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Ajmal Mian is a Professor of Computer Science at the University of Western Australia (UWA), affiliated with the School of Physics, Maths and Computing. He holds an Australian Research Council Future Fellowship (2022) and leads research in Artificial Intelligence, Computer Vision, and Machine Learning. His work focuses on 3D computer vision, adversarial AI defense, and explainable AI. His research interests include 3D point cloud analysis, face recognition, human action recognition, and remote sensing. He has published over 300 papers and secured major grants from ARC, NHMRC, and DARPA, totaling millions in funding. He has supervised 29 PhD students and mentored 12 postdoctoral researchers. Key projects include 3D diffusion models for scene generation, robust 3D vision systems, and defense against AI deception attacks. He serves as a fellow of IAPR, an ACM Distinguished Speaker, and has editorial roles at IEEE Transactions on Neural Networks and Pattern Recognition. Research Awards: HBF Mid-Career Scientist of the Year, West Australian Early Career Scientist of the Year, IAPR Best Scientific Paper Award. Grants: ARC Discovery Projects, National Intelligence & Security Discovery grants, DARPA grants for AI security. His teaching spans computer vision, machine learning, and programming courses. Collaborations include defense, medical, and agricultural applications.

• Artificial Intelligence
• Computer Vision
• Machine Learning
• +8 more

Qi Wu is an Associate Professor in the Department of Computer Science, School of Computer and Mathematical Sciences, Faculty of Sciences, Engineering and Technology at the University of Adelaide, based at the Australian Institute for Machine Learning on North Terrace campus. His decade-long career in computer vision includes leadership in Vision-and-Language research and prior roles as an ARC Senior Research Associate at the Australian Centre for Robotic Vision (ACRV) and a Postdoc Researcher at the Australian Centre for Visual Technologies (ACVT). His educational qualifications consist of a Master of Science (MSc) in Global Computing and Media Technology (2011) and a PhD in Computer Science (2015), both from the University of Bath, United Kingdom. Research expertise centers on computer vision and machine learning , with pioneering work in Vision-to-Language systems. Key contributions include state-of-the-art models for image captioning and visual question answering (VQA) , contextual object modeling across depictive styles, and object detection using environmental cues. His focus on neural architectures for image-text interaction has established him as a leader in multimodal AI. Scientific recognition includes: Microsoft COCO Image Captioning Challenge winner (2015) Visual Question Answering Challenge winner (2015) Dr Wu is eligible to supervise Masters and PhD students but is currently at capacity; prospective students must email directly for availability discussions. His research has been supported by Australian Research Council (ARC) funding through prior Senior Research Associate and Postdoctoral roles. Current work involves leading a dedicated Vision-and-Language team at the University of Adelaide, building on his extensive publication record in top venues. He maintains active affiliations with the Australian Institute for Machine Learning and previously contributed to the Australian Centre for Robotic Vision (ACRV) and Australian Centre for Visual Technologies (ACVT), reflecting deep integration with national AI research infrastructure.

• Computer Vision
• Machine Learning
• Vision-to-Language
• +4 more

Professor Sarath Kodagoda is a leading academic and researcher in robotics and mechatronics at the University of Technology Sydney (UTS), where he serves as Acting Director of the UTS Robotics Institute. He specializes in sensor fusion, data processing, and machine learning, with a focus on robotic solutions for infrastructure inspection and assistive technologies. His work includes developing the Robotic Remote Lab teaching facilities and founding the iPipes lab for wastewater infrastructure research. Affiliations: UTS Robotics Institute, Faculty of Engineering & IT Leadership Roles: President of the Australian Robotics & Automation Association, Ambassador for NSW Smart Sensing Network His research interests span robotics, sensor networks, and infrastructure robotics, with notable contributions to pipeline inspection and tactile sensing. He has published over 170 papers, attracted $6M+ in grants, and supervised 12 PhD students now working at Amazon, Google, and ABB. Awards include the UTS Medal for Teaching & Research Integration and multiple national/international innovation awards. Recent work emphasizes robotic systems for wastewater infrastructure, assistive robotics for vision-impaired individuals, and advanced sensor technologies. His articles highlight innovations in tactile sensing skins, 3D object detection (e.g., CaLiJD, LMIINet), and pipeline defect detection (PIPE-CovNet+). Grants: Contracts with Amplitel, NBN Co Ltd, and ARC Linkage Projects Labs/Teams: UTS Robotics Institute, iPipes Lab

