Scholar
Jing Du
Google Scholar ID: Ene4wqEAAAAJ
Postdoctoral Research Fellow at University of New South Wales
Citations & Impact
Citations
121
H-index
5
i10-index
4
Publications
17
Co-authors
8
list available
Contact
Publications
Resume (English only)
Academic Achievements
- Published 'A Probabilistic Framework for Imputing Genetic Distances in Spatiotemporal Pathogen Models' at ACM SIGSPATIAL 2025, focusing on avian flu forecasting and epidemic modeling.
- Published 'Explicit and Implicit Data Augmentation for Social Event Detection' at ACL 2025, exploring event detection and data augmentation with large language models.
- Published 'Enhanced Social Event Detection through Dynamically Weighted Meta-Paths Modeling' at The WebConf 2025, investigating meta-path modeling for event detection.
- Published 'Counterfactual Brain Graph Augmentation Guided Bi-Level Contrastive Learning for Disorder Analysis' at ICDM 2024, addressing brain disorder analysis via data augmentation.
- Published 'Distributionally-Adaptive Variational Meta Learning for Brain Graph Classification' at MICCAI 2024, advancing brain graph classification with distributionally adaptive GNNs.
- Published 'Identifiability of Cross-Domain Recommendation via Causal Subspace Disentanglement' at SIGIR, studying causal disentanglement in cross-domain recommendation.
Background
- Currently a Postdoctoral Research Fellow at the School of Computer Science & Engineering, University of New South Wales, supervised by Prof. Flora Salim.
- Research focuses on Human-centric Intelligence, Graph Representation Learning, and Spatio-temporal Modeling.
- Aims to design adaptive and trustworthy AI systems capable of robustly interpreting complex, multimodal data.
- Develops principled machine learning methods with demonstrated impact in brain network analysis, personalized recommendation, epidemic forecasting, and societal decision-making.
- Long-term vision is to establish next-generation human-centric AI frameworks integrating graph-based and spatio-temporal reasoning for robust, interpretable, and equitable decision support in healthcare, public safety, and sustainable society.
