Dr. Chang Xu is an Associate Professor in Machine Learning and Computer Vision at the University of Sydney's School of Computer Science. He holds a Bachelor of Engineering from Tianjin University and a PhD from Peking University. His research focuses on machine learning, data mining, and their applications in AI and computer vision, including multi-view learning, visual search, and face recognition. He is an ARC Future Fellow and a member of the Sydney Southeast Asia Centre and The Net Zero Institute. Education: B.E. in Engineering (Tianjin University), Ph.D. in Computer Science (Peking University). His research interests emphasize handling heterogeneous data, exploring data variety, and developing algorithms for robust AI systems. His work includes adversarial robustness, neural architecture search, and efficient deep learning models. Research trends in his articles include adversarial robustness in neural architectures, efficient vision transformers, multimodal 3D style transfer, and underwater image restoration. Key contributions span image restoration, video super-resolution, and lightweight network design. He has advised multiple PhD and master's students on topics like diffusion models, radar image synthesis, and graph similarity. Awards: ARC Future Fellow. Collaborations focus on cross-domain data integration and AI applications. His labs and teams explore generative models, robust learning, and scalable robotics policies. Recent work includes diffusion models for action segmentation and robust vision-language systems.
Tien Tsin Wong is a Professor in the Department of Data Science & AI at Monash University, Australia. Previously, he served as a Professor at the Chinese University of Hong Kong (1999–2024) and held a Visiting Assistant Professor position at the Hong Kong University of Science and Technology (1998–1999). His research focuses on Generative AI, Computer Graphics, Computer Vision, and Computational Manga, with significant contributions to GPU techniques, image-based rendering, and multimedia compression. Education: He earned a B.Sc. (1992), MPhil (1994), and PhD (1998) in Computer Science from the Chinese University of Hong Kong. Research Interests: His work bridges computational techniques with artistic applications, particularly in manga and animation. Notable areas include generative models, diffusion-based video synthesis, and physically plausible scene generation. His research aligns with UN Sustainable Development Goals through innovations in education and digital accessibility. Awards : He has received the 2004 Young Researcher Award, 2005 IEEE Transactions on Multimedia Prize Paper Award, and two international invention medals (Geneva 2018, Asia Hong Kong 2019). Editorial Roles : He serves as an Associate Editor for Computer Graphics Forum , IEEE Transactions on Visualization and Computer Graphics , and Computational Visual Media . His editorial work underscores his influence in advancing visualization and graphics research. Labs/Teams : While not explicitly named, his collaborations span global institutions, focusing on computational manga, generative AI, and GPU-optimized techniques. His work often involves interdisciplinary teams addressing challenges in digital media and AI.
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.
Lexing Xie is a Professor in Computer Science at the Australian National University. He leads the ANU Computational Media Lab ( http://cm.cecs.anu.edu.au ) and the ANU Integrated AI Network. His work focuses on the intersection of machine learning, social media analysis, and multimedia understanding. Dr. Xie's research broadly focuses on innovative design and use of machine learning algorithms, especially on large-scale graph data and collective behaviour. His recent work spans several key areas: Popularity in social media -- understanding, predicting, and optimization Multimedia knowledge graphs, vision and language integration Humanising machine intelligence through better understanding of social dynamics His publications reveal a strong trend toward understanding information diffusion patterns in social media, particularly through visual content. He has made significant contributions to the study of visual memes, popularity prediction using point processes, and multimodal learning that connects vision with language. His work often bridges theoretical machine learning with practical applications in social media analysis. Dr. Xie has received recognition for his research, including an Honourable Mention at CSCW 2019 for his work on attention flow in online video networks. His research has been supported by collaborations with major institutions including IBM Research and Columbia University. As an advisor, Dr. Xie has mentored numerous students who have gone on to contribute significantly to publications in top-tier conferences. His lab, the ANU Computational Media Lab, serves as a hub for interdisciplinary research connecting computer science with social sciences.
