- Deb commenced with the MLAI FSP on 18/2/2021 and has now pursued another pathway with RMIT as a lecturer from 15/7/2022.
Previously...I am working as a CSIRO Early Research Career (CERC) Postdoctoral Fellow, in CSIRO, where my work will contribute towards the Future Science Platform (FSP) of Machine Learning & Artificial Intelligence (MLAI) and its applications to anomaly detection in fisheries. Previously, I worked as a Postdoctoral Research Associate at RMIT University, Australia, working on projects related to food automation using computer vision and deep learning. I completed my Doctor of Philosophy from the University of Melbourne, Australia, where I worked on the topic of visual sensing for indoor positioning, which aims at the development of an infrastructure-free indoor positioning technology that is suitable for mass implementation. I have also worked in the field of object tracking and positioning in indoor environments, using computer vision and deep learning. My research works are published in international peer-reviewed journals and international conferences.
- Tian worked with the MLAI FSP during 2021.
Previously...I worked as a Marie Curie Early Stage researcher at AGH University of Science and Technology, Krakow, Poland, from 2017 to 2019. In the meanwhile, I conducted my PhD at AGH University, focusing on using Bayesian statistics for inferring the occurrence of abnormal operation in industrial processes. Currently I am working on some methods to extract key information from climate data for the use of data assimilation. My research interest include Bayesian inference, nonlinear dimension reduction and machine learning.”
My research involves addressing the gap between computer vision and robotic vision by developing learning techniques for reliable robotic vision. I will soon be receiving my PhD from the Queensland University of Technology, where I worked with the Australian Centre for Robotic Vision and QUT Centre for Robotics. My PhD thesis explored uncertainty estimation for object detectors in open-set conditions - in these conditions, novel objects are be encountered and traditional detectors typically make perception failures. Prior to my PhD, I received my Bachelors in Mechatronics Engineering at QUT.
- Xuhui worked with the MLAI FSP during 21-22.
Previously...I am currently working on developing advanced machine learning algorithms to address the Ocean and Atmosphere problems. I obtained my PhD degree in UTS and bachelor's degree in mathematical statistics at University of Science and Technology of China. My research focuses on developing Bayesian machine learning algorithms and applying them into real world problems.
- Yiqing worked with the FSP from 2020 to 2023 when he moved to Aquawatch.
Previously...I received my bachelor and master’s degrees from Beihang University, in 2012 and 2015, respectively, and my PhD degree from the University of New South Wales, Canberra Campus, in 2019, all in remote sensing. Before joining CSIRO in November 2020, I worked as a Data Scientist in the innovation industry for one and a half years. My research focuses on remote sensing and machine learning, with a view to applying these exciting techniques to ecology and agriculture. I served as the Inaugural Chair of the IEEE Geoscience and Remote Sensing Society University of New South Wales Canberra Student Chapter. I was a winner of the IEEE Geoscience and Remote Sensing Society Grand Student Challenge fund. I was also the recipient of the Best Student Paper Award at the Third International Conference on Agro-Geoinformatics
- Ben worked with the MLAI FSP from 2020 to 2022 when they moved to the CINTEL FSP.
Previously...joined CSIRO in 2020 as a Postdoctoral Fellow in the newly formed Machine Learning and Artificial Intelligence Future Science Platform. After on-boarding remotely from Melbourne, I'm now located in Brisbane. Prior to this position, I studied Computer Science and Mechatronics Engineering at Monash University, where I then completed my PhD in Computer Systems Engineering as part of the Australian Research Council Centre of Excellence for Robotic Vision. My doctoral thesis focused on the development of efficient algorithms for the representation and retrieval of high dimensional big data. I continue to have many research interests in related fields including multi-modal representation and retrieval, human-in-the-loop active learning and non-linear dimension reduction. Working at CSIRO I'm keen to continue investigating and developing fundamental data science tools, and am excited to leverage the breadth of scientific domains covered by the organisation in pursuit of seeing these tools utilised to their full potential. Outside of research work I also enjoying flexing my creative muscles with board game design and recipe development.
- Worked in the MLAI FSP until early 2023 when he accepted a role with Curtin University.
Previously...My current research focuses on developing biologically informed machine learning models through data integration and biological understanding for advancing analytical frontiers in areas with direct applications to animal and plant breeding. My research direction also focuses on developing advance technologies for diagnosing and managing various human diseases including covid-19, multiple sclerosis, and diabetes. I am also interested in developing AI models for emotion recognition and affective computing.
Prior to joining the Machine Learning & Artificial Intelligence Future Science Platforms (MLAI FSP) at CSIRO and Biological Data Science Institute (BDSI) at ANU in January 2021, I served as a postdoctoral research fellow in the Centre for Health Informatics (CHI) at Macquarie University, Sydney. During that time, I helped with developing AI models for diagnosing COVID-19 securely from acoustics signals. From November 2018 to November 2020, I served as a postdoctoral / research fellow and lecturer at OHIOH (Our Health in Our Hands) Grand Challenge and Research School of Computer Science (RSCS), ANU. During this fellowship, I focused on building predictive models for people living with multiple sclerosis and/or diabetes. I earnt my PhD in computer science in July 2019 from ANU, with my research focused on developing machine learning models for human computing and cognitive science.
I have taught over 20 university courses in different universities since January 2012, including the University of Information Technology & Sciences (UITS) and Khulna University of Engineering & Technology (KUET) in Bangladesh, University of Canberra and ANU. I am an Associate Fellow of Higher Education Academy (AFHEA), awarded by the UK Advanced Higher Education Academy.
- Dimity commenced with the MLAI FSP on 7/6/2021 and has pursued a pathway with QUT as a lecturer from 7/2022.