• Robotics
• Mechatronics
• Sensor Fusion
• +6 more

Professor Guy Wallis is a Professor and Director of Research at the School of Human Movement and Nutrition Sciences, Faculty of Health, Medicine and Behavioural Sciences at the University of Queensland. He is also an Affiliate of the Centre for Sensorimotor Performance. His work bridges visual neuroscience, computational modeling, and applied human factors research, with significant contributions to understanding visual recognition and visuomotor behavior. Education: Bachelor (Honours) of Engineering in Electrical and Electronic Engineering from Imperial College London PhD in Visual Neuroscience from University of Oxford, UK Prof. Wallis's research program combines computational modeling with behavioral studies, many conducted in computer-controlled virtual environments. His work spans visual neuroscience, object recognition, visuomotor control, and simulator-based training. He has made significant theoretical contributions to understanding how visual recognition is achieved in biological systems and how everyday visuomotor tasks are regulated, challenging existing paradigms and offering new insights. His recent publications reveal a strong focus on virtual reality applications, visual-motor integration challenges, and cross-species cognitive studies. There's a clear trend toward investigating how virtual environments can be optimized for training and assessment, with particular attention to visual perception limitations and how humans adapt to these environments across diverse contexts from surgical training to aviation. Scientific Awards and Recognitions: Elected Fellow of the Queensland Academy of Arts and Sciences (2022) ARC Medical Research Advisory Group (2022-2024) ARC College of Experts (2019-2021) CSIRO CSS Human Research Ethics Committee member (2020-2022) UQ Health and Behavioural Sciences Faculty, HDR Supervision Award (2018) ARC Future Fellowship (2011-2014) ARC QEII Fellowship (2003-2007) UQ Postdoctoral Fellowship (2001-2003) Prof. Wallis has successfully secured funding from major organizations including the Australian Research Council, the Human Frontier Science Program, and the Wellcome Trust. His industry partnerships span diverse sectors such as construction training, mining, healthcare, and aerospace. His research has led to the development of novel training programs for health professionals, impacted the design of man-machine interfaces for mining equipment, and informed the design parameters for pilot training systems. As Director of Research, he oversees the research direction of the School of Human Movement and Nutrition Sciences, fostering interdisciplinary collaborations and supporting early-career researchers through his leadership in the Centre for Sensorimotor Performance.

• Visual neuroscience
• Object recognition
• Visuomotor control
• +5 more

Dr. Ehsan Abbasnejad is an Associate Professor at Monash University's Department of Data Science and Artificial Intelligence, and holds adjunct positions at the Australian Institute for Machine Learning (AIML, University of Adelaide) and the Centre for Augmented Reasoning (CAR). He specializes in foundational AI, focusing on vision-language tasks, adversarial machine learning, and reinforcement learning. His work bridges theory with real-world applications in agriculture, energy, healthcare, and sports. Education: PhD in Computer Science from Australian National University (ANU). Research Interests: Machine Learning Theory and Adversarial Defenses Neural Network Robustness and Generalization Multimodal Learning (Vision-Language) Continual and Transfer Learning Applications in Energy, Healthcare, and Robotics Awards: Finalist for Australian AI Academic/Researcher of the Year (2024) Multidisciplinary competition wins (e.g., OzMineral Explorer Challenge) Advising & Grants: Australian Research Council (ARC) Discovery Project on Reinforcement Learning CSIRO's Next Generation Graduate Fund Accepting PhD students in foundational AI and applications Labs & Teams: Director of Foundational Machine Learning & Reasoning at Monash, leading global teams in AI competitions and industry collaborations (Microsoft Research, NEC Labs America).

