- 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.
Deb commenced with the FSP on 18/2/2021 and has now pursued another pathway with RMIT as a lecturer from 15/7/2022.
- My current research involves developing novel algorithms using deep learning for the biological applications with spatiotemporal data. I have obtained my master's degree from International Institute of Information Technology (IIIT), Bangalore, India and will be receiving my PhD degree from School of Computer Science, Queensland University of Technology (QUT), Brisbane in 2021. My PhD thesis explored visual representation learning for temporal and spatiotemporal data, while addressing the challenges of domain- and task-agnosticism. Prior to joining PhD, I was working as a researcher in TCS Research, the research wing of India's leading IT firm, where I developed deep learning-based solutions for temporal data for prognostics and prediction applications.
- I have a Ph.D. from the University of Sydney in reinforcement learning, with a focus on continuous control. My research interests are in developing autonomous agents that can solve sequential decision problems. Outside of research I enjoy reading historical non-fiction and fantasy fiction, as well as video games that emphasise building large things measured with big numbers.
- I attained a B.Sc from the University of Queensland (UQ). I furthered my studies at UQ by completing a Ph.D in Physics. My research was in the field of quantum error correcting codes. During this research I utilised many techniques relating to inference. I developed a framework which maps a large class of quantum codes onto simple graphical representations. Using these representations, I was able to use belief propagation algorithm for error detection. I enjoyed developing this framework and continued to broaden my knowledge of other concepts relating to machine learning. Currently I am working in the Hybrid Prediction group where I am adapting neural networks to approximate bushfire dynamics. Fast ensemble predictions enable better uncertainty estimation which can be used to make more informed decisions.
- I completed my PhD at the Australian National University (ANU) where I developed vision and language AI agents that can interact with humans by translating visual information from an image to perform downstream tasks such as describing what’s in the image, or answering intelligent questions about it. I have also worked as a Research Assistant at the ANU College of Engineering and Computer Science, where I co-supervised a student team developing an AI system to quantify the severity of corrosion in steel from an image. This project was done in collaboration with a leading Australian steel manufacturing company.
Before moving to Australia, I completed my Masters in Biomedical Engineering from the University of British Columbia (UBC), Vancouver, Canada. At UBC, I worked as a Research Assistant at the Biomedical Signal and Image Computing Laboratory (BiSICL) and Human Communication Technology (HCT) Lab. For my Master's thesis, I worked closely with Speech Language Pathologists (SLPs) from Vancouver General Hospital (VGH), BC, Canada and Medical University of South Carolina (MUSC), SC, USA; and developed a 3D biomechanical model of the head and neck region from video fluoroscopy, MRI and CT images for diagnosing patients suffering with swallowing disorders (i.e., dysphagia). My research interest include vision and language AI agents, biomedical image processing, multi task learning and general-purpose ML/AI algorithms.
Nina Ghanbari Ghooshchi
- I completed my PhD at Griffith University, which focussed on constraint-based automated planning and business process modelling. My research interests are in improving the quality of decision-making algorithms with the help of machine learning techniques. Currently, I am working on a project that aims at improving the algorithms for solving combinatorial optimizations problems with reinforcement learning. Before starting my PhD studies, I was working as a lecturer at the computer engineering department at Urmia University of Iran.
- 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
- I received my PhD from Ludwig Maximilians University of Munich in 2018 and I worked as a Postdoc at the University of Melbourne before joining the Future Science Platform at CSIRO. My research is focussed towards the understanding of the statistical properties of Active Galactic Nuclei, galaxy cluster cosmology, polarisation of radio sources, anisotropies in Cosmic Microwave Background radiation and the development of machine learning algorithms for Astronomy. In my role at CSIRO, I will be developing machine learning techniques to classify radio sources in the ASKAP's Evolutionary Map of the Universe (EMU survey), and construct consolidated radio catalogs.
- I 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.
- 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.
- 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.
- My research is focused on Explainable Artificial Intelligence (XAI). I have been trying to explain time series models developed using deep neural network and graph neural network to humans in generic terms. In doing so, I have been exploring any latent relationships between features and targets. I finished my PhD in Information Sciences in December 2020 from the University of South Australia. Within my PhD I developed ensemble methods for text classification incorporating XAI. I have also completed a MSc in Computer Science from St Francis Xavier University, Canada. As part of my MSc, I developed a framework for healthcare workflow verification using Computation Tree Logic (CTL). Additionally, I have obtained a BSc in Computer Science and Engineering from Khulna University, Bangladesh. In between studies, I have worked as part of the teaching faculty at Khulna University, Bangladesh and as a software engineer at different organizations.
