Professor Phoebe Chen is a Professor and Chair of the Department of Computer Science and Information Technology at La Trobe University, Melbourne, Australia since 2010. Previously, she held roles as Associate Professor at Deakin University (2003–2010) and Senior Lecturer at Queensland University of Technology (1999–2003). She earned her BInfTech (First Class Honours) and PhD in Computer Science from the University of Queensland (2000). Her research focuses on interdisciplinary areas including bioinformatics, artificial intelligence, medical image analysis, deep learning, and data mining. Notable projects include the ARC Centre of Excellence in Bioinformatics and drug design using visualization and machine learning. She has secured over 30 research grants totaling ~$8M, including 13 prestigious ARC grants. Professor Chen chairs international conferences like the Asia-Pacific Bioinformatics Conference and ACM MM, and serves on editorial boards such as IEEE Transactions on Neural Networks and Current Bioinformatics. Her work spans topics like cancer recurrence prediction via multi-omics data, medical image analysis using neural networks, and AI-driven solutions for healthcare challenges. She has published over 300 peer-reviewed papers in top journals/conferences (e.g., Nature Machine Intelligence, IEEE Transactions).
Dmitry A. Konovalov is a researcher at James Cook University with interdisciplinary expertise spanning computational biology, marine ecology, and machine learning applications. His work bridges theoretical and applied research across diverse domains including wildlife conservation, fisheries management, medical imaging, and environmental science. Developed deep learning solutions for underwater fish detection and whale identification Created bioelectrical impedance methods for turtle body condition assessment Contributed to statistical genetics and kinship analysis methodologies Worked on electron transport physics in biomolecular systems Developed biomedical imaging tools for fish measurement and pearl oyster analysis Research interests center on computational methods for ecological monitoring, including image processing, acoustic analysis, and genetic modeling. Current work focuses on developing automated tools for wildlife assessment and environmental data analysis. Recent publications demonstrate expertise in deep learning applications for marine biology, with projects spanning fish biomass estimation, turtle adipose analysis, and whale recognition systems. His work also includes environmental health risk assessment and signal processing in biological contexts.
Gayan Wijesinghe is a Lecturer (Education Focused) at the School of Computing Technologies, RMIT University, Australia. His research interests include Artificial Intelligence, Image Processing, Genetic Programming, and Algorithmic Reasoning. He focuses on developing educational tools to enhance programming skills and cognitive development in students. His work spans algorithm design, evolutionary computation, and applications in computer vision and medical imaging. Research outputs include contributions to genetic programming, image classification, and optimization techniques. Notable projects explore trajectory creation for code writing and parameter optimization in object detection. Collaborations have involved institutions and researchers in evolutionary algorithms and software engineering. No scientific awards or grants are explicitly mentioned in the provided texts. His academic role emphasizes education-focused research and innovation in computing technologies.
Dr Juan Sandino Mora is a Research Fellow in Remote Sensing at Queensland University of Technology (QUT), affiliated with the School of Electrical Engineering & Robotics and the Centre for Robotics. He specializes in developing drone-based solutions for environmental monitoring, biosecurity, and precision agriculture, with a focus on Antarctica's ecological conservation through advanced remote sensing technologies. Education: Doctor of Philosophy (Queensland University of Technology) Bachelor of Engineering (Mechatronics) Research Interests: His work centers on autonomous UAV decision-making, machine learning for object detection, and hyperspectral image processing. Key areas include environmental monitoring in Antarctica, automated search and rescue, and biosecurity applications. He leverages AI and sensor fusion to enhance UAV capabilities in challenging environments. Publications Trends: Recent work emphasizes UAV path planning algorithms, Antarctic vegetation monitoring, and AI-driven pest detection. His 2025 publications highlight advancements in Antarctic lichen mapping and multi-UAV coordination for environmental surveillance. Advising & Grants: No explicitly listed students, though involvement in collaborative projects suggests active research mentoring. Projects include Securing Antarctica’s Environmental Future and UAV-based biosecurity initiatives. Labs/Teams: Participates in QUT's Centre for Robotics and the Securing Antarctica's Environmental Future initiative, advancing autonomous systems for polar ecosystems and precision agriculture.
