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.
Halil Ali is a Lecturer in Data Science (Education Focused) at the School of Computing Technologies, RMIT University. His research spans privacy-preserving machine learning, blockchain technologies, and cybersecurity. Key research areas include federated learning , quantum-enhanced AI , secure biometrics , edge unlearning , and privacy in healthcare data . His recent publications focus on resilient AI systems , blockchain applications , and ethical data handling in emerging technologies. His work demonstrates expertise in integrating machine learning with blockchain security across domains like IoT, smart grids, and metaverse healthcare. He contributes to practical frameworks for zero-trust architectures , lightweight consensus protocols , and quantum-classical hybrid models .
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.
Professor Irena Koprinska is a faculty member at the School of Computer Science, University of Sydney, specializing in Machine Learning, Data Mining, and Neural Networks. Her research focuses on practical applications in education, health, and energy sectors. She has received multiple awards, including the Dean’s Award for Outstanding Teaching (2017, 2008) and Best Paper Awards at CHI 2019 and other conferences. Koprinska has supervised 11 PhD and over 60 Honours students, many of whom have won prestigious scholarships like the Google Fellowship. Education: PhD and MSc in Computer Science, MEd in Higher Education. Research Interests: Develops algorithms for pattern extraction and predictive modeling in healthcare (e.g., sleep apnea prediction), education (student behavior analysis), and energy (solar power forecasting). Her work bridges algorithmic innovation with multidisciplinary collaboration. Publications: Over 100 articles in top journals/conferences, emphasizing applications of machine learning in health, energy, and education. Recent works include deep learning for sleep apnea and ensemble methods for solar forecasting. Awards: Highlighted awards include the Dean’s Teaching Awards, Best Paper recognitions, and the Thompson Research Fellowship (2018). Advising & Grants: Currently supervises Hanxue Yao. Previously led initiatives like the Data Science for Social Good workshop at ECML PKDD. Served as Associate Head for Research Education and Sub-Dean for Teaching & Learning. Labs/Teams: Leads the Computer Human Adapted Interaction Research Group, focusing on human-centric technology solutions.
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.
Mohd Fikree Hassan is a Lecturer at the School of Information Technology, Monash University Malaysia, joining in June 2023. He holds a Ph.D. and Master's from the University of Malaya, and a B.Eng. in Electronics Engineering from Multimedia University. With over 14 years of academic experience, he is actively engaged in research, teaching, and supervision. B.Eng. in Electronics Engineering (Telecommunications), Multimedia University, 2004 M.Eng. in Engineering (Telecommunications), University of Malaya, 2015 Ph.D. in Signal and Systems, University of Malaya, 2018 His research focuses on image and signal processing , particularly in image enhancement, restoration, computer vision, and human color vision . His work contributes to improving image visibility, removing color casts, and developing algorithms for noisy or degraded images. He applies mathematical and computational techniques to solve real-world imaging challenges. The recent publication trends (2021–2025) show a strong focus on image restoration using variational methods (e.g., total variation, ℓ0 regularization), color enhancement in HSI space, and video analysis for sports applications. His work bridges theoretical optimization and practical computer vision systems. He actively contributes to the academic community through peer review for journals such as Neurocomputing , Journal of Imaging , and International Journal of Computational Intelligence Systems , as well as for IEEE conferences. Mohd Fikree is currently accepting PhD students and serves as an external examiner for academic programs. His consistent research output and editorial service reflect a growing impact in the field of image processing and computer vision. While no formal lab or team is mentioned in the text, his collaborations with researchers like R. Paramesran, T. Adam, and G. Krishnasamy suggest active research partnerships in signal and image processing.
