This document attempts to be an up-to-date overview of the deep learning landscape in Australia. There are three main sections: researchers, institutes and centres, grants. There is also a brief guide to some of the conferences in this area, which is where much of the top quality research is published. There are certainly omissions and mistakes, if you notice one please get in touch and we’ll quickly fix it. Deep learning is a (successful) subset of the broad field of machine learning and artificial intelligence; this document does not attempt to give an overview of the broader field.


Below is a (no doubt incomplete) list of researchers in Australia for whom deep learning is currently their main research area. This includes e.g. researchers in computer vision whose main tool is deep learning. To make this point even clearer: the list does not include every single person who has published a paper using deep learning. That list would be much larger. Included is a caricature of the interests of each researcher, in the form of a sampling of the conferences/journals that they have published in.

  • Anton Van Den Hengel, Professor University of Adelaide, ECCV, ICCV, ICML, CVPR.
  • Tim Baldwin, Professor University of Melbourne: deep learning for NLP, ACL.
  • Wray Buntine, Professor Monash University, probabilistic deep learning, NeurIPS, ICML, UAI, MLJ, KDD.
  • Ian Reid, Professor University of Adelaide, computer vision and robotics, ICRA, WACV, ICCV.
  • Dacheng Tao, Professor University of Sydney, computer vision, AAAI, CVPR, ECCV.
  • Chunhua Shen, Professor University of Adelaide, computer vision, ECCV, ICCV, ICML, CVPR.
  • Stephen Gould, Associate Professor ANU, computer vision, WACV, ICCV, CVPR, NeurIPS.
  • Jianfei Cai, Professor Monash University, vision and language, CVPR, ECCV, TPAMI, IJCV.
  • Ivor Tsang, Professor University of Technology Sydney, NeurIPS, ICML, JMLR, TPAMI.
  • Yi Yang, Professor University of Technology Sydney, IJCAI, CVPR, AAAI, ICLR, ECCV.
  • James Bailey, CS Professor University of Melbourne, ICCV, ICML, IJCAI, CVPR, AAAI.
  • Qinfeng Shi, Professor University of Adelaide, ICCV, IJCAI, CVPR, ECCV, AAAI.
  • Lexing Xie, Professor ANU, graph data and computer vision, CVPR, AAAI, WACV.
  • Dinh Phung, Professor Monash University, AAAI, PMLR, ICLR.
  • Karin Verspoor, Professor University of Melbourne, deep learning for NLP, particularly for biomedical text, BMC Bioinformatics, ACL.
  • Truyen Tran, Associate Professor Deakin University, AAAI, KDD, ICLR, NeurIPS, CVPR, ICML.
  • Trevor Cohn, Associate Professor University of Melbourne: deep learning for NLP, ICML, ACL, EMNLP.
  • Richard Nock, Adjunct Professor ANU/Sydney, NeurIPS, ICCV, ICML, CVPR, AAAI.
  • Reza Haffari, Associate Professor Monash, natural language processing, many papers in ACL.
  • Qi Wu, Senior Lecturer University of Adelaide, visual question answering, CVPR, AAAI, IJCAI
  • Tongliang Liu, Lecturer University of Sydney, ICML, NeurIPS, CVPR, ECCV, AAAI, IJCAI.
  • Chang Xu, Lecturer University of Sydney, computer vision, ICML, NeurIPS, CVPR, ECCV, KDD, IJCAI, AAAI.
  • Mingming Gong, Lecturer University of Melbourne, NeurIPS, ICML, CVPR, ICCV, ECCV, UAI.
  • Yunchao Wei, Lecturer University of Technology Sydney, computer vision, AAAI, ECCV, CVPR, NeurIPS.
  • Liang Zheng, Lecturer ANU computer vision, ECCV, CVPR, ICCV.
  • Linchao Zhu, Lecturer University of Technology Sydney, NeurIPS, ICCV, CVPR, AAAI, IJCAI.
  • Xiaojun Chang, Senior Lecturer Monash University, CVPR, AAAI, IJCAI, Neural Computation.
  • Shirui Pan, Lecturer Monash University, KDD, AAAI, IJCAI, ICDM.
  • Xingjun Ma, Lecturer Deakin University, ICLR, NeurIPS, CVPR, ICML.
  • Qiuhong Ke, Lecturer University of Melbourne, CVPR, ICCV, ECCV.
  • Peter Vamplew, Associate Professor Federation University, deep RL, AAMAS2020, AJCAI,
  • Richard Dazeley, Associate Professor Deakin University, deep RL, AJCAI.
  • Thanh Thi Nguyen, Senior Lecturer Deakin University, deep RL.

The best venues for publication in deep learning are conferences. See below for a list.