• Machine Learning
• Computer Vision
• Natural Language Processing
• +3 more

Dr. Feras Dayoub is a Senior Lecturer at the School of Computer and Mathematical Sciences (Faculty of Sciences, Engineering and Technology) at the University of Adelaide , specializing in Embodied AI and Robotic Vision within the Australian Institute for Machine Learning (AIML) . He co-directs the CROSSING French-Australian laboratory for human-autonomous agent teaming and holds an Adjunct position at the Queensland University of Technology (QUT) , serving as an Associate Investigator at its Centre for Robotics . Previously, he was a Chief Investigator at the ARC Centre of Excellence for Robotic Vision . His research focuses on advancing reliable deployment of computer vision and machine learning on mobile robots in real-world environments. Applied projects include agricultural automation , environmental conservation , and autonomous infrastructure monitoring . He has published extensively on topics like object detection , domain adaptation , 3D representation learning , and vision-language navigation , with a particular emphasis on robustness in dynamic and partially observed environments. Dr. Dayoub is also an educator specializing in programming , computer vision , and robotic perception . He contributes to open-source robotics research through tools like AARK (Autonomous Racing Toolkit) and has led teams developing solutions for precision agriculture (e.g., Deepfruits fruit detection system) and environmental monitoring (e.g., Crown-Of-Thorns starfish detection ). Key Collaborations : CROSSING Lab, QUT Centre for Robotics Research Themes : Embodied AI, Robust Perception, Domain Adaptation

• Computer Vision
• Machine Learning
• Robotics
• +6 more

About Vipul Jain: Associate Professor Vipul Jain leads Supply Chain and Logistics Management at RMIT University’s School of Accounting, Information and Supply Chain Management. With over 20 years of academic experience, he has held senior roles at institutions like Victoria University of Wellington (NZ) and IIT Delhi (India), contributing to curriculum development, research strategy, and academic-industry collaboration. His international collaborations span over 20 global institutions, emphasizing interdisciplinary research in supply chain resilience, sustainability, and Industry 4.0. Education & Academic Leadership: Previously served as Programme Director for Master of Technology (Industrial Engineering) at IIT Delhi, leading the establishment of the Modelling and Analysis of Supply Chain (MASC) Lab. Holds editorial roles for journals like International Journal of Intelligent Enterprise and Computers & Industrial Engineering . Research Focus: Specializes in supply chain design, sustainability, circular economy, and business analytics. His work integrates ICT and big data to address complex supply chain challenges, with notable contributions to vaccine distribution, blockchain adoption, and post-pandemic resilience. Over 145 peer-reviewed publications, including high-impact journals like OMEGA and International Journal of Production Economics , reflect his prolific output. Industry Impact: Conducted management training for organizations like JCB and IBM. Served as an expert assessor for New Zealand’s Ministry of Business and Industry and advised the Indian Railways. His research has been funded by the Department of Science and Technology (India) and EU initiatives like FP6 I*PROMS. Awards & Recognition: Ranked #7 in India’s top logistics academics (2018), recipient of Literati Awards (2024, 2020), and multiple editorial leadership roles. Active in global conferences, including co-chairing ANZAM 2023. Labs & Collaborations: Founded MASC Lab at IIT Delhi. Collaborates with Coventry University, Monash University, Hong Kong Polytechnic, and institutions in Switzerland, Spain, Canada, and the U.S. Focus areas include sustainable supply chains, digital transformation, and crisis resilience.

