Guanjin Wang

Associate Professor

Murdoch University

Australia

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Dr. Guanjin Wang is an Associate Professor at Murdoch University in Australia. His research focuses on artificial intelligence applications in health, including perinatal mental health and human activity classification, as well as techniques for imbalanced data classification. His recent publications explore innovative approaches such as ensemble fuzzy classifiers and multi-view learning methods.

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Guanjin Wang

University Name

Murdoch University

PhD Scholarship in Responsible AI, Explainable AI, and Multimodal Deep Learning

Murdoch University in Western Australia is advertising PhD Scholarship Opportunities in AI with Associate Professor Guanjin Wang . The research group is seeking highly motivated PhD candidates to work on Responsible AI , Explainable AI , Multimodal Deep Learning , and other cross-disciplinary AI applications. The opportunity emphasizes research with real-world impact and collaboration with industry partners and interdisciplinary experts. This is a strong fit for applicants interested in machine learning, data-driven research applications, and applied AI topics that connect technical innovation with practical deployment. Eligibility highlights include a strong academic background and a proven research track record , including publications in high-quality venues. The post welcomes both onshore and offshore candidates . This is presented as a PhD scholarship ; however, the post does not specify stipend amount, tuition coverage, or other funding details. No deadline is stated in the post. To apply, email [email protected] with the subject line Your Name – PhD Scholarship .

Guanjin Wang

Research Interests

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About

Positions (1)

PhD Scholarship in Responsible AI, Explainable AI, and Multimodal Deep Learning

Articles (20)

Investigating Protective and Risk Factors and Predictive Insights for Aboriginal Perinatal Mental Health: Explainable Artificial Intelligence Approach

An Interpretable Ensemble Fuzzy Classifier for Smartphone Sensor-Based Human Activity Classification

Deep Reconciled and Self-Paced TSK Fuzzy System Ensemble for Imbalanced Data Classification: Architecture, Interpretability, and Theory

Pseudolabel Enhanced Multiview Deep Concept Factorization Fuzzy Clustering

Artificial intelligence in perinatal mental health research: A scoping review

Graph Fuzzy System for the Whole Graph Prediction: Concepts, Models, and Algorithms

Recognition of Sleep-Wake Stages by Deep Takagi-Sugeno-Kang Fuzzy Classifier with Random Rule Heritage

An AUC-maximizing classifier for skewed and partially labeled data with an application in clinical prediction modeling

A Novel AUC Maximization Imbalanced Learning Approach for Predicting Composite Outcomes in COVID-19 Hospitalized Patients

BE-ELM: Biological ensemble Extreme Learning Machine without the need of explicit aggregation

A deep multi-view imbalanced learning approach for identifying informative COVID-19 tweets from social media

An accuracy-maximization learning framework for supervised and semi-supervised imbalanced data

Cyclic Gate Recurrent Neural Networks for Time Series Data with Missing Values

Seasonal and Year-Round Distributions of Bactrocera dorsalis (Hendel) and Its Risk to Temperate Fruits under Climate Change

A Deep-Ensemble-Level-Based Interpretable Takagi–Sugeno–Kang Fuzzy Classifier for Imbalanced Data

A fuzzy system with common linear-term consequents equivalent to FLNN and GMM

Enhancement of prostate cancer diagnosis by machine learning techniques: an algorithm development and validation study

Support vector machines with the known feature-evolution priors

A review of irregular time series data handling with gated recurrent neural networks

Tweet Topics and Sentiments Relating to COVID-19 Vaccination Among Australian Twitter Users: Machine Learning Analysis

Collaborators (3)

Roz Walker

Ferdous Sohel

YongLin Ren

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