Researcher

Quan Z. Sheng

Also known as Michael Sheng

Quan Z. Sheng is a computer scientist at Macquarie University in Sydney whose research connects the Internet of Things with service computing and machine learning. He worked at the University of New South Wales as a visiting research fellow from 1999 to 2001, spent ten years at the University of Adelaide's School of Computer Science, and received the Chris Wallace Award for Outstanding Research Contribution in 2012 and an ARC Future Fellowship in 2014. In 2021, he coauthored the Communications of the ACM article An Internet of Things Service Roadmap, and he coauthored surveys on deep learning for community detection and graph anomaly detection. He is a Distinguished Professor in Macquarie's School of Computing, served as its Head of School from 2017 to 2026, and is a Deputy Director of the ARC Research Hub in the IoT for Water.

Now at
Macquarie University and Australian Research Council Research Hub in the Internet of Things for Water

Timeline

  1. 1999–2003

    UNSW and a Microsoft fellowship

    After six years as a senior software engineer in industry, he works at the University of New South Wales as a visiting research fellow from 1999 to 2001. He earns a computer science PhD at UNSW and does postdoctoral work as a research scientist at the CSIRO ICT Centre. In 2003, he receives a Microsoft Research Fellowship.

  2. 2012

    Chris Wallace research award

    He receives the Chris Wallace Award for Outstanding Research Contribution in 2012.

  3. 2014

    ARC Future Fellowship

    He receives an Australian Research Council Future Fellowship in 2014.

  4. 2017–2026

    Head of Computing at Macquarie

    Before moving to Macquarie University, he spends ten years at the University of Adelaide's School of Computer Science, where he serves as interim Head and Deputy Head of School. He serves as Head of School of Computing at Macquarie from 2017 to 2026. By 2022, he is also Associate Director of Macquarie's Smart Green Cities Research Center and Vice Chair of the Executive Committee of the IEEE Technical Community on Services Computing.

  5. 2018

    AMiner Most Influential Scholar

    AMiner gives him its Most Influential Scholar Award on IoT in 2018, covering the years 2007 to 2017.

  6. 2021–2024

    IoT roadmap and graph surveys

    In 2021, he coauthors An Internet of Things Service Roadmap with Athman Bouguettaya, Boualem Benatallah, Lina Yao and others, a Communications of the ACM cover article that combines service computing with Internet of Things technology. He also coauthors surveys of community detection and graph anomaly detection with deep learning, published in IEEE TNNLS in 2024 and IEEE TKDE in 2023, and a survey of session-based recommender systems in ACM Computing Surveys in 2022.

  7. As of October 2026

    Distinguished Professor at Macquarie

    He is a Distinguished Professor in the School of Computing at Macquarie University and a Deputy Director of the ARC Research Hub in the IoT for Water. Springer publishes his book with Yao Liu and Lina Yao, Harnessing Artificial Intelligence and Pervasive Internet of Things for an Empowered Aging, in May 2026.

Career

  1. Since 2017Macquarie UniversityHead of School
  2. NowAustralian Research Council Research Hub in the Internet of Things for WaterDeputy Director
  3. Until 2017University of AdelaideInterim Head and Deputy Head of School
  4. 1999–2001UNSW SydneyVisiting Research Fellow
  5. CSIROResearch Scientist

Education

  1. UNSW SydneyPhD

Sources and credits

Photo: We have no published photo of Quan Z. Sheng yet. Send us a free-licensed photo.

  1. Prof. Quan Z. (Michael) Sheng's Home Page web.science.mq.edu.au. Checked 22 October 2026.
  2. IEEE ICWS 2022 Keynote: Michael Sheng conferences.computer.org. Checked 22 October 2026.
  3. Speakers – AIHE 2025 – Nguyen Tat Thanh University aihe.ntt.edu.vn. Checked 22 October 2026.
  4. [2103.03043] An Internet of Things Service Roadmap arxiv.org. Checked 22 October 2026.
  5. [2105.12584] A Comprehensive Survey on Community Detection with Deep Learning arxiv.org. Checked 22 October 2026.
  6. [2106.07178] A Comprehensive Survey on Graph Anomaly Detection with Deep Learning arxiv.org. Checked 22 October 2026.
  7. [1902.04864] A Survey on Session-based Recommender Systems arxiv.org. Checked 22 October 2026.