Research Scientists and Postdocs
- 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.
- Ashley Gillman is a Research Scientist with the Neuroimaging Team at CSIRO’s Australian eHealth Research Centre, based in Townsville. His research interests are in multimodal image analysis and reconstruction (especially PET/MR), image registration, spatiotemporal (dynamic) medical imaging, physically-constrained learning, and medical imaging protocol design. He is currently investigating multi-site harmonisation (domain adaptation in medical imaging between different scanners); MR-guided PET reconstruction applied to Epilepsy; MRI-based motion correction and prediction applied in PET imaging and radiotherapy; and multimodal spatiotemporal atlas techniques for application in preterm neonatal MRI imaging.
- 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 am a research scientist whose long-term goal is to see robots able to cope with the unpredictable real world. I attained my PhD on adaptable systems for autonomous weed species recognition in 2018 from the Queensland University of Technology (QUT). Between 2018 and 2022 I was a research fellow with the Australian Centre of Robotic Vision (ACRV) and QUT Centre for Robotics (QCR) within QUT. There I worked on creating new evaluations measures and challenges for robotic vision systems, performance predictors for weed classification systems, and some work on using implicit models for visual place recognition. Career highlights include development of the probability-based detection quality (PDQ) evaluation measure for probabilistic object detection and hosting several robotic vision challenges at international robotics and computer vision workshops. At CSIRO I hope to apply computer vision and machine learning to solve real-world problems that are of benefit to the general public within autonomous systems.
- I completed my PhD at the Queensland University of Technology (QUT) in 2021, concerning the topic of visual place recognition. My research focused on enabling autonomous systems the ability to navigate the world autonomously, using only a vision sensor, even during challenging environmental conditions such as at night-time or during adverse weather conditions. I then worked as a post-doc at QUT, where I worked on a couple of projects. The first was a project with Amazon in which I furthered my PhD research, then I worked on a project with Ford where I specialised in 6-DoF visual localisation for autonomous vehicles. Prior to my PhD, I also worked in a couple of roles spanning electronics manufacturing and electrical engineering. Currently I am working in the Spatiotemporal team in the MLAI-FSP, where I will continue researching solutions to autonomous navigation and perception using machine learning techniques.
- 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.
- Xuesong Li is currently an postdoctoral research fellow in ANU BDSI and visiting research fellow in CSIRO FSP, working on machine learning approaches for biomass prediction. Before this, he was an advanced engineer researching on autonomous driving at Huawei. He received his Ph.D. degree on robotics from University of New South Wales (Sydney) in 2020, and the PhD thesis is about object detection for intelligent robots. His bachelor and master’s degree were achieved from Wuhan University of Technology in 2013 and 2016. His current research interests include machine learning, point cloud processing, object detection, computer vision, and robotics perception.
- 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 Electrical and Computer Systems Engineering degree at Monash University, Melbourne (2018). I completed a PhD degree of computer vision and machine learning at Monash University (2022). During my PhD, my primary research interest was on Few-Shot Learning (FSL) to tackle the efficient adaptation problem. I was also interested in applications of FSL in the context of computer vision, such as visual tracking and object detection. My current research interest is on the 3D computer vision with deep learning solutions.
- 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.
- 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.
- My current research interests are machine learning security and privacy, system and software security.
- Ryan is a Research Associate at the University of New South Wales and a Visiting Scientist in CSIRO’s Data61. He works on foundational questions in statistical machine learning, developing data-analytic tools for 21st-century problems. Ryan’s interests and expertise include high-dimensional statistics, time series forecasting, and mathematical optimisation. Besides fundamental research on these topics, he has worked on applied problems spanning economics, chemistry, and psychology. Prior to joining UNSW/CSIRO, Ryan was a postgraduate student in the Department of Econometrics and Business Statistics at Monash University.
- After studying Mechatronics Engineering and Mathematics at the University of Queensland, I worked as an electronics hardware engineer for two years. I then completed a PhD in Machine Learning, before joining the MLAI FSP. I enjoy exploring connections between traditional machine learning techniques and modern deep learning approaches. Broadly, my research interests are in Bayesian neural networks, Gaussian processes, and infinitely wide neural networks. As part of my work with the FSP, I am currently applying some of these techniques to astronomical data. In my spare time, I enjoy playing the violin.
- 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.
- Xinlin is a CERC Fellow at CSIRO Energy Business Unit, Australia. He has two years of Postdoctoral Fellow experience in the Donald Bren School of Information and Computer Sciences at the University of California Irvine, CA, USA. Xinlin received his Ph.D. from Seoul National University, South Korea, with an “Outstanding Doctoral Dissertation” award. He is trained in multi-disciplinary education backgrounds, including Mechanical Engineering, Electrical Engineering, and Computer Science. His research experiences broadly cover AI, AI-for-Energy, Sustainable Development, the Internet of Things (IoT), and Smart Manufacturing. Xinlin’s work is highly regarded by various Top-Tier academic journals. As the first author, he has published nine journal papers in reputable journals such as Applied Energy, Energy, and Sustainable Cities and Society. He also has won “Best Paper,” “Most Cited Paper,” and “Best Poster” awards at different international academic conferences. Moreover, Xinlin has led and built the world’s first AI-centric smart village in Africa, demonstrating how AI can improve energy efficiency and address climate change to achieve sustainable development.