PeopleofSuperintelligence

Researcher

Mohammed Bennamoun

Mohammed Bennamoun is a computer scientist at The University of Western Australia whose research covers 3D vision, object recognition and deep learning. He received an M.Sc. in control theory and a PhD in computer vision, joined QUT in 1993, and moved to UWA in 2003, where he became a professor in 2007 and headed the School of Computer Science and Software Engineering until 2012. His 2013 Rotational Projection Statistics paper described a 3D local feature descriptor, and he coauthored A Guide to Convolutional Neural Networks for Computer Vision in 2018 and a survey of deep learning for 3D point clouds in 2019. A tutorial on spiking neural networks that he coauthored won the 2024 Best Paper Award of Proceedings of the IEEE, and Clarivate named him a Highly Cited Researcher for 2025. He is a Winthrop Professor at UWA.

Born
1961

Years link to their sources.

Timeline

  1. 1993–2002

    Lecturer and centre director at QUT

    He receives an M.Sc. in control theory from Queen's University in Kingston, Canada, and a PhD in computer vision from Queen's/QUT in Brisbane, Australia. After lecturing robotics at Queen's, he joins QUT in 1993 as an Associate Lecturer, becomes a Lecturer in 1996 and a Senior Lecturer in 1998, and directs a research centre from 1998 to 2002.

  2. 2003–2012

    Professor and head at UWA

    In January 2003, he joins the Department of Computer Science and Software Engineering at The University of Western Australia (UWA) as an Associate Professor, and he becomes a Professor in 2007. From February 2007 to March 2012, he is Head of the School of Computer Science and Software Engineering.

  3. 2013

    The RoPS 3D descriptor

    In 2013, he coauthors Rotational Projection Statistics (RoPS) with Yulan Guo, Ferdous Sohel, Min Lu and Jianwei Wan, a method for 3D local surface description and object recognition. The RoPS-based algorithm reaches recognition rates of 100%, 98.9%, 95.4% and 96.0% on the Bologna, UWA, Queen's and Ca' Foscari Venezia datasets.

  4. 2018–2019

    Books on CNNs and 3D shape

    A Guide to Convolutional Neural Networks for Computer Vision appears in February 2018, with Salman Khan, Hossein Rahmani and Syed Afaq Ali Shah as his coauthors. In March 2019, Wiley publishes 3D Shape Analysis: Fundamentals, Theory, and Applications, which he writes with Hamid Laga, Yulan Guo, Hedi Tabia and Robert B. Fisher.

  5. 2019

    Deep learning for 3D point clouds

    In December 2019, he and Yulan Guo, Hanyun Wang, Qingyong Hu, Hao Liu and Li Liu post Deep Learning for 3D Point Clouds: A Survey, accepted by IEEE TPAMI. It reviews deep learning methods for 3D shape classification, 3D object detection and tracking, and 3D point cloud segmentation.

  6. 2024

    Proceedings of the IEEE best paper

    In 2024, Proceedings of the IEEE names Training Spiking Neural Networks Using Lessons From Deep Learning its Best Paper Award winner; he is one of nine authors, with Jason K. Eshraghian, Max Ward, Emre O. Neftci and others. The tutorial, published in September 2023, shows how to apply modern deep learning principles to train networks of spiking neurons.

  7. 2025–2026

    Clarivate Highly Cited Researcher

    Clarivate names him a Highly Cited Researcher for 2025, and The Australian includes him in its 2026 list of Australia's top 250 researchers.

  8. As of October 2026

    Winthrop Professor at UWA

    He is a Winthrop Professor in Computer Science and Software Engineering within UWA's School of Physics, Mathematics and Computing. His research drives advances in 3D vision, deep learning and multimodal AI, with applications in defence and security, medical imaging, crime prevention, transportation and agriculture.

Career

  1. Since 2003University of Western AustraliaAssociate Professor, Head of School
  2. 1993–2002Queensland University of TechnologyAssociate Lecturer, Lecturer
  3. Queen's UniversityLecturer

Education

  1. Queen's UniversityM.Sc.
  2. Queensland University of TechnologyPhD

From the biography.

  1. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other universityIncludes Mohammed Bennamoun. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other university. We compare doctorate and 5+ year employment links, career timing and current workplaces for 187 major-prize and 1,111 best-paper winners.
  2. More people shaping AI studied at Massachusetts Institute of Technology than at any other institutionIncludes University of Western Australia. We estimate where the people shaping AI studied and where their careers led. Massachusetts Institute of Technology leads with 467 former students, including 291 recorded doctorates.
  3. The company share of AI researchers’ top papers was 31% in 2020–2024 and 8% in 1970–1974Includes University of Western Australia. We estimate where the people shaping AI worked when their most-cited papers appeared: the company share was 31% in 2020–2024 and 8% in 1970–1974, and Google led all organizations in 2020–2024.
  4. Google had the most people shaping AI every year from 2014 to 2025Our estimates of which organizations employed the most people shaping AI each year from 1955 to 2025, tracing the shift from university labs to companies.

Colleagues

People with dated positions at the same organization in the same years.

Sources and credits

Photo: We have no published photo of Mohammed Bennamoun yet. Send us a free-licensed photo.

  1. Mohammed Bennamoun - the UWA Profiles and Research Repository research-repository.uwa.edu.au. Checked 21 October 2026.
  2. Rotational Projection Statistics for 3D Local Surface Description and Object Recognition arxiv.org. Checked 21 October 2026.
  3. A Guide to Convolutional Neural Networks for Computer Vision odbms.org. Checked 21 October 2026.
  4. Wiley-VCH - 3D Shape Analysis wiley-vch.de. Checked 21 October 2026.
  5. Deep Learning for 3D Point Clouds: A Survey arxiv.org. Checked 21 October 2026.
  6. 2024 Best Paper Award Winner - Proceedings of the IEEE proceedingsoftheieee.ieee.org. Checked 21 October 2026.