Dr Ye Zhu

Dr Ye Zhu

Senior Lecturer of Computer Science, IEEE Senior Member

Deakin University


I am working in the School of Information Technology at Deakin University as a Senior Lecturer of Computer Science. I am also the HDR coordinator of D2I (Data to Intelligence Research Centre) and a member of CSRI (Centre for Cyber Security Research and Innovation) at the school. My research works focus on the fields of data mining and machine learning. Particular topics include clustering analysis, anomaly detection, similarity learning and their applications for pattern recognition and information retrieval. Most research outcomes have been published in top-tier computer science conferences and journals, including SIGKDD, VLDB, IJCAI, AAAI, AIJ, TKDE, ISJ, PRJ, JAIR and MLJ.

I got my Ph.D. in AI with a Mollie Holman Medal for the best doctoral thesis of the year from Monash University in 2017, supervised by Prof. Kai Ming Ting and A/Prof. Mark Carman. I obtained the Research Award for Excellence in Early Career Research Performance and the Teaching and Learning Award from Deakin University in 2020 and 2021, respectively. I have also secured six research grants of around AUD$540,000 in total since 2018.


  • Clustering analysis
  • Anomaly detection
  • Similarity learning
  • Unsupervised learning


  • PhD in Artificial Intelligence, 2017

    Monash University, Australia

  • MSc in Computing, 2012

    Imperial College London, UK

  • BEng in Computer Science, 2010

    Huazhong Normal University, China

Teaching Units

SIT741 - Statistical Data Analysis

The aim of this unit is to provide students with the opportunity to develop advanced working knowledge in statistical modelling and statistical programming. Students will learn how to apply advanced statistical theories such as generalised additive modelling to model real-world data problems.

SIT742 - Modern Data Science

In this unit, students will have the opportunity to learn fundamental aspects of data science, modern methods, techniques and applications of data science. Upon successful completion of study, students will be able to use distributed storage and computing platform to process and analyse big data, and use modern techniques in data analytics.

SIT718 - Real World Analytics

SIT718 introduces students to two concepts at the heart of real world analytics: optimisation and multivariate data aggregation. Students will learn how decision-making problems in industry, business, and civic services can be solved using modern modelling and solution techniques.

Professional Services

Journal Reviewer

  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Data Mining and Knowledge Discovery
  • ACM Transactions on Knowledge Discovery from Data
  • Pattern Recognition
  • IEEE Transactions on Neural Networks and Learning Systems
  • Machine Learning
  • IEEE Intelligence Systems
  • Knowledge and Information Systems
  • IEEE Transactions on Computational Social Systems
  • International Journal of Data Science and Analytics

Conference Reviewer

  • AAAI (2021, 2022)
  • IJCAI (2021,2022)
  • KDD (2021,2022)
  • PKDD-ECML (2019, 2020, 2021, 2022)
  • PAKDD (2019, 2020, 2021, 2022)
  • IEEE International Conference on Data Mining (2016, 2021)
  • ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2016, 2022)

Conference Organiser/Volunteer

  • IEEE Symposium on Computers and Communications (ISCC 2021, 2022)

  • AMSI Workshop Dynamical Systems in Physiology(Melbourne, Australia, 2019)

  • #123 NII Shonan Meeting (Tokyo, JAPAN, 2018)

  • Pacific-Asia Conference on Knowledge Discovery and Data Mining (Melbourne, Australia, 2018)

  • ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (San Francisco, California, USA 2016)