Quan Z. Sheng
Also known as: Michael Sheng
Research areas: Computer Science · Information Systems · Service-Oriented Architecture and Web Services · IoT and Edge/Fog Computing · Recommender Systems and Techniques
0000-0002-3326-4147
About
Dr. Michael Sheng is a Chair Professor and Associate Vice President (Global Engagement) at Beijing Normal-Hong Kong Baptist University. He was a Distinguished Professor of School of Computing at Macquarie University where he also served as the Head of School during 2017-2026. Michael also spent 10 years at School of Computer Science, the Adeladie University. Michael holds a PhD degree in computer science from the University of New South Wales (UNSW) and did his post-doc as a research scientist at CSIRO ICT Centre. From 1999 to 2001, Sheng also worked at UNSW as a visiting research fellow. Prior to that, he spent 6 years as a senior software engineer in industries. Professor Michael Sheng is ranked by Microsoft Academic as one of the Most Impactful Authors in Services Computing (ranked Top 4 All Time) and in Web of Things (ranked Top 17 All Time) in 2021. He is ranked as a Highly Ranked Scholar (Top 3 Lifetime and Top 2 Prior 5 Years) in Web Information System by ScholarGPS in 2025. He is the recipient of ARC (Australian Research Council) Future Fellowship Award (2014), Chris Wallace Award for Outstanding Research Contribution (2012), and Microsoft Research Fellowship Award (2003). Distinguished Professor Michael Sheng is the Vice Chair of the Executive Committee of the IEEE Technical Community on Services Computing (IEEE TCSVC, 2022-2024) and a member of the ACS (Australian Computer Society) Technical Advisory Board on IoT (2019-2025).
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Publications (805)
- A survey of the state of the art in conversational question answering systems Save
- DOEM: A Novel Development-Oriented Evaluation Metric Framework for Service Recommendation Save
- Generative Chain of Behavior for User Trajectory Prediction Save
- Beyond benchmark accuracy: A generalization-centered analysis of deep learning for skeleton-based human activity recognition Save
- Parameter-Efficient Domain Adaptation for CSI Crowd-Counting via Self-Supervised Learning With Adapter Modules Save
- MSFF-YOLOv11: a multi-scale feature fusion enhanced YOLOv11 for ampoule head defect detection Save
- Federated learning: From design foundations to advanced participant selection paradigms Save
- Towards faithful class-level self-explainability in graph neural networks by subgraph dependencies Save
- Self-expression property theory guided multi-modal brain graph learning Save
- Self-supervised Causal Effects Estimation Save
- Causal Abstraction Learning for Multi-Modal Grounded Planning Save
- Attention Bidirectional Gated Fusion Based Multimodal Intent Recognition Under Uncertain Missing Modalities Save
- MEG-RAG: Quantifying Multi-modal Evidence Grounding for Evidence Selection in RAG Save
- Purifying Multimodal Retrieval: Fragment-Level Evidence Selection for RAG Save
- Frequency-aware spatio-temporal topology learning for skeleton-based human activity recognition Save
- Fusion decomposition and backbone gathering based multimodal sentiment analysis under uncertain missing modalities Save
- STAR Save
- Next POI recommendation for random group based on Spatio-Temporal heterogeneous graph Save
- Beyond Smoothing: A Discriminative Enhanced Aggregation Graph Neural Network for Camouflaged Fraud Detection Save
- CALM: Causal Abstraction Learning for Multi-Modal Grounded Planning Save