Dr. Arnick Abdollahi is a Research Fellow at the University of Technology Sydney (UTS) within the Transdisciplinary School. Specializing in Earth and space science informatics, AI, and environmental science, he holds a Ph.D. from UTS and previously served as a Research Fellow at ANU’s Bushfire Research Centre of Excellence. His work focuses on AI-driven solutions for bushfire resilience, remote sensing, and sustainable agriculture. He leads national initiatives like Foragecaster (AI-powered grazing planner) and PastureProbe (radar imaging for livestock feed quality). Notable projects include developing AI frameworks for bushfire risk sensing and vegetation analysis. His research has influenced national strategies in environmental monitoring and risk management. Education: Ph.D., UTS; Research Fellowship, ANU Research interests span wildfire behavior analysis, responsible AI, remote sensing applications, and climate-resilient food security. He chairs Remote Sensing Webinars and serves as Guest Editor for Remote Sensing special issues on AI and wildfire management. Key awards include the 2025 Australian Space Awards’ Rising Star of the Year and 2024 Australian AI Awards recognition. He has secured strategic funding from UTS, WUN, and ARDC, totaling over AUD 2M. Awards: Rising Star of the Year (2025), AI Academic/Researcher of the Year (2024 nominee) Service: Peer reviewer for 35+ journals, TERN data analyst, CSIRO NBIC contributor Teaching: Courses on Machine Learning, Environmental Sensing, and Data Science at UTS and ANU
Professor Gianluca Demartini is a Professor in Data Science and an ARC Future Fellow at the School of Electrical Engineering and Computer Science, Faculty of Engineering, Architecture and Information Technology at the University of Queensland, Australia. He also serves as an affiliate of the Centre for Enterprise AI. His research focuses on human-in-the-loop artificial intelligence systems with applications for public good, bridging structured knowledge graphs and unstructured text analytics to address societal challenges. Dr. Demartini earned his Ph.D. in Computer Science from Leibniz University of Hannover in Germany in 2011, with a focus on Semantic Search. His academic journey includes positions as a Lecturer at the University of Sheffield (UK), post-doctoral researcher at the eXascale Infolab at the University of Fribourg (Switzerland), visiting researcher at UC Berkeley, junior researcher at the L3S Research Center (Germany), and intern at Yahoo! Research (Spain). His research interests span four major interconnected domains: Misinformation (studying human interaction with misinformation and AI-based mitigation strategies), Crowdsourcing and Human Computation (improving efficiency of human-in-the-loop systems), Big Data Analytics (designing scalable algorithms for large datasets), and AI for Public Good (applying AI for societal and environmental benefits). His work consistently addresses real-world challenges in information quality, human-AI collaboration, and ethical technology deployment. Analysis of Professor Demartini's recent publications reveals a clear trajectory toward addressing misinformation through sophisticated human-AI collaboration frameworks, with increasing emphasis on cognitive aspects of fact-checking, data bias management, and strategic application of large language models. His research bridges theoretical advances in information retrieval with practical applications for societal challenges, particularly in media literacy, online safety, democratic discourse, and environmental conservation. Professor Demartini has received numerous prestigious awards recognizing the quality and impact of his work: Best Paper Award at ACM SIGIR International Conference on the Theory of Information Retrieval (ICTIR) in 2023 Best Paper Award at AAAI Conference on Human Computation and Crowdsourcing (HCOMP) in 2018 Best Paper Awards at European Conference on Information Retrieval (ECIR) in 2016 and 2020 Best Demo award at International Semantic Web Conference (ISWC) in 2011 Honorable Mention Award at CSCW 2020 (Top 2% of submissions) As an active supervisor, Professor Demartini currently guides PhD students working on cutting-edge topics including Retrieval Augmented Generation, Human-in-the-Loop Decision Systems for Online Safety, Human-Centred Artificial Intelligence for Democracy, and Bias in Data Pipelines. His research program is generously funded through multiple major grants: ARC Future Fellowships (2025-2028): PBIAS - A Principled Approach to Data Bias Management Swiss National Science Foundation (2022-2025): Large-Scale Political Participation: Issue Identification, Deliberation, and Co-creation ARC Training Centre for Information Resilience (2021-2026) Previous funding from Wikimedia Foundation, Meta, Google, and Facebook for projects on misinformation detection and human-AI collaboration Professor Demartini's work sits at the critical intersection of human computation, information retrieval, and AI ethics. Through extensive collaborations with industry partners including Facebook, Google, Microsoft, Yahoo!, IBM, SAP, and The National Archives (UK), he has developed practical systems that address real-world challenges in misinformation detection, data quality, and human-AI collaboration. His research group actively explores how to make AI systems more transparent, accountable, and beneficial for society through principled human-in-the-loop approaches that leverage both machine intelligence and human expertise.