Previously...My research involves addressing the gap between computer vision and robotic vision by developing learning techniques for reliable robotic vision. I will soon be receiving my PhD from the Queensland University of Technology, where I worked with the Australian Centre for Robotic Vision and QUT Centre for Robotics. My PhD thesis explored uncertainty estimation for object detectors in open-set conditions - in these conditions, novel objects are be encountered and traditional detectors typically make perception failures. Prior to my PhD, I received my Bachelors in Mechatronics Engineering at QUT.
- Liyuan worked with the MLAI FSP during 21-22.
Previously...I completed my PhD degree in computer vision at the Australian National University. My research interests include image deblurring, flow estimation, depth completion, and high-speed image reconstruction with event cameras. As part of my work with the FSP, I am currently working on feature extraction with multi-modal architectures by fusing LiDAR data and RGB data.
- Gajan worked with the MLAI FSP during 2021.
Previously...I have a Ph.D. from University of New South Wales in speech processing and machine learning (ML). My research was on anti-spoofing for voice biometrics systems where I developed novel feature extraction, modelling and variability compensation techniques. I won the best student paper award at 10th APSIPA ASC in 2018. As a ML engineer, I have also developed and deployed efficient ML algorithms for various applications in network traffic optimization, supply chain, Internet of things and medical anomaly detection. My research interests are in developing machine learning, deep learning and statistical data science techniques to solve real-world decision problems. Currently, I am attached to both CSIRO and UNSW Data Science hub.
- Jing worked with the MLAI FSP during 2021.
Previously...I completed a Ph.D degree with Research School of Electrical, Energy and Materials Engineering, the Australian National University. My main research interests include saliency detection, weakly supervised learning, generative model, uncertainty estimation. I won the best student paper prize at DICTA 2017, the best deep/machine learning paper prize at APSIPA ASC 2017 and the best paper award nominee at IEEE CVPR 2020. In my spare time. I love cooking and playing badminton.
- Muming worked with the MLAI FSP during 2021.
Previously...Muming finished her PhD at University of Technology Sydney, with research areas focusing on machine learning and computer vision. She is now with the MLAI FSP/Data 61 under the supervision of Peyman Moghadam. Muming’s PhD thesis studied the vision task of crowd counting in surveillance scenes. This is a dense prediction task with a focus on 2D images. Currently she is extending her research to high-dimensional 3D or 4D data. There will be both richer information and more challenging problems to be exploited and studied in the high-dimensional space. Working at CSIRO, Muming is excited to research on novel machine learning algorithms for multimodal spatialtemporal data and also keen to solve real world problems with both internal and external collaborations.
- Yan worked with the MLAI FSP during 2021-22 and has a new role with Amazon from 8/2022.
Previously...I completed my PhD in 2019 from Monash University where my research focused on a family of learning algorithms categorised as ensemble learning methods. I investigated incorporating decision forests and ferns within deep learning frameworks and applying them to computer vision for robotics. These applications include a range of tasks including image classification, image segmentation, image synthesis and video prediction. Under the ML/AI Future Science Project and I am looking at investigating machine learning and deep learning methods for manufacturing and materials science applications. In my spare time, I enjoy cooking, calisthenics and travelling.
- Wei is now a research scientist with the Health and Bio Security Business Unit.
Previously...I completed a PhD in Biomedical Engineering at the University of New South Wales, with a focus on low-power wearable fall detection systems. Prior to my PhD, I completed a joint undergraduate programme in Control and Automation, at Wuhan University in China and the University of Dundee in Scotland, followed by a Master’s in Biomedical Engineering at the University of Dundee. In between, I worked as a research intern at the Institute of Medical Sciences & Technology in Scotland and Medtronic Shanghai Innovation Centre. My research interests are in smart home and sensor-based assistive technologies for aged care. Currently, I am developing a health monitoring system based on the time series data collected from in-home sensors. In my free time, I enjoy playing/watching soccer and basketball.
Suk Yee Yong
- Suk Yee has a new role with Macquarie University.
Previously...Across one continent to another, I finished my undergrad at Pennsylvania State University in the US and continued my graduate studies in Australia. Early this year, I completed my PhD in Astrophysics from the University of Melbourne. My PhD focused on studying the most luminous object in the universe known as quasars using combination of modelling, statistical methods, and machine learning. My research involves finding rare and unknown objects (including search for extra-terrestrial intelligence?) in astronomical data sets. By harnessing the capability of ML and AI, I aim to tackle challenges not only in big data, but also the big universe.
- Al-Mamun has a new role with the University of Western Australia.
Previously...I have formal qualifications in computer science, bioinformatics and quantitative genetics. My current research focuses on the development of machine learning methods to integrate high-dimensional multi-omic data to i) improve prediction accuracy of commercially important phenotype in both plants and animals and ii) understand the various factors determining biological outcomes. My specific research interests are artificial intelligence and machine learning for optimization of biological problems, computational methods and statistical analysis of high-throughput genomic data, the use of functional knowledge in genomic selection and functional integration of GWAS and gene expression data.
- Worked with the MLAI FSP until 2022 when they accepted a role as a research scientist with Data61's Software and Computational Systems Group.
Previously...I completed my PhD jointly at CSIRO’s Data61 and Swinburne University of Technology. I have been working on addressing problems of adversarial examples and neural backdoors against deep neural networks. Before moving to Australia, I received my bachelor’s degree from Huazhong University of Science & Technology (HUST), China. My primary research interest resides in adversarial machine learning and AI-related cybersecurity. My other research interests include learning theory, applied machine learning, malware detection, and complex networks. Under the ML/AI Future Science Platform, I am currently investigating the security & privacy problems of neural networks in an adversarial context.