• Supply Chain Management
• Sustainability and Circular Economy
• Innovation and ICT
• +5 more

Dr. Sophie Adams serves as an ARC DECRA Fellow in the Department of Sociology at La Trobe University, where she conducts interdisciplinary research at the intersection of human geography and Science and Technology Studies. Her work critically examines the social dimensions of climate change mitigation and adaptation, with particular emphasis on renewable energy transitions and the societal implications of technological solutions to environmental crises. Adams' research program centers on the politics of environmental knowledge , investigating how climate science informs policy formation and the negotiation of social objectives in decarbonisation processes. She employs qualitative and participatory methodologies to analyze climate change adaptation in energy systems , social license for technological interventions , and the redistribution of expertise in public health and environmental governance. Her scholarship consistently addresses power dynamics in knowledge production and the democratic challenges of sustainability transitions. Analysis of her publication trajectory (2019-2025) reveals increasing focus on energy-social interfaces, with significant contributions to understanding virtual power plants, demand-side management, and the role of modeling in climate policy. Her work demonstrates methodological innovation through participatory approaches while maintaining critical engagement with resilience thinking and adaptation frameworks. Scientific recognition includes: Australian Research Council Discovery Early Career Researcher Award (ARC DECRA) Supported by her DECRA fellowship, Adams leads independent research projects examining the societal dimensions of energy transitions. Her collaborative work spans geography, public health, and energy engineering, as evidenced by interdisciplinary co-authorship patterns. Current projects investigate Australian householders' conditions for participating in virtual power plants and the social implications of climate modeling in adaptation planning, reflecting her commitment to socially relevant environmental research.

• Human Geography
• Science and Technology Studies
• Climate Change Adaptation
• +4 more

Professor Arcot Sowmya is a distinguished academic at the University of New South Wales, serving as Professor in the School of Computer Science and Engineering. With a strong background in both computer science and mathematics, she has established herself as a leading researcher in machine learning and computer vision applications, particularly in medical imaging and diagnostics. Dr. Sowmya earned her PhD in Computer Science from the Indian Institute of Technology, Bombay, along with an MTech in Computer Science, MSc in Mathematics, and BSc in Mathematics from the same institution. Her academic journey has positioned her at the intersection of theoretical computer science and practical medical applications. Her research interests span multiple domains with a primary focus on Machine Learning for Computer Vision . She has made significant contributions to learning object models, feature extraction, segmentation, and recognition techniques. Her work extends into medical image analysis, computer-aided diagnostics, high-resolution remote sensing, and biomedical informatics. More recently, she has applied similar techniques to social sciences domains, developing improved forecasting models for genocide and politicide. Her earlier work also includes contributions to real-time, concurrent, and embedded systems. Analyzing her recent publications reveals a strong trend toward medical applications of computer vision and deep learning. Her work spans from OCT-based glaucoma diagnosis to tumor segmentation, lung disease detection, and breast cancer prognosis. She has successfully bridged computer science with clinical medicine, developing practical tools for disease diagnosis and prediction that incorporate explainable AI approaches. Professor Sowmya's collaborative approach is evident in her extensive publication record across multiple journals and conferences. She has worked with researchers from diverse fields including ophthalmology, oncology, neurology, and public health, demonstrating the interdisciplinary nature of her research. Her laboratory work focuses on developing robust deep learning architectures for medical image analysis, with particular attention to segmentation networks, transformer models, and multimodal data fusion techniques. Her team has developed specialized networks for lung segmentation, tumor detection, and disease classification that address specific challenges in medical imaging.