- My research is focused on the development of machine learning algorithms for automated detection of harmful algal bloom species in microscopic imagery. My PhD research investigated a range of mathematical, statistical and computational mechanisms for improving the quality of satellite data, including deconvolution and spatial resolution enhancement. During my PhD studies, I completed a Graduate Diploma in Marine Science through the CSIRO-UTAS Quantitative Marine Science program where I gained experience in quantitative approaches to a variety of marine science applications. Beyond CSIRO, I worked in a university setting and in private industry, developing computational systems for the optimisation of aquaculture planning, architecting data management systems for drone-based multispectral imagery, and developing progressive web applications using modern web technologies. Returning to CSIRO in the NCMI Information and Data Centre (IDC) in 2018, I was involved in the design and development of scientific data management systems which included leading the Marlin software development team and delivering a major release of the O&A Marlin Metadata System, as well as project managing and leading the software development of the O&A CRC atmospheric composition and chemistry processing system. I am passionate about marine imaging. During my role in the IDC, I customised and managed the operation of the Video Annotation and Reference System software platform used for the annotation of O&A deep-sea coral imagery. I am currently working on machine learning and artificial intelligence and have active research interests in self-supervised learning, unsupervised cross-domain image transfer using adversarial networks, and the design and development of remotely deployable deep learning imaging systems.
- I received my Bachelor and Master's degree from the Beijing Institute of Technology, before doing my PhD study on Data analytics at University of Technology Sydney. My research interests include multi-view learning, factorisation models and sparse-high dimensional data. I have verified my methods on some real industrial applications, e.g., fault prediction for railway infrastructure, crowd-flow prediction for a railway network. I am also interested in theoretical analysis of machine learning algorithms, including convergence and generalisation error analysis, especially for high-dimensional and sparse data. Currently my research involves biosecurity-related problems. I will combine spatial-temporal analysis with learning methods like meta-learning.
- Jia is a statistician, her research is focused on the development of statistical and computational methods with applications in different scientific fields. She got a PhD and a Msc degree both in applied statistics from Finland. Her research interests lie primarily in Bayesian statistics, machine learning, spatial & spherical data analysis, optimal design and image analysis.
- 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.
- I completed a degree in Applied Mathematics and another in Geography at the ANU, before doing a Masters in Theoretic Statistics at ETH Zürich, following up with a PhD in stochastic simulation for statistical inference at UNSW Sydney. In between I was an Asialink fellow in Bandung, Indonesia in their music exchange program. My research applications have been in music, social media, and wireless communications. I have used methods spanning stochastic signal processing, quantitative risk management and miscellaneous tricks from neural network learning. I hope to combine these methods into tools for statistical inference in non-Gaussian stochastic differential equations, with the notion this can be useful for principled modelling in complex heavy-tailed phenomena such as environmental systems. To that theme, I am currently working with the groundwater modelling team experimenting with neural surrogate models in that application area. In my spare time, I remix Indonesian death metal music and run a community costume studio.
- 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.
Dimity commenced with the FSP on 7/06/2021 and has now pursued another pathway with QUT as a lecturer from 25/7/2022.
- I completed a computer and electronic B.Eng. at Griffith University, Brisbane from 2013-2016. During the final year of the B.Eng. I completed a semester-long internship at the Hong Kong University of Science and Technology (HKUST), where I worked on automatic speech recognition (ASR). I conducted my PhD at the Signal Processing Laboratory at Griffith University since 2017, which focused on deep learning for robust speech processing (speech enhancement, robust speech recognition and speaker identification/verification). I enjoy eating great food, being in the great outdoors, diving, and playing guitar. I also am an avid follower of the National Football League (NFL) and Formula 1.
- Saimun Rahman recently completed his doctoral research in a collaboration between CSIRO Data61 and the University of Wollongong. His doctoral research was about higher-order visual representation learning with deep networks. He received his M.Sc. (by Research) in Computer Vision and B.Sc. in Computer Science & Engineering. He has been an awardee on many scholarships and honours throughout his life for academic and research excellence, including the Data61 PhD scholarship. His current research interests include computer vision, robotics and machine learning. He regularly reviews papers from top artificial intelligence conferences such as CVPR, ECCV, ACM MM etc., and serves on the program committees of various international conferences.
- 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.
- 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.
Suk Yee Yong
- 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.
- 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.