• 345 Citations
  • 9 h-Index
1988 …2020

Research output per year

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Personal profile

Research interests

Pattern recognition, computer vision and machine vision, artificial neural networks, machine learning and deep learning.  Applications in medical image analysis, malware detection, industrial inspection.

Research engagement

Len engages with industry in R&D for a variety applications of machine vision and machine learning.  Past projects include:

  • Recognition of targets in sonar images from autonomous underwater vehicles, with Defence Industry Network.
  • Recognition of radar signals with Defence Services Technology Group.
  • Locomotive pantograph inspection system (PanCam) with Aurizon Ltd.  The developed system has been deployed since 2007 for timely automated detection and reporting of pantograph damage and wear.
  • Biscuit bake inspection system with Arnott's Biscuits Ltd.  Combines colour-calibrated image analysis with machine learning to report bake colour quality.
  • Inspection of recycled glass colour purity with Visy Recycling.

Research student supervision

Len supervises PhD and MRes candidates in topics related to machine learning and image analysis.  He is currently supervising three PhD candidates. 

Recent completions:

1. Mahmood Yousefi-Azar, “Machine learning for automatic malware representation and analysis,” PhD, 2020.

2. Tahereh Hassanzadeh, "Convolutional neural networks for prostate magnetic resonance image segmentation," MRes, 2018. 

3. Robert Newport, “Radar emitter recognition using hierarchical feature extraction within magnitude and frequency domains,” MRes, 2018.

4. Saruar Alam, “Impact of MRI technology on Alzheimer's disease detection,” MRes, 2018.

5. Mahmood Yousefi-Azar, “Query-oriented single-document summarization using unsupervised deep learning,” MRes, 2016.



Len currently teaches units in computer graphics and in systems programming.  He has over 30 years experience in University teaching, ranging across areas of computer science including networks and operating systems, computer architecture, and programming.

Len's innovative approach to teaching involves gamification and physical modeling of difficult concepts that arise in particular subject areas such as security protocols and 3D image formation.  His innovations in assessment tasks include uniquely defining the task for each student and provide immediate evaluation and feedback throughout the task.

Education/Academic qualification

Computer Science, PhD, Carnegie Mellon University

Statistics, BSc(hons), Macquarie University

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Automated pathogen detection using time-gated luminescence microscopy

Connally, R., Piper, J., Shen, J., Iredell, J., Hamey, L. & APAI, A.


Project: Research

Research Outputs

A survey on anomalous behavior detection for elderly care using dense-sensing networks

Deep, S., Zheng, X., Karmakar, C., Yu, D., Hamey, L. & Jin, J., 2020, In : IEEE Communications Surveys and Tutorials. 22, 1, p. 352-370 19 p.

Research output: Contribution to journalArticle

  • Automatic recognition of student engagement using deep learning and facial expression

    Mohamad Nezami, O., Dras, M., Hamey, L., Richards, D., Wan, S. & Paris, C., 2020, Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings, Part III. Brefeld, U., Fromont, E., Hotho, A., Knobbe, A., Maathuis, M. & Robardet, C. (eds.). Cham, Switzerland: Springer, Springer Nature, p. 273-289 17 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11908 LNAI).

    Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

  • Adversarial attacks on mobile malware detection

    Shahpasand, M., Hamey, L., Vatsalan, D. & Xue, M., 21 Mar 2019, AI4Mobile 2019 - 2019 IEEE 1st International Workshop on Artificial Intelligence for Mobile. Liu, Y., Ma, L., Li, L. & Xue, M. (eds.). Piscataway NJ: Institute of Electrical and Electronics Engineers (IEEE), p. 17-20 4 p. 8672711

    Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

  • 1 Citation (Scopus)

    Biomedical concept detection in medical images: MQ-CSIRO at 2019 ImageCLEFmed caption task

    Singh, S., Karimi, S., Ho-Shon, K. & Hamey, L., 9 Sep 2019, CLEF 2019 Working Notes: Working Notes of CLEF 2019 - Conferenceand Labs of the Evaluation Forum. Cappellato, L., Ferro, N., Losada, D. E. & Müller, H. (eds.). Aachen, Germany, p. 1-15 15 p. 131. (CEUR Workshop Proceedings; vol. 2380).

    Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

    Open Access
  • 1 Citation (Scopus)
    5 Downloads (Pure)

    Convolutional neural networks for prostate magnetic resonance image segmentation

    Hassanzadeh, T., Hamey, L. G. C. & Ho-Shon, K., 13 Mar 2019, In : IEEE Access. 7, p. 36748-36760 13 p., 8666973.

    Research output: Contribution to journalArticle

    Open Access
  • 4 Citations (Scopus)