Dr. Chandranath Adak is an Assistant Professor at the Department of Computer Science and Engineering, Indian Institute of Technology Patna (IIT Patna), and concurrently serves as a Visiting Fellow at the School of Computer Science, University of Technology Sydney (UTS), Australia. He holds a Ph.D. in Analytics from UTS (2019) and previously served as an Assistant Professor at Indian Institute of Information Technology Lucknow (IIITL) and the Centre for Data Science at JIS Institute of Advanced Studies, Kolkata. Education: Ph.D. (Analytics), University of Technology Sydney (2019) M.Tech., Computer Science and Engineering, University of Kalyani (2014) B.Tech., Computer Science and Engineering, West Bengal University of Technology (2012) Research Interests: His work spans Computer Vision, Deep Learning, Reinforcement Learning, Document Image Analysis, and AI-driven solutions for healthcare, forensics, and industrial automation. He has pioneered methods in biomarker detection using electrochemical sensors combined with ML models, handwriting analysis for educational and forensic applications, and anomaly detection in industrial systems. His research bridges theoretical advances with real-world applications, such as medical diagnostics and quality control systems. Publications: His recent work includes innovations in biosensor-based medical diagnostics, handwriting evaluation systems, and transformer networks for historical document analysis. These contributions reflect a focus on interdisciplinary applications of AI across healthcare, cultural heritage preservation, and industrial automation. Awards: Start-up Research Grant, SERB, India (2022) Dr. Kalam Doctoral Scholarship, UTS (2018) IEEE CIS Graduate Student Research Grant (2017) Senior Member, IEEE (2024) Teaching & Supervision: Taught courses at UTS including 'Introduction to Data Analytics' and supervised research in machine learning and computer vision. His mentorship emphasizes hands-on experience with AI tools and real-world problem-solving. Labs & Teams: Engaged in collaborative projects at UTS's CIBCI Centre and Griffith University's IIIS, focusing on computational intelligence and sensor-driven AI systems.
Felicity McArdle is a Professor in the Department of Early Childhood Education within the Faculty of Education at Queensland University of Technology. With a distinguished career spanning over two decades, her work centers on reimagining early childhood pedagogy through critical lenses of social justice, art education, and play-based learning. Her research has significantly influenced Australian early childhood policy and practice. McArdle's research interests demonstrate a consistent commitment to transformative education. She critically examines how art and play function as vehicles for social justice, challenging neoliberal discourses in early childhood education. Her work on refugee children's artistic expression and anti-bias curriculum development reveals her dedication to equity-oriented pedagogy. Through collaborative drawing methodologies and visual research approaches, she has pioneered innovative ways of understanding children's perspectives. Her publication trends reveal a sophisticated evolution from foundational work on visual arts in education (2001-2010) toward increasingly critical examinations of power, policy, and social justice (2011-2021). The consistent citation impact of her work—particularly her 2002 paper 'Early Childhood Art Education: A Palimpsest' with over 13,000 citations—demonstrates her field-defining contributions to reconceptualizing art education as critical praxis. McArdle has held significant editorial roles including co-editing the influential 2013 volume 'Young Children, Pedagogy and the Arts' and serving on the editorial board of Australian Journal of Early Childhood. Her collaborations with leading scholars like Susan Grieshaber, Jennifer Sumsion, and Linda Knight have produced seminal work on intentional teaching and play-based learning. Her research methodology expertise centers on visual and arts-based approaches, particularly collaborative drawing techniques that engage children as active research participants. This methodological innovation reflects her commitment to ethical research practices that honor children's agency while generating meaningful insights about early childhood experiences.
Dr. Sirui Li is a Lecturer at Murdoch University's School of Information Technology within the College of Science, Technology, Engineering and Mathematics. Her research focuses on Artificial Intelligence, Natural Language Processing (NLP), Machine Learning, Knowledge Graphs, Data Analysis, Temporal Data, and Multi-modal Models, with applications in medicine, agriculture, and mining. She collaborates with industry partners like BHP and has published in journals such as Food Chemistry and Knowledge and Information Systems , as well as conferences like ICSME and IJCNN. Education: Bachelor of Advanced Computing (Honours) in Computer Science at Australian National University Master of Computing (Specialising in AI) at ANU Ph.D. in Information Technology (AI) at Murdoch University Research interests include interdisciplinary applications of AI, such as clinical coding privacy solutions, disease spread modeling, and drug repurposing for pandemics. Her work emphasizes practical industry integration, demonstrated through awards like the 2024 EMNLP Best Demo Award and the 2023 Iron Ore Circuit Hackathon innovation prize. Professional roles include IEEE Western Australia Section committee membership, conference chair positions, and peer review for top journals. She actively mentors students pursuing Honours, Master's, or PhD projects in her areas of expertise.
