Hi, I am a Postdoctoral Scholar in the Department of Energy Resources Engineering at Stanford University since November 2021. Prior to joining Stanford, I worked with Prof. David Wallom as a Postdoc in the Energy and Environmental Informatics Group in the Department of Engineering Science at the University of Oxford for two years. I received my PhD degree in Building Environment and Energy Engineering from The Hong Kong Polytechnic University in 2019 (Supervisor: Prof. Linda Xiao). In 2018, I studied as a guest PhD student with Prof. John Bagterp Jørgensen in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark.
My research interests include data analytics, data-driven modelling, numerical optimization, and model predictive control of the building and urban energy systems for GHG reduction, energy efficiency, energy flexibility, and energy resiliency. I have been actively contributing to international collaborations, including the ongoing IEA EBC Annex 81 (Data-Driven Smart Buildings) and Annex 82 (Energy Flexible Buildings Towards Resilient Low Carbon Energy Systems).
Creating sustainable data-centric technologies for building and urban energy systems at the interface of Building Environment and Energy Engineering, Computer Science, and Control Engineering.
Data Cleaning | Data Visualization | Data Clustering | EDA
Grey-/Black-box | Machine Learning | Deep Learning
Building Thermal Dynamics | Refrigeration/HVAC systems | ODEs and State Space Representations
Mathmatical and Metaheuristic (GA/PSO) Algorithms
Moving from predictive to prescriptive analytics | MPC = Data-driven Modeling + Receding-horizon Numerical Optimization + Online Feedback Control
Monte Carlo technique |Stochastic Occupancy and Occupant Behavior (Markov Chain)
For energy efficiency/energy flexibility
Receding Horizon Optimisation: Opt.→Decision→Measurement→Opt.→Decision→Measurement→…
Using Heterogeneous Markov-chain Monte Carlo method
Simple and ready for control/large-scale purpose