I have broad interests in computer and robotic vision, machine learning, structured models, and optimization. My main research focus is on the application of machine learning techniques to geometric and semantic scene understanding in images and video. I am also interested in seeing research get used; I collaborate with industry and have previously been involved in founding start-up companies.
I am always looking for motivated students who are interested in doing research with me. I encourage ANU students to contact me about undergraduate or PhD research projects. Students outside of ANU who are interested in doing a PhD with me should visit my homepage and read the ANU application procedures before contacting me.
Stephen Gould is a Professor of Computer Science at the Australian National University (ANU). He is also an Australian Research Council (ARC) Future Fellow and Amazon Scholar. He is a former ARC Postdoctoral Fellow, Microsoft Faculty Fellow, Contributed Researcher at Data61, Principal Research Scientist at Amazon Inc, and Director of the ARC Centre of Excellence in Robotic Vision. 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.
Activities & Awards#
Workshops, Conferences, and Journals
I have regularly served as program committee member or reviewer for the following conferences and journals: CVPR, ECCV, ICCV, ICML, IEEE PAMI, IEEE TIP, IJCV, JMLR, NeurIPS, RSS, UAI and others.
Tutorial on Deep Declarative Networks at ECCV 2020, organized by Itzik Ben-Shabat with Dylan Campbell, Steven Diamond, Brandon Amos and Akshay Agrawal.
Co-organizing a workshop on Deep Declarative Networks at CVPR 2020, with Anoop Cherian, Dylan Campbell and Richard Hartley.
Co-organized the second Robotic Vision Summer School (RVSS 2016) with Chuong Nguyen and Sareh Shirazi.
Co-organized a workshop on Semantics for Visual Reconstruction, Localization and Mapping at CVPR 2015, with Ian Reid and Silvio Savarese.
Organized the inaugural Robotic Vision Summer School (RVSS 2015).
Co-organized a tutorial on Learning and inference in discrete graphical models at CVPR 2014, with Nikos Komodakis, Dhruv Batra and Nikos Paragios.
Co-organized the Machine Learning Summer School (MLSS) for Beijing, 2014 with Hang Li and Zhi-Hua Zhou.
Co-organized the ICCV 2013 Workshop: Inference for probabilistic graphical models (PGMs) on 2 December 2013, with Qinfeng (Javen) Shi, Chunhua Shen, Jason L. Williams and Tiberio Caetano.
Co-organized the Angry Birds AI Competition at IJCAI 2013, with Jochen Renz and XiaoYu (Gary) Ge.
Co-organized the Workshop on Inference in Graphical Models with Structured Potentials at CVPR 2011, with Julian McAuley, Tiberio Caetano, Pushmeet Kohli and Pawan Kumar.
Selected Invited Talks and Tutorials Invited talk titled “Ikea Assembly and
Other Experiences in Dataset Labelling” given at DanaXa Industry Forum, 2021.
Invited talk titled “Deep Declarative Networks” given at IVCNZ, 2019. [slides (5MB)]
Invited talk titled “AI and Advanced Machine Learning” given at IP Australia, 2019.
Invited talk titled “When you come to a fork in the road, take it” given at RoboVis, 2018.
Invited talk titled “Advances in Visual Perception” given at the Huawei Asia-Pacific Innovation Day, 2016.
Tutorial on structured prediction for computer vision given at MLSS Sydney, 2015. [slides (3MB) | [video] (https://www.youtube.com/watch?v=AOBYCfPD-gI)] Tutorial on inference in discrete graphical models given at CVPR, 2014. [ slides (3MB) ]
Simulation of the Monty Hall Problem from my Introduction to AI talk for Girls in ICT Day, 2014. Also Eliza.
Invited talk titled “Consistency Potentials for Scene Understanding: from Pairwise to Higher-order” given at ICCV Workshop on Graphical Models for Scene Understanding: Challenges and Perspectives, 2013. [ slides ]
Technical workshop for computer science PhD students at ANU, 3-4 May, 2012.
ANU Workshop on Developing and Debugging Machine Learning Algorithms, September 2011. [ Sessions 1-3 (2.9MB)] Tutorial titled “Markov Random Fields for Computer Vision” given at the Machine Learning Summer School (MLSS 2011), 13-17 June 2011, Singapore. [ slides (part 1) | slides (part 2) | slides (part 3) ]
Invited talk titled “Probabilistic Models in Holistic Scene Understanding” given
at Stanford Computer Science Faculty Luncheon, 2010.
Invited talk titled “A Region-based Approach to Scene Understanding” given at The First IEEE Workshop on Visual Place Categorization (VPC), 2009.
Invited talk titled “Multi-modal Robotic Vision: Detecting Objects and People” given at Honda Research Institute, April 2008.
Teaching COMP4680/8650: Advanced Topics in Machine Learning (Semester 2, 2021)
COMP4680/8650: Advanced Topics in Machine Learning (Semester 2, 2020)
COMP4680/8650: Advanced Topics in Machine Learning (Semester 1, 2019)
COMP4680/8650: Advanced Topics in Statistical Machine Learning (Semester 2, 2018)
COMP1040: The Craft of Computing (Semester 2, 2016)
COMP4680/8650: Advanced Topics in Statistical Machine Learning (Semester 2, 2016)
COMP1040: The Craft of Computing (Semester 2, 2015) with Mark Reid
COMP2550: Advanced Computing R&D Methods (Semester 1, 2015)
COMP4680/8650: Advanced Topics in Statistical Machine Learning (Semester 2, 2014) with Justin Domke and Xinhua Zhang
COMP2550: Advanced Computing R&D Methods (Semester 1, 2014). Co-taught with ENGN2706 by David Nisbet
COMP4680/8650: Advanced Topics in Statistical Machine Learning (Semester 2, 2013) with Justin Domke and Xinhua Zhang
COMP3130/2550: Computer Science Group Project (Semester 1, 2013)
COMP8650: Advanced Topics in Statistical Machine Learning (Semester 2, 2012) with Bob Williamson and Mark Reid
COMP3130: Computer Science Group Project (Semester 1, 2012)
COMP3130: Computer Science Group Project (Semester 1, 2011)