Dylan Campbell is a Lecturer in Computing at the Australian National University (ANU), affiliated with the ANU College of Systems & Society. His research focuses on computer vision, optimization, and robotics, particularly in 3D vision and deep learning applications. He has held prior roles as a Research Fellow at the University of Oxford’s Visual Geometry Group and ANU’s Australian Centre for Robotic Vision. Campbell holds a PhD from ANU (2018) and a BE in Mechatronic Engineering from UNSW (2012). Research interests include geometric sensor alignment, neural radiance fields, and differentiable optimization layers. He actively supervises students (7 PhD/DPhil, 3 MEng, 9 honours) and teaches advanced courses in computer vision and robotics. Notable awards include the Marr Prize Honourable Mention (2017) and the IEEE Australia Council Postgraduate Student Paper Competition (2018). He has organized workshops at ECCV and CVPR, served as a reviewer for top conferences like CVPR/ICCV/ECCV, and contributed to datasets like SEED4D and RefRef. His work emphasizes efficient training of neural networks and leveraging symmetries in data for long-range connections.
Professor Robert McLaughlin is a faculty member in the School of Biomedicine at the University of Adelaide, affiliated with the Faculty of Health and Medical Sciences. He leads the Bioengineering Imaging Group and serves as Managing Director of the start-up Miniprobes. His research focuses on developing optical imaging technologies, including non-invasive tools for blood flow assessment and miniaturized imaging probes. He has secured over $14M in research grants and holds an h-index of 45 with 98 journal papers, 7 patents, and 2 book chapters. Prof. McLaughlin’s career includes roles at the University of Oxford and Siemens Medical Solutions, followed by academic leadership since 2007. His innovations span optical coherence tomography (OCT), fluorescence imaging, and dual-modality systems for clinical applications. Awards include the 2014 WA Innovator of the Year, 2015 Australian Innovation Challenge, and 2016 South Australian Premier’s Research Fellowship. His research emphasizes practical medical solutions, such as imaging needles for deep-tissue diagnostics and optical devices for real-time surgical monitoring. The Bioengineering Imaging Group collaborates with industry and academia to translate technologies into clinical practice.
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).
Dr Nga Wun (Doris) Li is a Senior Lecturer at the University of Technology Sydney (UTS) within the Faculty of Design, Architecture and Building, Department of Fashion and Textiles. She leads research in seamless knitting technology, smart textiles, and sustainable fashion innovation. PhD in Fashion & Textile Design (HK PolyU, 2021) BA (Hons) in Fashion & Textiles (HK PolyU, 2012) Certifications in Wholegarment Machine (Shima Seiki, Japan) and Higher Education Pedagogy (UTS) Her research focuses on functional garments, knitting technology, and bio-informed textile design. Key projects include smart socks for DVT prevention , one-size sports bras , and buoyant swimwear for children . She combines knitting with machine learning and 3D printing for material innovation. Recent publications highlight her work on compression textiles (2025), wearable glucose sensors (2025), and knitted metasurfaces for acoustic comfort (2024). She has secured AU$52,000+ in grants across 5 funded projects. Fashion Design Award (Jeanswest, China) Outstanding Presentation & Research Paper Awards (2024) Dr Li supervises PhD and MPhil students in smart textiles and sustainable design while coordinating machine knitting courses. She collaborates with AiDLab (Hong Kong) and Powerhouse Museum (Australia).
