Professor Jin Ma serves as Professor of Electrical Power Engineering in the School of Electrical and Computer Engineering, Faculty of Engineering at the University of Sydney. He earned his B.S. degree in electrical engineering in 1997 and M.S. degree in 2000, both from Zhejiang University, Hangzhou, China. Since September 2013, he has been affiliated with the University of Sydney, contributing to the Power Engineering Research Group. His research specializations encompass electrical energy transmission, networks and systems; electrical energy storage; electrical energy generation including renewables excluding photovoltaics; electrical engineering; and photovoltaic power systems. Ma's work addresses critical challenges in power system stability, integration of large-scale renewable energy sources such as wind and tidal power, load dynamics, electromechanical oscillations, and smart grid technologies.

Professor Ma has obtained extensive research funding from prestigious bodies, including multiple Australian Research Council grants such as the Discovery Project 'A Unified Framework for Resource Management in Edge-Cloud Data Centres' (2020-2024), the Linkage Equipment Grant 'Smart grid testing facility' (2015-2017), and Discovery Project 'Data driven studies on power system operation and control' (2023). He has also received support from the National Natural Science Foundation of China for projects like 'Analysis of Broadband Electromechanical Oscillation Characteristics and Intelligent Control Strategy of Low Inertia Power System' (2019-2022) and earlier initiatives on load modeling and renewable integration. His publication portfolio exceeds 350 outputs, featuring influential papers including 'Impact of Load Dynamics on Electromechanical Oscillations of Power Systems' (IEEE Transactions on Power Systems, circa 2019), 'Tidal Energy Hosting Capacity in Australia's Future Energy Mix' (Energies, 2021), 'A novel GBDT-BiLSTM hybrid model on improving day-ahead photovoltaic power forecasting' (2023), and 'Global emission factor dataset for Scope 3 machine learning applications' (Scientific Data, 2026). Additionally, he was awarded the Beijing Nova Program in 2008. Ma's contributions enhance the reliability and sustainability of modern power grids amid increasing renewable penetration.

Professional Email: j.ma@sydney.edu.au