Len Hamey

Associate Professor

  • Source: Scopus
  • Calculated based on number of publications stored in Pure and citations from Scopus
1983 …2022

Research activity 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 has over 30 years experience in University teaching, ranging across areas of computer science including networks and operating systems, computer architecture, computer graphics, 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 providing immediate evaluation and feedback throughout the task.

Education/Academic qualification

Computer Science, PhD, Carnegie Mellon University

Award Date: 26 Feb 1988

Statistics, BSc(hons), Macquarie University


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