Greg Falzon is an Associate Professor of Precision Agriculture Systems at Flinders University's College of Science and Engineering. His work focuses on advancing agricultural technology through artificial intelligence, data science, and machine learning to address food security challenges. He holds a PhD in Biomedical Image Analysis from the University of New England (2011) and has a strong record in industry-funded research, including over $26 million in grants. Notable projects include the e-Technology Hub for pest management and the Wild Dog Alert System. His research spans agriculture sectors like cotton, dairy, grains, and livestock, with global impacts. Awards include the 2020 Australian Academy of Technology & Engineering ICM Agrifood Award and the 2016 President’s Medal from the Australian Society of Sugar Cane Technologists. Falzon teaches courses in Artificial Intelligence, Data Science, and Statistics, and actively mentors students in these fields. Key contributions include innovative systems for wildlife monitoring (e.g., Sentinel Bait Station), livestock vocalization detection, and automated cattle counting. His work emphasizes interdisciplinary approaches, combining computational, biological, and physical sciences for tangible agricultural outcomes.
Prof Qiang Wu is a Professor and Deputy Head of School (Research) at the School of Electrical and Data Engineering, University of Technology Sydney. He is a core member of the Global Big Data Technologies Centre. His research focuses on computer vision, image processing, pattern recognition, and machine learning, with applications in video surveillance, biometrics, and human-computer interaction. He holds a B.Eng. and M.Eng. from Harbin Institute of Technology and a Ph.D. from UTS. Prof Wu has led industry collaborations with organizations like Meat & Livestock Australia, Canon, and Westpac Bank. He has authored over 300 publications, including works on pedestrian detection, virtual clothing fitting, and gait recognition patented in the U.S. He serves as an Associate Editor for IEEE Transactions on Multimedia and is a Senior IEEE Member. His research has been funded by grants from bodies like the Australian Egg Corporation and the Australian Research Council. He advises on PhD and Master’s students in computer vision and related areas.
Professor Wanqing Li is a leading academic in machine learning and 3D computer vision at the University of Wollongong, where he serves as Director of the Advanced Multimedia Research Lab (AMRL). He holds a B.Sc. and M.Sc. from Zhejiang University and a Ph.D. from The University of Western Australia. His career includes roles at Motorola Labs (Senior/Principal Researcher) and visiting stints at Microsoft Research. His research focuses on 3D multimedia signal processing, human activity understanding, and medical image processing, with applications in aged care and health monitoring. He has published over 250 papers in top venues like TIP, CVPR, and AAAI, and his work has been recognized with awards including the Motorola CTO’s Award (2003). He currently leads the Centre for Artificial Intelligence (CAI) at UOW and is a co-founder of the Centre for Multimedia Signal Processing and Content Management. Education: B.Sc. (Zhejiang University), M.Sc. (Zhejiang University), Ph.D. (University of Western Australia). Research Interests: Machine Learning (statistical/deep learning), 3D computer vision (human motion analysis, 3D reconstruction), medical imaging, free-viewpoint video systems, and applications in healthcare. He pioneered 3D data-driven human activity understanding, now a cornerstone of modern computer vision. Awards & Recognition: Motorola CTO’s Award (2003), Australia’s top multimedia researcher (2021–2023), elected Associate Editor of IEEE Transactions on Image Processing (2022). Labs/Teams: Director of Advanced Multimedia Research Lab (AMRL), co-founder of CAI and Multimedia Signal Processing Centre.
Lei Wang is a Professor at the School of Computing and Information Technology, University of Wollongong, Australia, and Founding Director of the Centre for Artificial Intelligence. He holds a PhD from Nanyang Technological University (2004) and B.Eng/M.Eng from Southeast University (1996/1999). His research focuses on machine learning, pattern recognition, and computer vision, with applications in medical imaging and AI. He leads the VILA group, emphasizing visual information learning and analysis. Key contributions include over 200 peer-reviewed publications, with citations exceeding 14,600 (Google Scholar). Notable grants include ARC Discovery Project (2021) and NHMRC Ideas Grant (2020). He serves as Action Editor for Transactions on Machine Learning Research, Associate Editor for ACM Transactions on Intelligent Systems and Technology, and holds leadership roles in top conferences like CVPR, NeurIPS, and ICCV. His work spans collaborative perception, medical report generation, and few-shot learning.