• Machine Learning
• Computer Vision
• Medical Image Analysis
• +7 more

Prof Raphaël Phan is a Professor and Deputy Head of the School of IT at Monash University Malaysia. His expertise spans security, cryptography, malicious AI, emotion recognition, motion analysis, and generative AI. He has published over 220 papers and led significant projects including privacy-preserving data mining funded by UK MoD and Malaysian government grants exceeding RM4 million. He co-designed the BLAKE hash function (SHA-3 finalist) and has an h-index of 50. Education: PhD in Cryptography (Multimedia University, 2005), MEngSci (2001), BEng (Hons) Computer Engineering (1999). Research focuses on adversarial AI, brain networks, and secure systems. Current projects include Æmbience: emotion-aware virtual assistants using motion magnification. Supervised 15 PhD graduates and 19 current students. Professional affiliations: Chartered Engineer (IET, UK), HEA Fellow, Board of Engineers Malaysia. Recent work emphasizes causal bias detection in micro-expressions, brain tumor detection via advanced YOLOv8, and generative adversarial networks for medical imaging. His work bridges cybersecurity with neuroscience applications.

• Security
• Cryptography
• Malicious AI
• +5 more

Roberto Togneri is a Professor and Senior Honorary Research Fellow at the University of Western Australia's School of Electrical, Electronic and Computer Engineering. He has been affiliated with the university since 1988, following his PhD in 1989. His research focuses on signal processing, speech recognition, machine learning, and biometrics, with notable contributions to audio-visual recognition systems and fraud detection. Education: PhD in Electrical Engineering (University of Western Australia, 1989). Research interests include feature extraction for audio signals, neural network models for speech and speaker recognition, and applications of machine learning to fraud prevention. His work has been recognized with awards such as the Education Innovation Award (ICASSP 2019) and grants from the Australian Research Council (e.g., DP110103336 for a 3D Audio-Visual Speech Recognition System). Key projects include developing robust speech recognition systems in adverse environments and advancing graph-based fraudster group detection using spatio-temporal data. He has also contributed to editorial roles in IEEE Signal Processing Magazine and authored over 214 research outputs. Funding highlights include $279,000 for a 3D audio-visual speech recognition system (2011–2013) and $230,000 for robust speech recognition in hostile environments (2010–2012). His research aligns with UN SDGs related to innovation and infrastructure.

• Signal Processing
• Speech Recognition
• Machine Learning
• +6 more

Piotr Koniusz is a Principal Research Scientist at Data61/CSIRO and an Honorary Associate Professor at the Australian National University (ANU), with an Adjunct role at UNSW. He holds a PhD in Computer Vision from the University of Surrey (2013) and a BSc from Warsaw University of Technology (2004). His research focuses on Foundation Models, Representation Learning, and Few-shot Learning, with contributions to Graph Neural Networks and Adversarial Robustness. Key roles include Program Chair for NeurIPS’25, Senior Area Chair for ICML’25 and ICLR’25, and Workshop Co-Chair for WWW’25. Awards include the Sang Uk Lee Best Student Paper (ACCV’22) and recognition as an Outstanding Area Chair (ICLR 2021–2023). Research interests span Vision-Language Models (VLMs), Generative Adversarial Networks (GANs), and Domain Adaptation. He supervises PhD students at ANU and collaborates with industry on projects like traffic forecasting and ecotoxicology prediction.

• Foundation Models
• Representation Learning
• Graph Learning
• +4 more

Associate Professor Mahsa Baktashmotlagh is an ARC Future Fellow at the School of Electrical Engineering and Computer Science, University of Queensland. Her research focuses on machine learning techniques applied to visual data analysis, biomedical data (e.g., antibacterial activity prediction), and cybersecurity. She holds a PhD from the University of Queensland (2014) and has contributed to over 50 peer-reviewed publications. Her research interests include domain adaptation, deep learning, and robust generalization across domains. Notable contributions include the development of DI-NIDS (a domain-invariant network intrusion detection system) and advancements in open-set domain adaptation. Her work bridges theoretical machine learning with practical applications in healthcare and computer vision. Education: PhD in Machine Learning, The University of Queensland (2014) Awards: ARC Future Fellowship (202X) Research Themes: Domain Adaptation, Cybersecurity, Biomedical AI Her recent work explores challenges in cross-domain generalization, adversarial machine learning, and scalable 3D object detection. She is actively involved in supervising graduate students and collaborates on interdisciplinary projects involving robotics and medical imaging.

