Accepting PhD Students

PhD projects

AI for Energy: Energy Data Analytics, Applied Machine Learning for Smart Grid and Smart City; Economics and Optimisation of Power and Energy System


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Personal profile


PhD Opportunities:

  • We have three PhD Scholarships which are open now until they are filled:
    • Data-driven behavioural modelling and decentralised AI for DERs 
    • Incentive mechnism design and distributed optimisation for DERs

(both funded through the ARC DECRA project "Reliable Integration of Distributed Low-Carbon Energy Resources")

    • Human-in-the-loop AI for Microgrid Management (with Ariel, Frits, and Buser, funded by ENGIE) - Click Here to Apply

Honours and Masters Minor Thesis Opportunities:

Accepting one-year Honours and Masters Minor Thesis students. Check out the projects at


Hao Wang is an ARC DECRA Fellow and Senior Lecturer in the Department of Data Science and Artificial Intelligence, Monash University. He is also affiliated with the Monash Energy Institute and Monash Data Futures Institute.

He was a Postdoctoral Research Fellow in the Stanford Sustainable Systems Lab working with Prof. Ram Rajagopal and Dr. Chin-Woo Tan at Stanford University and a Washington Research Foundation (WRF) Innovation Fellow working with Prof. Baosen Zhang at University of Washington, Seattle. He received his PhD from The Chinese University of Hong Kong under the supervision of Prof. Jianwei Huang.

His research interests are in applied machine learning and data analytics for smart grids and smart cities, optimization of power & energy systems, and business models and mechanism design for incentivizing participation of electric vehicles and prosumers. He has been awarded the best paper award at IEEE PECON 2016, the best paper run-up at IEEE ICC 2017, and the best paper nomination at IEEE SmartGridComm 2020. He is on the editorial board of the International Journal of Precision Engineering and Manufacturing-Green Technology (IJPEM-GT), an associate editor for Energy Conversion and Economics, IEEE Access, and Frontiers in Energy Research, and a referee for top journals: IEEE Transactions on Smart Grid, IEEE Transactions on Power Systems, IEEE Transactions on Sustainable Energy, IEEE Transactions on Industrial Informatics, IEEE Internet of Things Journal, Applied Energy, Renewable Energy, etc. He has served as an Organization Committee member for ACM e-Energy 2019 and a TPC member for conferences including IEEE SmartGridComm, IEEE Globecom, and IEEE ICC.

He is looking for Ph.D. students with self-motivation and strong interests in interdisciplinary research of smart energy systems, such as AI and machine learning, data analysis, optimisation, and mechnism design. Visiting scholars and students are also welcome.

Call for papers 

Frontiers in Energy Research - "AI, Data Analytics, and Mechanism Design for DER Integration Toward Net Zero
Full paper submission closed and the EDITORIAL published

IET Renewable Power Generation - "Computational Methods and Artificial Intelligence Applications in Low Carbon Energy Systems"
Full paper submission deadline: 30 April 2023

Research interests

  • Optimisation for smart grids, microgrids, and transactive energy
  • Machine learning (e.g., reinforcement learning, online learning) for energy systems
  • Data analytics for distributed energy resources, e.g., residential prosumers and electric vehicles
  • Energy economics and business models, e.g., game-theoretic analysis and sharing economy

Expertise related to UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):

  • SDG 7 - Affordable and Clean Energy
  • SDG 8 - Decent Work and Economic Growth
  • SDG 11 - Sustainable Cities and Communities
  • SDG 12 - Responsible Consumption and Production
  • SDG 13 - Climate Action

Research area keywords

  • Power Systems
  • Optimisation
  • Distributed energy resources
  • Machine learning
  • Demand response
  • Renewable Energy
  • Data Analytics
  • Smart grid
  • Energy management

Collaborations and top research areas from the last five years

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