Professor Judy Kay is a leading academic in Computer Science at the University of Sydney, holding a Chair in Human-Centred Technology Research within the Faculty of Engineering and IT. Her work focuses on designing personalized systems that empower users to control their data, particularly in lifelong learning and health contexts. She leads the Human Centred Technology Research Cluster and is Editor-in-Chief of the International Journal of Artificial Intelligence in Education (IJAIED). Education: Studied at the University of Sydney, inspired to pursue academia due to its multidisciplinary environment and student collaboration opportunities. Research interests include user modeling, scrutable AI systems, and interfaces for personal informatics. Current projects span virtual reality learning environments, health data dashboards, and ethical AI transparency. Her work has led to commercialized technologies like the Personis system. Publications include over 200 peer-reviewed works in top venues like CHI, UbiComp, and AIED conferences. Awards highlight her contributions to computing education and AIED, including the 2023 Lifetime Achievement Award. Advises 4 current PhD/Master’s students focusing on topics like Open Learner Models in VR and health decision-making tools. Collaborates with multidisciplinary teams across computer science, health sciences, and education. Labs/Teams: Leads research groups in the School of Computer Science and collaborates with the Charles Perkins Centre on health-related projects.
Mark Billinghurst is a Professor and Director of the Australian Research Centre for Interactive and Virtual Environments at UniSA STEM, University of South Australia. His work focuses on Augmented Reality (AR), Virtual Reality (VR), and empathic computing, emphasizing remote collaboration, human-computer interaction, and inclusive design. He has authored influential books like Computer-Supported Collaboration: Theory and Practice (2024) and pioneered systems such as Empathy Glasses and gaze-tracking interfaces for collaborative tasks. Education & Affiliations: Mark Billinghurst is affiliated with the Empathic Computing Laboratory and has collaborated globally with institutions like Keio University, Nokia, and the University of Canterbury. His research spans AR applications in manufacturing, healthcare, and social interaction, with a focus on improving accessibility and user experience. Research Interests: Dr. Billinghurst’s research explores AR/VR for remote guidance, wearable technologies, and emotional interfaces. He investigates how gaze cues, multimodal feedback, and AI can enhance collaboration and empathy in virtual environments. His work bridges technical innovation with human-centric design, addressing challenges in industrial assembly, mental health, and workplace transformation. Publications & Impact: His 2016 study on gaze tracking in remote collaboration (cited over 100 times) and 2024 book on AR collaboration highlight his contributions. Recent projects include Vibe360 (group emotion analysis) and CAEVr (biofeedback-driven empathy systems). He actively contributes to conferences like IEEE VR and CHI, shaping the future of immersive technologies. Labs & Future Work: His lab develops tools like the SAR system for aircraft drilling and empathic agents (EMiRAs). Future directions include AI-driven emotional interfaces, inclusive metaverse design, and VR solutions for cognitive rehabilitation.
Yang Song is an ARC Future Fellow and Scientia Associate Professor at the School of Computer Science and Engineering , University of New South Wales (UNSW) . She serves as Associate Head of School (Research) and Co-Director of iCinema , focusing on AI and Computer Vision applications for social good. Education: BEng in Computer Engineering (Nanyang Technological University, Singapore), PhD in Computer Science (UNSW, 2013) Research Areas: Biomedical image analysis, human-centred AI, graph data modeling, neuro-symbolic learning, and AI trustworthiness. Her work develops domain-specific deep learning models for radiological segmentation, histopathology cancer analysis, and 3D reconstruction. Recent projects address explainability in LLMs, fairness in AI, and human-robot interaction frameworks. With over 200 peer-reviewed publications in top venues like CVPR , MICCAI , and NeurIPS , her research spans biomedical imaging, robotics, and general multimodal AI. Scientific Awards include: 2024: ARC Industrial Transformation Research Hub for Human-Robot Teaming 2023: Google Inclusion Research Award 2022: NHMRC Ideas Grant for computational brain imaging 2021: UNSW Engineering Research Excellence Award 2020: Scientia Fellowship (UNSW) 2019: ARC Future Fellowship She supervises 24 current PhD/MPhil students and has graduated 15 advisees, including placements at Harvard University and Siemens Healthineers. Her grants include collaborations with Surf Life Saving Australia and industry partnerships for AI-driven solutions.
