data
    Yang Deng, Rui Liang, Dan Wang, Ao Li, and Fu Xiao
    ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys), 2023. [pdf] [slides]
  7. Behavior testing of load forecasting models using BuildChecks
    Yang Deng, Jiaqi Fan, Hao Jiang, Fang He, Dan Wang, Ao Li, and Fu Xiao
    ACM International Conference on Future Energy Systems (e-Energy), 2022. [pdf] [slides]
  8. Energon: A Data Acquisition System for Portable Building Analytics
    Fang He, Yang Deng, Yanhui Xu, Cheng Xu, Dezhi Hong, and Dan Wang
    ACM International Conference on Future Energy Systems (e-Energy), 2021. [pdf] [slides]