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Dr Jo Plested

Dr Jo Plested

Associate Lecturer
UNSW Canberra
School of Systems & Computing

Jo Plested is an Associate Lecturer who has been researching deep learning for 10 years. Her expertise is focused on transfer learning for small specialised datasets. Jo created the new honours level course Deep Learning at UNSW Canberra. For three years, Jo lectured in and produced all course material and assessments for the deep learning section of honours and masters level courses “Neural Networks, Deep Learning and Bio-inspired Computing” for up to 250 students at the Australian National University. Jo is also part of a group that developed a data science and artificial intelligence short course for Defence. She has supervised over 20 students doing Honours, Masters and Chief of Defence Force (CDF) one and two semester projects. She has mentored many more coursework students in research undertaken as part of her course. Over 20 of these projects are published as high ranking international conference and journal papers.
E-mail
j.plested@adfa.edu.au
  • Journal articles | 2020
    Yao Y; Plested J; Gedeon T, 2020, 'Information-preserving feature filter for short-term EEG signals', Neurocomputing, 408, pp. 91 - 99, http://dx.doi.org/10.1016/j.neucom.2019.11.106
  • Conference Papers | 2022
    Le V-H; Plested J; El-Fiqi H, 2022, 'Deep learning based detection of high similarity objects on limited hardware robots', in Ishibuchi H; Kwoh C-K (ed.), Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence (IEEE SSCI) December 4 – 7, 2022, Singapore, IEEE SSCI, Singapore, pp. 1770 - 1771, presented at 2022 IEEE Symposium Series on Computational Intelligence (IEEE SSCI) December 4 – 7, 2022, Singapore, Singapore, 04 December 2022 - 07 December 2022, https://rpsonline.com.sg/rps2prod/ieee-ssci2022/pdf/IEEE-SSCI22-341.pdf
    Preprints | 2022
    Plested J; Gedeon T, 2022, Deep transfer learning for image classification: a survey, , http://arxiv.org/abs/2205.09904v1
    Conference Papers | 2022
    Wise C; Plested J, 2022, 'Developing Imperceptible Adversarial Patches to Camouflage Military Assets From Computer Vision Enabled Technologies', in Ishibuchi H; Kwoh C-K (ed.), Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence (IEEE SSCI) December 4 – 7, 2022, Singapore, IEEE SSCI, Singapore, pp. 1772 - 1773, presented at 2022 IEEE Symposium Series on Computational Intelligence (IEEE SSCI) December 4 – 7, 2022, Singapore, Singapore, 04 December 2022 - 07 December 2022, https://rpsonline.com.sg/rps2prod/ieee-ssci2022/pdf/IEEE-SSCI22-344.pdf
    Conference Papers | 2022
    2022, 'FERM: A FEature-space Representation Measure for Improved Model Evaluation'
    Conference Papers | 2021
    Plested J; Shen X; Gedeon T, 2021, 'Rethinking Binary Hyperparameters for Deep Transfer Learning', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 463 - 475, http://dx.doi.org/10.1007/978-3-030-92270-2_40
    Preprints | 2021
    Plested J; Shen X; Gedeon T, 2021, Non-binary deep transfer learning for image classification, , http://arxiv.org/abs/2107.08585v2
    Conference Papers | 2021
    Shen X; Plested J; Caldwell S; Gedeon T, 2021, 'Exploring Biases and Prejudice of Facial Synthesis via Semantic Latent Space', in Proceedings of the International Joint Conference on Neural Networks, http://dx.doi.org/10.1109/IJCNN52387.2021.9534287
    Conference Papers | 2020
    Cui R; Plested J; Liu J, 2020, 'Declarative Residual Network for Robust Facial Expression Recognition', in Communications in Computer and Information Science, pp. 345 - 352, http://dx.doi.org/10.1007/978-3-030-63820-7_39
    Conference Papers | 2020
    Evans N; Plested J; Gedeon T, 2020, 'Exploring the Correlation between Random Convolutional Architectures and the Trained Equivalent', in Proceedings of the International Joint Conference on Neural Networks, http://dx.doi.org/10.1109/IJCNN48605.2020.9207465
    Conference Papers | 2020
    Shen X; Plested J; Yao Y; Gedeon T, 2020, 'Pairwise-GAN: Pose-Based View Synthesis Through Pair-Wise Training', in Communications in Computer and Information Science, pp. 507 - 515, http://dx.doi.org/10.1007/978-3-030-63820-7_58