Conferences in deep learning

NeurIPS and ICML are the two most prestigious conferences in machine learning (including deep learning). ICLR, UAI, and AISTATS are smaller-scale conferences that are as prestigious as NeurIPS and ICML, but focusing on specific areas. ICLR focuses on deep learning, UAI focuses on graphical models and causal inference, and AISTATS focuses on the intersection between Statistics and ML. Most of the machine learning theories and new methodologies will appear in these conferences.

Because vision and language are the two most important aspects of artificial intelligence, they have their own top conferences which focus on solving real problems in vision and language. For example, CVPR, ICCV, and ECCV are the top three conferences in computer vision, and ACL, EMNLP, and NAACL are the top three conferences in natural language processing. Because deep learning is currently the leading technology in vision and language, you will see lots of deep learning papers in these conferences.

AAAI and IJCAI and the top two conferences in the general artificial intelligence area. Especially, many excellent papers in traditional AI areas, like automated theorem proving, game playing, and planning, are published in AAAI and IJCAI. Data mining (science) focuses on applying ML methods to solve real-world data analysis problems. The best conference is KDD. ICDM and SDM are also good ones.

A brief summary of the top conferences in each area:

  • Machine Learning: NeurIPS, ICML, ICLR, UAI, AISTATS
  • Computer Vision: CVPR, ICCV, ECCV
  • Natural Language Processing: ACL, EMNLP, NACCL
  • Artificial Intelligence: AAAI, IJCAI
  • Data Mining: KDD, ICDM, SDM, WSDM

Institutes and labs

The technology-oriented institutes, large centres and labs:

Smaller research groups:

Institutes focused on policy and ethics (and on AI broadly, not deep learning):


Training in deep learning:

  • Brane shop specialists in strategic & technical AI education.



The following are all grants containing the terms “deep learning” found using ARC grant search. If you notice that we’ve missed one, please get in touch. DP means Discovery Project, LP means Linkage Project, DE are DECRAS, FT are Future Fellowships and FL are Laureate Fellowships. Listed are CIs only. Grants are listed in reverse chronological order (newest first).

The most common Field of Research (FoR) codes for research in deep learning:

  • 080109 - Pattern Recognition and Data Mining
  • 170203 - Knowledge Representation and Machine Learning
  • 0801 - Artificial Intelligence and Image Processing
  • 080108 - Neural, Evolutionary and Fuzzy Computation


  • DE200101610 “Towards Explainable Multi-source Multivariate Time-series Analysis” University of Queensland (Sen Wang).
  • DP200102427 “Active Visual Navigation in an Unexplored Environment” University of Adelaide (Ian Reid, Seyed Hamid Rezatofighi).
  • DE200101253 “Making Machine Learning Fair(er)” University of Melbourne (Susan Wei).
  • DP200101068 “Weighing the Giants: Using Galaxy Clusters to understand Dark Energy” (Christian Reichardt).
  • DP200101640 “When every second counts: Multi-drone navigation in GPS-denied environments” Queensland University of Technology and University of Technology Sydney (Luis Gonzalez, Jonghyuk Kim).
  • DP200102252 “Deep Learning Architecture with Context Adaptive Features for Image Parsing” Central Queensland University (Brijesh Verma).
  • DP200103718 “Edge-Accelerated Deep Learning” University of Sydney (Bing Zhou, Wei Bao, Nguyen Tran, Dong Yuan).
  • DP200100938 “Automatic Machine Learning with Imperfect Data for Video Analysis”
  • DP200101328 “Adversarial Learning of Hybrid Representation” University of Technology Sydney (Ivor Tsang, Yulei Sui).
  • DP200103797 “Deep Learning that Scales” University of Adelaide (Chunhua Shen).
  • DP200103015 “Deep learning based time series modeling and financial forecasting” University of Sydney (Minh-Ngoc Tran, Junbin Gao, Richard Gerlach).
  • DE200100245 “Bayesian nonparametric learning for practical sequential decision making” University of Technology Sydney (Junyu Xuan).
  • DP200102497 “Non-contact Integrity Assessment of Façade Panels of High-rise Buildings” University of Sydney (Lin Ye, Ye Lu, Dikai Liu).
  • DP200103760 “ Quantum-Inspired Machine Learning” University of Queensland (Ian McCulloch).
  • DE200101439 “ Towards a Reliable and Explainable Health Monitoring and Caring System” Macquarie Univeristy (Wenjie Ruan).
  • DP200102274 “3D Vision Geometric Optimisation in Deep Learning” Australian National University (Richard Hartley, Miaomiao Liu).
  • DP200103207 “ Privacy-preserving Biometrics based Authentication and Security” University of New South Wales (Jiankuun Hu).


