I am a ITF-funded Postdoctoral Fellow at the Department of Computing at Hong Kong Polytechnic University (PolyU), working with Prof. Dan WANG and Prof. Chen Jason Zhang.
During my PhD study in PolyU, my research interest lies in machine learning model evaluation in Building Energy System, with hands-on experience in time series forecasting and applications in building HVAC system. I have also contributed my expertise to support AI competitions for Building E&M Facilities hosted by the Hong Kong government. My recent research centers on enhancing the “Scalability” of AI in (building) energy systems, with a focus on two following perspectives: a) Large Foundation Model and b) Model Ensemble. More details of my overall research framework can be found here.
Prior to PolyU, I gained valuable industry experience worked as machine learing engineer at Huawei Shanghai Institute and JD.com, two China’s leading tech companies. This experience provided me with practical insights into the deployment of AI in real-world scenarios, which inspired my pursuit of a PhD.
Email: yang2.deng@connect.polyu.hk
News
- Oct 2025: Delivered an invited talk titled “Empowering AI Scalability in Building Energy Management Systems” at the HKCS (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 project among PolyU, Osaka University, and Daikin on optimizing HVAC control.
- May 2025: Three papers on foundation model (FM) and data augmentation (DA) 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
BaiTestProject. - Sep 2024: I passed my PhD defense! And many thanks to 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
- 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 - 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) - 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] - 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] - 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] - 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]