Thanh Le Hoang is a Lecturer in Artificial Intelligence at the University of Wollongong's School of Computing and Information Technology. He holds an M.Sc. from the University of Queensland and a Ph.D. from the University of Wollongong, where his thesis received the Examiners' Commendation for Outstanding Thesis. His research spans artificial intelligence , computer vision , and deep learning , with applications in assistive navigation, camouflage generation, and maritime surveillance. His recent publications demonstrate strong focus on transformer architectures, multi-modal learning, and practical deployment of AI systems. Awards include: Examiners' Commendation for Outstanding Thesis Current doctoral supervision includes projects on assistive navigation and statistical AI methods. He has secured multiple grants including funding for AI-based SAR satellite imaging systems and fall risk mitigation in elderly populations.
Dr. Masood Khan is a Senior Lecturer in Mechatronics and Mechanical Engineering at Curtin University’s School of Civil and Mechanical Engineering (Faculty of Science and Engineering). He teaches undergraduate and postgraduate courses including Mechatronic Design Projects, Sustainable Energy Systems, and oversees MPhil/PhD training in mechatronics and robotics. His research focuses on human-centered AI applications, including affective computing, responsible AI, and biomedical engineering. Notable projects include developing AI-driven tongue-strengthening systems for dysphagia therapy and thermal imaging for stress diagnosis, supported by a $1.35M CRC grant. He holds the Research Excellence Award (2006) and is a Fellow of the Higher Education Academy (UK) and Pakistan Academy of Engineering. His work spans over 60 peer-reviewed publications, with recent emphases on generative AI in education and explainable machine learning frameworks. Research Highlights: AI-based tongue-strengthening devices for swallowing therapy Thermal imaging for osseous stress pathology detection Facial thermal feature analysis for emotion recognition Intelligent tutoring systems for industrial training Grants & Awards: $1.35M CRC SmartCrete Grant (2016) $440K equipment support for thermography research Fellowships: Higher Education Academy (UK), Pakistan Academy of Engineering Teaching & Supervision: Leads capstone projects and supervises graduate research in mechatronics, AI, and biomedical robotics. Active in curriculum development for technical education in Brunei and SEAMEO.
Dr. Ljiljana Brankovic is a Professor in Computational Science at the School of Science and Technology, University of New England (UNE). She holds a PhD in Computer Science from the University of Newcastle (1998) and a Graduate Electrical Engineer degree from the University of Belgrade (1987). Her research spans cybersecurity and privacy , algorithms , graph theory , and privacy-preserving machine learning , with significant funding from the Australian Research Council, German Research Foundation, London Mathematical Society, and Royal Society, UK. Education : PhD (Computer Science), University of Newcastle (1998); Graduate Electrical Engineer, University of Belgrade (1987). Her work on privacy integrates technological , ethical , legal , and psychological aspects . In algorithms, she focuses on parameterized approximation algorithms for NP-hard problems. Google Scholar articles (2025–2017) reflect her expertise in combinatorial algorithms , graph theory , privacy-preserving techniques , and machine learning applications in domains like healthcare , explosion prediction , and botanical identification . 2025 : Diverging Assessment: A Student Perspective; Advancements in Medicinal Plant Identification Using Deep Learning. 2024 : Diverging assessments: what, why, and experiences; Incremental and Zero-Shot Machine Learning for Vietnamese Medicinal Plant Classification. 2023–2017 : Domination chain complexity, ambient intelligence for health, upper domination facets, face-antimagic graphs. While no explicit awards are listed, her research funding and editorial roles (e.g., special issues on combinatorial algorithms and privacy) underscore her contributions. She has also co-authored works on statistical database security , mobile agent security , and network privacy , with over 150 publications in theoretical and applied domains.
Maoying Qiao is a Lecturer at Australian Catholic University's Peter Faber Business School, specializing in machine learning and artificial intelligence. Her research spans graph neural networks, computer vision, and probabilistic modeling, with applications in network analysis and 3D vision. Key publications focus on improving graph convolutional networks through negative sampling, adapting stochastic block models for power-law networks, and developing conditional graphical lasso methods for multi-label image classification. Marine science applications include automated catch detection systems for fisheries.
Ross McAree is Professor and Head of the School of Mechanical and Mining Engineering at the University of Queensland, and Fellow of the Australian Academy of Technology and Engineering. His research focuses on mining automation, including pioneering work on autonomous excavators and bulldozers. Research encompasses machinery dynamics, sensor fusion for terrain mapping, and collision avoidance systems. Recent publications address point cloud processing for mining applications, predictive maintenance using machine learning, and hyperspectral ore classification techniques. Publication themes show progression from fundamental sensing/control algorithms toward integrated autonomous mining systems, with increasing emphasis on AI applications in harsh environments. Work consistently addresses industrial challenges through rigorous theoretical development and field validation. Awards & Honors: Fellow of the Australian Academy of Technology and Engineering (ATSE)
