DrYinsong Chen

Postdoctoral Research Fellow

Faculty of Health/School of Health and Social Development

  • Postdoctoral Research Fellow
    Faculty of Health/School of Health and Social Development
  • Melbourne Burwood Campus, 221 Burwood Highway, Burwood, Victoria 3125

BIO

Dr. Yinsong Chen is an Associate Research Fellow at the School of Engineering, Deakin University, and a Postdoctoral Researcher at the ARC Future Grids Training Centre, University of Wollongong.

He received his Ph.D. in Machine Learning and Power Systems from Deakin University in February 2025. From 2023 to 2024, he was awarded an Australia–Germany DAAD Research Fellowship and served as a visiting scholar at FernUniversität in Hagen, Germany.

Dr. Chen is a driven and inquisitive researcher with a strong focus on renewable energy systems and probabilistic deep learning. His research interests span wind power forecasting, uncertainty quantification, Bayesian neural networks, Laplace approximation, and multi-objective optimization. He has published high-impact research in leading Q1 journals and delivered oral presentations at internationally recognized conferences, including IEEE Transactions on Sustainable Energy, Sustainable Energy, Grids and Networks, and the IEEE International Symposium on Circuits and Systems.

In addition to his academic pursuits, Dr. Chen is a passionate and versatile programmer with expertise in Java, cloud technologies, web development, and artificial intelligence. His technical skill set includes Python, Java, JavaScript, React, HTML, CSS, SQL, NumPy, pandas, PyTorch, AWS, and more.

DEAKIN UNIVERSITY CURRENT APPOINTMENT

  • Postdoctoral Research Fellow
    Deakin University, School of Health and Social Development

DEGREES

  • Doctor of Philosophy
    Deakin University, Geelong, Australia
  • Master of Engineering (Software)
    University of Melbourne, Melbourne, Australia
  • Bachelor of Engineering (Electronic & Elec. Eng.)
    Nanjing University of Science and Technology, Nanjing, China

FIELDS OF RESEARCH

  • Deep learning
  • Applied statistics
  • Electrical energy generation (incl. renewables, excl. photovoltaics)
  • Neural networks
  • Electrical energy transmission, networks and systems

AREA/FACULTY

  • Faculty of Health

DEPARTMENT/SCHOOL/INSTITUTE/DISCIPLINE

  • School of Health and Social Development

AREAS OF EXPERTISE