• Machine Learning
• Computer Vision
• Domain Adaptation
• +3 more

Dr. Edoardo Bertone is a Senior Lecturer at Griffith University's School of Engineering and Built Environment - Architecture and Design. He holds a PhD in Water Resources Engineering from Griffith University and Bachelor/Master degrees in Civil Engineering from the Polytechnic University of Turin. His research focuses on data-driven modeling, Bayesian Networks, and System Dynamics applied to water resources management, climate change adaptation, and the water-energy nexus. He is affiliated with Griffith's Cities Research Institute and Australian Rivers Institute, collaborating on projects with water utilities, governments, and private entities. Dr. Bertone has received awards such as the 2024 PVC Science Excellence in Teaching and the JSPS Fellowship (2022). He supervises doctoral and master's students in areas like water quality management and climate change impacts. Education: PhD in Engineering (Griffith University, 2015); MEng and BEng in Civil Engineering (Polytechnic University of Turin, 2009-2011). Research Interests: Water quality modeling, drinking water optimization, data-driven prediction, climate change adaptation, and sustainable development goals. He leads over 25 funded research projects, including initiatives on reservoir water quality management in Thailand, real-time nutrient monitoring, and cyanobacteria bloom modeling. Dr. Bertone’s work integrates advanced sensors, machine learning, and Bayesian networks to address environmental challenges. Awards: Listing includes PVC Excellence Awards (2024, 2017), JSPS Fellowship, and recognition as a Rising Star in Queensland Science (2015). Grants & Supervision: Principal supervisor for 10+ doctoral candidates and collaborator on projects funded by Seqwater, CSIRO, and the Ian Potter Foundation. Key grants include $745k for biofertilizer combatting eutrophication and $269k for coagulation optimization models. Dr. Bertone’s contributions extend to urban sustainability, co-editing the book *SeaCities: Urban Tactics for Sea-Level Rise* and developing frameworks for integrating SDGs into architectural education.

• Water quality monitoring and modeling
• Data-driven prediction modeling
• Bayesian Network modeling
• +5 more

Mehrtash Tafazzoli Harandi is an Associate Professor in the Department of Electrical and Computer Systems Engineering at Monash University, part of the Faculty of Engineering. His research focuses on machine learning and computer vision, particularly visual data analysis, with contributions to geometric deep learning, continual learning, and medical imaging. He holds editorial roles at IET Computer Vision , Frontiers in Imaging , and Journal of Imaging . Education & Previous Affiliations: Prior to Monash, he worked at NICTA (Canberra & Queensland Research Labs) and CSIRO-Data61. His Erdős number is 4 via a collaboration path through Richard Hartley. Research Interests: His work spans geometric learning, diffusion models, medical image analysis, and sustainable AI applications. Key areas include unlearning mechanisms in AI, 3D reconstruction compression, and robust MRI reconstruction using contrastive learning. Grants & Projects: He leads projects funded by ARC, US Air Force, and industry collaborations, including 'Can Machines Unlearn?' (ARC, A$790k) and 'Exploiting Geometries of Learning' (ARC, A$420k). His work addresses challenges in lifelong learning, model adaptation, and trustworthy AI from limited data. Awards: Recipient of Best Recognition Paper (IEEE DICTA 2013), NICTA Impact Award (2015), and multiple outstanding reviewer recognitions at top conferences. Teaching: Teaches courses on neural networks, computer vision, and advanced data analysis at Monash University. Supervises PhD students with a focus on mathematical and computational proficiency. Labs/Teams: Collaborates with the Australian Center for Robotic Vision (ACRV) and contributes to interdisciplinary projects at CSIRO-Data61. His research group explores cutting-edge AI applications in healthcare, manufacturing, and environmental sustainability.

• Machine Learning
• Deep Learning
• Computer Vision
• +3 more