TESS Seminars

TESS Seminar Series 2022

TESS seminars feature international, Australian, and local JCU researchers whose work falls within Tropical Environmental and Sustainability Sciences. Speakers are typically established or postdoctoral researchers.

Where: JCU Cairns, Nguma-bada campus, Smithfield (Crowther Lecture Theatre A03-003)

When: Wednesdays on the advertised date and time

Who: All are welcome

Time: 4 pm to 5 pm (AEST)

Followed by drinks & nibbles

COVID-19 safe measures guidelines of Queensland Government's roadmap to easing restrictions

*The seminars is also live streamed on Zoom and YouTube:

If you are hosting a visitor or would like to give a seminar, please contact the seminar coordinator, Dr Yoko Ishida by email to yoko.ishida@jcu.edu.au

2022 TESS ANNUAL SCIENCE MEETING is confirmed! Save the date 2 and 3 of November

Next Seminar

Interested in adopting AI in your domain research but lacking labelled data? An introduction to contrastive learning

When: 12 October, Wednesday 2022, 4-5 pm (AEST)

Where: Crowther Lecture Theatre A03-003 (Smithfield, Nguma-bada campus)

Speaker: Dr Kevin Tao | Lecturer, Electronic Systems and IoT Engineering | Nguma-bada campus

Abstract: Artificial intelligence (AI) might be no longer unfamiliar to you as it has been widely used around us. For example, when you receive an email, the email application might generate several reply options for you to choose from based on the content of the received email. In addition, in some professional applications, AI can be used to assist, for example, biologists in performing image analysis and predicting protein structure. Usually, training those AI systems requires a supervised deep learning approach, which requires a large amount of labelled data. Obtaining large amounts of labelled data is an expensive exercise, especially for applications requiring specialized domain knowledge. The contrastive learning technique, proposed in the research community in recent years, is a self-supervised learning method without the need for labels. In this talk, we will firstly introduce deep learning and latent space. Then we will introduce the contrastive learning technique. Finally, at a high level, we will show our recent work that uses contrastive learning for Radar detection points-based instance segmentation. Hopefully, by the end of this seminar, you can see the potential of contrastive learning and are motivated to think about how to adopt this technique to assist your domain research

Biography: Tao Huang (Kevin) received his PhD in Electrical Engineering from The University of New South Wales, Sydney, Australia. He received his M.Eng. degree in Sensor System Signal Processing from The University of Adelaide, Adelaide, Australia. He received his B.Eng. Degree in Electronics and Information Engineering from Huazhong University of Science and Technology, Wuhan, China. Dr Huang is a lecturer in Electronic Systems and IoT Engineering and the program coordinator for the Master of Engineering (Professional) (Internet of Things and Data Engineering) at James Cook University, Cairns, Australia. He was an Endeavour Australia Cheung Kong Research Fellow, a visiting scholar at The Chinese University of Hong Kong, a research associate at the University of New South Wales, and a postdoctoral research fellow at James Cook University. He has co-authored a Best Paper Award from the 2011 IEEE WCNC, Cancun, Mexico. He is a co-inventor of one patent on MIMO systems. He has served in several international conferences as TPC chair, track chair, program vice chair, and local chair. Dr Huang is a senior member of IEEE, serving as the MTT- S/Com Vice-Chair and Young Professionals Affinity Group Chair for the IEEE Northern Australia Section. He is a member of the IEEE Computational Intelligence Society and the Communication Society. He is a topic editor of Electronics MDPI. Before academia, Dr Huang held various positions in the industry, such as senior software engineer, senior data scientist, project team lead, and technical advisor. His research includes wireless communications, IoT, and interdisciplinary research that requires deep learning, such as object detection, instance segmentation, remote sensing, image processing, computer vision, and pattern recognition. More about his recent research work can be found at https://research.jcu.edu.au/portfolio/tao.huang1/ and the Intelligent Computing and Communications Lab https://www.taoicclab.com/

Coming up

Tittle: TBA


When: 19 October, Wednesday 2022, 4-5 pm (AEST)

Where: live streamed - Crowther Lecture Theatre A03-003 (Smithfield, Nguma-bada campus)

Speaker: Dr Meghna Krishnadas | Senior Scientist | Centre for Celluar and Molecular Biology (CSIR)

Abstract: TBA

Biography: TBA