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Biography

Stephen Gould is a Professor of Computer Science at the Australian National University (ANU) and an Australian Research Council (ARC) Future Fellow. He is a former ARC Postdoctoral Fellow, Microsoft Faculty Fellow, Contributed Researcher at Data61, Principal Research Scientist at Amazon Inc, Director of the ARC Centre of Excellence in Robotic Vision, and Amazon Scholar. Stephen received his BSc degree in mathematics and computer science and BE degree in electrical engineering from the University of Sydney in 1994 and 1996, respectively. He received his MS degree in electrical engineering from Stanford University in 1998. He then worked in industry for several years where he co-founded Sensory Networks, which later sold to Intel in 2013. In 2005 he returned to Stanford University and was awarded his PhD degree in 2010. In November 2010, he moved back to Australia to take up a faculty position at the ANU. Stephen has broad interests in the areas of computer and robotic vision, machine learning, deep learning, structured prediction, and optimization. He teaches courses on advanced machine learning, research methods in computer science, and the craft of computing. His main research focus is on automatic semantic, dynamic and geometric understanding of images and videos.

Research Interests

I have broad interests in computer and robotic vision, machine learning, probabilistic graphical models, and optimization. My main research focus is on the application of machine learning techniques (specifically, structured probablistic models and, more recently, deep learning) to geometric and semantic scene understanding. I am also interested in seeing research get used; I collaborate with industry and have previously been involved in founding start-up companies.

Research student supervision

  • Registered to supervise

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Collaborations and top research areas from the last five years

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  • The First to Know: How Token Distributions Reveal Hidden Knowledge in Large Vision-Language Models?

    Zhao, Q., Xu, M., Gupta, K., Asthana, A., Zheng, L. & Gould, S., 2025, Computer Vision – ECCV 2024 - 18th European Conference, Proceedings. Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T. & Varol, G. (eds.). Springer Science+Business Media B.V., p. 127-142 16 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 15106 LNCS).

    Research output: Chapter in Book/Report/Conference proceedingConference Paperpeer-review

    8 Citations (SciVal)
  • Unsupervised Dense Prediction Using Differentiable Normalized Cuts

    Liu, Y. & Gould, S., 2025, Computer Vision – ECCV 2024 - 18th European Conference, Proceedings. Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T. & Varol, G. (eds.). Springer Science+Business Media B.V., p. 287-304 18 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 15098 LNCS).

    Research output: Chapter in Book/Report/Conference proceedingConference Paperpeer-review

    Open Access
    1 Citation (Scopus)
  • 3DInAction: Understanding Human Actions in 3D Point Clouds

    Ben-Shabat, Y., Shrout, O. & Gould, S., 2024, In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. p. 19978-19987 10 p.

    Research output: Contribution to journalConference articlepeer-review

    Open Access
    26 Citations (Scopus)
  • An Empirical Study Into What Matters for Calibrating Vision–Language Models

    Tu, W., Deng, W., Campbell, D., Gould, S. & Gedeon, T., 2024, In: Proceedings of Machine Learning Research. 235, p. 48791-48808 18 p.

    Research output: Contribution to journalConference articlepeer-review

    3 Citations (Scopus)
  • Candidate Set Re-ranking for Composed Image Retrieval with Dual Multi-modal Encoder

    Liu, Z., Sun, W., Teney, D. & Gould, S., 2024, In: Transactions on Machine Learning Research. 2024

    Research output: Contribution to journalArticlepeer-review

    10 Citations (SciVal)