  • DP190102181 “Quantification, optimisation, and application of deep uncertainty” Deakin (Saeid Nahavandi, Abbas Khosravi, Chee Peng Lim).
  • DP190102443 “Defense against adversarial attacks on deep learning in computer vision” University of Western Australia (Ajmal Mian).
  • DP190103744 “X-ray imaging and magnetic resonance approach for enhanced oil recovery” UNSW (Crhsitoph Arns, Adrian Sheppard).
  • “Towards Data-Efficient Future Action Prediction in the Wild (ARC DECRA, 2019-2021)” link, Dr. Xiaojun Chang (Monash).
  • LP190100378 “ Deep Learning Augmented Intelligent Grinding Mill Simulation and Design” University of Newcastle (Craig Wheeler, Stephan Chalup, Mark Jones, Gabriel Lodewijks).
  • DP190101294 “ Improving the specificity of affective computing via multimodal analysis” University of Canberra (Roland Goecke, Munawar Hayat).
  • DP190102479 “Integrating biologically-inspired auditory models into deep learning” University of New South Wales (Eliathamby Ambikairajah, Julien Epps, Vidhyasaharan Sethu).
  • FT190100525 “ Adapting Deep Learning for Real-world Medical Image Datasets” University of Adelaide (Gustavo Carneiro).
  • FT190100197 “ Deep Weak Learning for Morphology Analysis of Micro and Nanoscale Images” University of New South Wales (Yang Song).
  • DE190100626 “Towards data-efficient future action prediction in the wild” Monash University (Xiaojun Chang).
  • DP190102353 “Deep attribute-aware hashing for cross retrieval” University of Queensland (Zi Huang).


  • DP180103232 “Deep reinforcement learning for discovering and visualising biomarkers” University of Adelaide (Gustavo Carneiro, Andrew Bradley, Lyle Palmer).
  • LP180100697 “Music can speak for you: making music with a deep net partner” Western Sydney University (Roger Dean, Tara Hamilton).
  • LP180101309 “Dynamic Deep Learning for Electricity Demand Forecasting” RMIT (Mahdi Jalili, Xinghuo Yu, Peter Sokolowski).
  • DP180100106 “Towards interpretable deep learning with limited examples” UTS (Ivor Tsang, Yi Yang).
  • DP180103491 “Intention-aware cooperative driving behaviour model for Automated Vehicles” QUT (Andry Rakotonirainy, Ronald Schroeter).
  • DP180103023 “Deep visual understanding: learning to see in an unruly world” University of Adelaide (Andton van den Hengel, Damien Teney).
  • DP180102060 “ Protein structure prediction by deep long-range learning” Griffith (Yaoqi Zhou, Kuldip Paliwal).
  • DE180100203 “Deep space-time models for modelling complex environmental phenomena” Wollongong (Andrew Zammit Mangion)
  • “Generative Adversarial Networks for Behaviour Discovery (DST, 2018-2019)” link Prof. Dinh Phung. (Monash)
  • FL180100060 “Deep learning: the first billion years with next generation Telescopes” Swinburne Univeristy of Technology (Karl Glazebrook).


  • LP170101255 “An automated system for the analysis of road safety and conditions” Central Queensland University (Brijesh Verma).
  • DE170101259 “Zero-shot and few-shot learning with deep knowledge transfer” University of Adelaide (Lingqiao Liu).
  • FL170100117 “On snapping up semantics of dynamic pixels from moving cameras” University of Sydney (Dacheng Tao).
  • “Deep Learning for Cybersecurity (DST/Data61, 2017-2021)” link Prof. Dinh Phung (Monash)
  • DP170104600 “ Hierarchical information processing in the primate visual cortex” Monash Univeristy (Marcello Rosa, Hsin-Hao Yu, Mark McDonnell, Adam Morris).
  • DE170101259 “ Zero-shot and few-shot learning with deep knowledge transfer” University of Adelaide (Lingqiao Liu).


  • LP160101162 “ A data science framework for modelling disease patterns from medical images” Sydney (Dagan Feng, Jinman Kim).
  • DP160102686 “Learning Deep Semantics for Automatic Translation between Human Languages” University of Melbourne (Trevor Cohn, Gholamreza Haffari).
  • LE160100090 “Computational infrastructure for developing deep machine learning models” University of Adelaide (Ian Reid, Svetha Venkatesh, Peter Corke, Dr Mohammed Bennamoun, Stephen Gould, Anton van den Hengel, Chunhua Shen, Anthony Dick, Gustavo Carneiro, Dinh Phung, Niko Suenderhauf, Ajmal Mian).


  • DP140102794 “Automated analysis of multi-modal medical data using deep belief networks” University of Adelaide (Gustavo Carneiro, Andrew Bradley).
  • DE140100180 “Advancing Dense 3D Reconstruction of Non-rigid Scenes by Using a Moving Camera” Australian National University (Yuchao Dai).