    Conference Papers | 2020
    Wang Y; Plested J; Gedeon T, 2020, 'MultiTune: Adaptive Integration of Multiple Fine-Tuning Models for Image Classification', in Communications in Computer and Information Science, pp. 488 - 496, http://dx.doi.org/10.1007/978-3-030-63820-7_56
    Conference Papers | 2019
    Kennardi A; Plested J, 2019, 'Evaluation on Neural Network Models for Video-Based Stress Recognition', in Communications in Computer and Information Science, pp. 440 - 447, http://dx.doi.org/10.1007/978-3-030-36802-9_47
    Conference Papers | 2019
    Li W; Plested J, 2019, 'Pruning Convolutional Neural Network with Distinctiveness Approach', in Communications in Computer and Information Science, pp. 448 - 455, http://dx.doi.org/10.1007/978-3-030-36802-9_48
    Conference Papers | 2019
    Liu T; Plested J, 2019, 'Using evolutionary algorithms for hyperparameter tuning and network reduction techniques to classify core porosity classes based on petrographical descriptions', in Communications in Computer and Information Science, pp. 750 - 757, http://dx.doi.org/10.1007/978-3-030-36808-1_82
    Conference Papers | 2019
    Liu Y; Yao Y; Wang Z; Plested J; Gedeon T, 2019, 'Generalized Alignment for Multimodal Physiological Signal Learning', in Proceedings of the International Joint Conference on Neural Networks, http://dx.doi.org/10.1109/IJCNN.2019.8852216
    Conference Papers | 2019
    Plested J; Gedeon T, 2019, 'An analysis of the interaction between transfer learning protocols in deep neural networks', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 312 - 323, http://dx.doi.org/10.1007/978-3-030-36708-4_26
    Conference Papers | 2019
    Vos A; Plested J, 2019, 'Using an evolutionary algorithm to optimize the hyper-parameters of a cascading neural network', in Communications in Computer and Information Science, pp. 758 - 765, http://dx.doi.org/10.1007/978-3-030-36808-1_83
    Conference Papers | 2019
    Yao Y; Plested J; Gedeon T; Liu Y; Wang Z, 2019, 'Improved Techniques for Building EEG Feature Filters', in Proceedings of the International Joint Conference on Neural Networks, http://dx.doi.org/10.1109/IJCNN.2019.8852302
    Conference Papers | 2018
    Yao Y; Plested J; Gedeon T, 2018, 'A Feature Filter for EEG Using Cycle-GAN Structure', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 567 - 576, http://dx.doi.org/10.1007/978-3-030-04239-4_51
    Conference Papers | 2018
    Yao Y; Plested J; Gedeon T, 2018, 'Deep Feature Learning and Visualization for EEG Recording Using Autoencoders', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 554 - 566, http://dx.doi.org/10.1007/978-3-030-04239-4_50
    Conference Papers | 2017
    Plested JF; Gedeon TD; Zhu XY; Dhall A; Geocke R, 2017, 'Detection of universal cross-cultural depression indicators from the physiological signals of observers', in 2017 7th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos, ACIIW 2017, pp. 185 - 192, http://dx.doi.org/10.1109/ACIIW.2017.8272612

Jo Plested has received over $900,000 in research grants, in deep learning algorithms and application areas.  She has been the chief investigator for 5 external grants.

I am looking for PhD students who have an interest in deep learning and a background in deep learning or a strong maths background. ***Scholarships of $35,000 (AUD) are available for Ph.D. students ***

Dr Jo Plested is Head of the Deep Learning Group at UNSW Canberra. We are available to collaborate with domain experts to apply deep learning models in the best way to any application area. Some of the research groups/areas I work with are:

- The UNSW Bushfire Research Group working on bushfire spread prediction and related tasks.

- Swarming and robotics on visual swarming

- Physical computation on implementing neural networks using physical materials

- Quantum computing on using deep learning to predict and overcome the effects of environmental noise

- Military and AI ethics on assessing the implications of AI trageting systems and related tasks