Professor Gavan McNally is a distinguished behavioral neuroscientist at the University of New South Wales, where he serves as a Professor in the School of Psychology. He is actively engaged in research on the fundamental behavioral and brain mechanisms for learning and motivation, with applications to clinical conditions such as addictions, anxiety disorders, and mood disorders. McNally holds several prestigious editorial positions, including Editor-in-Chief of Neurobiology of Learning & Memory and Senior Editor of The Journal of Neuroscience. He also serves as President-Elect of the European Behavioral Pharmacology Society and is a Member of the Australian Research Council College of Experts. McNally's research interests span behavioral neuroscience, focusing on how fundamental brain mechanisms apply to clinical conditions. He employs a systems neuroscience approach, combining well-controlled behavioral approaches with optogenetics, chemogenetics, in vivo calcium imaging, and whole brain circuit mapping in both normal and transgenic animals. His work bridges basic science with clinical applications through collaborations with colleagues at University of Sydney, Sydney Local Health District, Monash University, and Turning Point. McNally's research particularly examines the cellular, circuit, and systems level mechanisms underlying learning, motivation, and their dysregulation in disorders like addiction. His laboratory investigates how these mechanisms translate to human conditions, with a strong emphasis on developing new treatments for psychological disorders. His extensive publication record demonstrates a clear trajectory in understanding punishment learning, addiction mechanisms, and the neural circuits underlying motivated behavior. Recent work has increasingly focused on the cognitive pathways to punishment insensitivity, the role of specific neural circuits in addiction, and translational approaches to understanding maladaptive behaviors. McNally's research bridges animal models with human studies, creating a comprehensive understanding of the neural mechanisms that govern learning and motivation, with particular attention to how these processes go awry in addiction and other psychological disorders. 2008 QEII Fellow, Australian Research Council 2009 Association for Psychological Science, International Rising Star 2010 Fellow, Association for Psychological Science 2010 UNSW Faculty of Science Staff Excellence Award for Research and Training 2011 Pavlovian Research Award, The Pavlovian Society 2012 Future Fellow (Level 3), Australian Research Council 2016 D.G. Marquis Behavioral Neuroscience Award, American Psychological Association 2017 Fellow, American Psychological Association 2019 Fellow of the Academy of Social Sciences in Australia 2021 D.G. Marquis Behavioral Neuroscience Award, American Psychological Association 2022 Ross Day Plenary Lecturer, Australasian Brain and Psychological Sciences 2023 European Behavioural Pharmacology Society Plenary Lecturer 2024 Elspeth McLachlan Plenary Lecturer, Australasian Neuroscience Society 2024 D.G. Marquis Behavioral Neuroscience Award, American Psychological Association Professor McNally actively supervises several students including Bixuan Lin, Si Yin Lui, Hannah Machet, Bart Cooley, Kelly Zhuang, and Alexandra Gregory. His current research is supported by significant funding including an Australian Research Council Discovery Project (2024-2026) on "Risky choices: From cells and circuits to computations and behaviour," another Discovery Project (2025-2028) on "Multimodal mapping of punishment learning," and NHMRC grants including a Synergy Grant on "Linking clinical and basic science discovery to find new treatments for alcohol-use disorder" and an Ideas Grant on "Novel pathways to abstinence from alcohol seeking." These projects reflect his commitment to both fundamental neuroscience and translational applications for treating psychological conditions. His teaching responsibilities include PSYC2081 Learning & Physiological Psychology and PSYC3051 Physiological Psychology. McNally's laboratory employs advanced techniques including optogenetics, chemogenetics, in vivo calcium imaging, and whole brain circuit mapping to investigate the neural mechanisms underlying learning, motivation, and their dysregulation in disorders. His team works at the intersection of basic neuroscience and clinical applications, with strong collaborations across multiple institutions to translate fundamental findings into potential treatments for addiction and other psychological disorders. The lab has made significant contributions to understanding the role of brain regions like the ventral pallidum, paraventricular thalamus, and nucleus accumbens in addiction, fear learning, and punishment sensitivity.
