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  • The University of Western Australia (M018), 35 Stirling Highway,

    6009 Perth

    Australia

Personal profile

Biography

Professor Farid Boussaid received the M.S. and Ph.D. degrees from the National Institute of Applied Science (INSA), Toulouse, France, in 1996 and 1999 respectively. In March 2000, he joined Edith Cowan University, as a Post-Doctoral Research Fellow and a member of the Visual Information Processing Research Group. In December 2001, he was the recipient of an Australian Research Council APD Fellowship to develop a new generation of smart vision sensors featuring on-chip and pixel-level implementation of human vision-based algorithms. In 2005, he joined The University of Western Australia (UWA) as a lecturer. He is currently a Professor in the School of Electrical, Electronic and Computer Engineering (ECEE).

Over the duration of his career, Prof. Farid Boussaid has pursued a broad and intrinsically interdisciplinary research agenda focused mainly on the design, implementation and fabrication of low cost highly integrated smart sensing systems. Besides signal processing and machine learning techniques, he has investigated a neuromorphic approach that seeks to emulate the key signal processing mechanisms occurring in the olfactory and visual pathways. Such an approach aims at matching the efficiency and capabilities of biological systems, which remain far superior to that of engineered systems. This has enabled him to acquire a unique breadth of multi-disciplinary research expertise in the areas of smart sensors, signal processing, computer vision, machine learning and neuromorphic engineering.

His commitment to high quality teaching was recognized by a 2016 citation for outstanding contribution to Student Learning by the Australian Department of Education and Training. From 2014-2017, he was appointed Head of the School of Electrical, Electronic, & Computer Engineering at UWA.

Research expertise keywords

  • Microelectronics
  • Mixed-mode integrated circuit design
  • Machine learning / deep learning
  • Neuromorphic engineering
  • Gas sensors
  • Camera-on-a-chip
  • smart sensors
  • Computer vision

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