Jo Plested

Dr Jo Plested is an Associate Lecturer at UNSW Canberra with over 10 years of research experience in deep learning, specializing in transfer learning for small specialized datasets. She has made significant contributions to both academia and defense applications through her research, teaching, and leadership as Head of the Deep Learning Group at UNSW Canberra.

Her research interests span multiple interdisciplinary domains including:

• Transfer learning for small specialized datasets
• Bushfire spread prediction modeling with the UNSW Bushfire Research Group
• Visual swarming algorithms for robotics applications
• Physical implementation of neural networks using novel materials
• Quantum computing applications where deep learning helps predict and overcome environmental noise
• Military AI ethics examining implications of targeting systems

Dr Plested has secured over $900,000 in research funding as chief investigator for 5 external grants. Her publication record includes 2 journal articles, 19 conference papers, and 2 preprints. Analysis of her recent publications reveals a strong focus on practical applications of deep learning across diverse domains, with particular emphasis on defense-related applications, environmental modeling, and ethical considerations in AI deployment.

She has supervised over 20 students working on Honours, Masters, and Chief of Defence Force projects, with more than 20 of these student projects published in high-ranking international conferences and journals. Dr Plested created the honours-level Deep Learning course at UNSW Canberra and previously developed and delivered deep learning content for courses at the Australian National University for up to 250 students. She is also part of a team that developed a data science and AI short course specifically for Defence applications.

Currently recruiting PhD students with interests in deep learning, Dr Plested offers scholarships of $35,000 AUD, seeking candidates with backgrounds in deep learning or strong mathematical foundations.