Rate My Professor Guanjin Wang

Guanjin Wang is an Associate Professor in the School of Information Technology at Murdoch University, also affiliated with the Ngangk Yira Institute for Change. She earned a joint PhD degree in software engineering, artificial intelligence, and digital health from The Hong Kong Polytechnic University and the University of Technology Sydney in 2018, prior to joining Murdoch University. Her academic career at Murdoch began as a Lecturer in Information Technology and has progressed to her current position as Associate Professor.

Wang's research focuses on artificial intelligence, machine learning, explainable AI, responsible AI, transfer learning, and health data analytics, with interdisciplinary applications in areas such as perinatal mental health, medical imaging, predictive analytics for ICU patients, and spatiotemporal data analysis. She has authored numerous peer-reviewed publications in prestigious journals, including 'Artificial intelligence in perinatal mental health research: A scoping review' (Computers in Biology and Medicine, 2024), 'A systematic review of multi-modal large language models on multimodal sentiment analysis and emotion recognition' (Artificial Intelligence Review, 2025), 'TransLIME: Towards transfer explainability to explain black-box models beyond LIME' (Information Sciences, 2026), 'An artificial intelligence approach for predicting death or organ failure in ICU patients' (Artificial Intelligence in Medicine, 2023), 'Policy gradient empowered LSTM with dynamic skips for irregular time series forecasting' (Applied Soft Computing, 2023), and the highly cited 'A review of irregular time series data handling with gated recurrent neural networks' (Neurocomputing, 2021, cited over 550 times). Her scholarly impact is evidenced by over 2,498 citations and an h-index of 21 on Google Scholar. Wang has received the Google Society-Centered AI Research Award for her work exploring AI-enhanced mixed-ability social interactions. She actively supervises PhD students and contributes to research on privacy-preserving multi-agent systems and lithium-ion battery state-of-health estimation using machine learning techniques.