Dr. Bo Liu is an Associate Professor in the School of Computer Science at the University of Technology Sydney (UTS), where he serves as a core member and director of the AI Security and Privacy (AISP) Research Lab at the Australian Artificial Intelligence Institute (AAII). With expertise spanning cybersecurity, privacy protection, AI and machine learning, and wireless communications, Dr. Liu has established himself as a leading researcher in the field of AI security and privacy. Dr. Liu earned his PhD from the Department of Electronic Engineering at Shanghai Jiao Tong University in 2010. His academic journey at UTS has progressed from Senior Lecturer (November 2019-December 2022) to his current position as Associate Professor (January 2023-present). Dr. Liu's research focuses on the critical intersection of artificial intelligence and security, particularly addressing emerging threats in the age of advanced AI systems. His work spans multiple dimensions of security and privacy, including deepfake detection, privacy-preserving data synthesis, AI model security, and fair machine learning. He has pioneered approaches to detect AI-generated content, protect visual privacy through de-identification techniques, and address the complex relationship between algorithmic fairness and privacy preservation. His publication record demonstrates significant contributions across multiple cutting-edge research areas, with particular emphasis on detecting and mitigating threats from generative AI systems. His recent work reveals a strong focus on deepfake detection across multiple modalities (images, video, and audio), privacy-preserving techniques for sensitive data, and the security implications of emerging AI architectures like Retrieval-Augmented Generation systems. Dr. Liu has secured substantial research funding, including as Lead Chief Investigator on multiple ARC Discovery and Linkage Projects, totaling over $3.5 million AUD. His industry collaborations include partnerships with the NSW Department of Planning and the Reserve Bank of Australia, demonstrating the practical applicability of his research. As an academic leader, Dr. Liu serves as Associate Editor for IEEE Transactions on Broadcasting and actively contributes to the academic community through conference organization, peer review for top-tier venues, and assessment for ARC grant schemes. He also teaches courses including Penetration Testing, Ethical Hacking and Offensive Security, and supervises Masters and PhD students in cybersecurity and privacy research.
Professor David Taubman is a distinguished academic serving as Professor and Deputy Head of School (Research) at the School of Electrical Engineering and Telecommunications (EE&T) at the University of New South Wales (UNSW) in Sydney, Australia. He is also co-director of Kakadu Software Pty. Ltd. and its affiliates Kakadu R&D and Kakadu GPU. With a career spanning over three decades, Professor Taubman has made significant contributions to the field of image and video compression, most notably as the author of the EBCOT coding algorithm adopted in the JPEG2000 international standard. Professor Taubman earned his B.Sc. in Mathematics and Computer Science (1986) and B.E. (Medal) in Electrical Engineering (1988) from the University of Sydney, followed by an M.Sc. (1992) and Ph.D. (1994) in Electrical Engineering from the University of California at Berkeley. His professional journey includes engineering work at the Electricity Commission of N.S.W. (1988-1990), research positions at Hewlett-Packard Laboratories in Palo Alto (1994-1998), and an academic career at UNSW where he progressed from Senior Lecturer (1998-2003) to Associate Professor (2004-2009) and finally to Professor (2009-present). He has held various leadership roles including Head of the EE&T Telecommunications Research Group (2003-2014), Head of the EE&T Signal Processing Research Group (2014-present), Director of Research for the School of EE&T (2011-2016), and Deputy Head of School (Research) since 2017. Professor Taubman's research interests center on image and video compression, with particular expertise in JPEG2000 standards and implementations. His work spans signal processing, wavelet transforms, scalable video coding, motion modeling, and multimedia systems. He has pioneered numerous compression algorithms and frameworks, including the EBCOT coding algorithm that became central to the JPEG2000 standard. His recent research focuses on efficient motion modeling with cuboidal partitioning, learned lifting-based transform structures, and high-throughput implementations of JPEG2000 for video applications. His work bridges theoretical foundations with practical implementations, as evidenced by the commercially successful Kakadu Software tools that have garnered around 500 commercial licensees. Analysis of Professor Taubman's recent publications reveals a consistent focus on advancing compression technologies with particular emphasis on scalability, efficiency, and adaptability. His work spans traditional image compression (JPEG2000 extensions), video coding (cuboid-based partitioning for UHD/360-degree video), and emerging applications (nanopore sequencing data compression). A notable trend is the integration of machine learning techniques with traditional compression frameworks, as seen in his work on learned lifting-based transform structures. His research maintains strong connections to real-world applications across diverse domains including medical imaging, astronomical data processing, and genomic sequencing. IEEE Fellow Engineers Australia Fellow (by invitation) Professor Taubman has served as Associate Editor for the IEEE Transactions on Image Processing for two four-year appointments (2003-2005 and 2010-2013). He has been actively involved in numerous research grants focused on image and video compression technologies, particularly those related to the JPEG2000 standard and its extensions. His work has received significant industry support, reflected in his consultancy with various U.S., Japanese, and Australian corporations. He has also contributed to international standards development as a member of Standards Australia Technical Committee MS-065 (mirroring ISO TC42 on Digital Photography) and as a constitutional member of Standards Australia Technical Committee IT-029 (Coded Representation of Picture, Audio and Multimedia/Hypermedia Information). Professor Taubman co-directs Kakadu Software Pty. Ltd. and its research affiliates Kakadu R&D and Kakadu GPU, which have developed the commercially successful Kakadu Software tools for JPEG2000. His research group at UNSW focuses on advanced image and video compression techniques, with particular expertise in wavelet-based methods, scalable coding, and motion modeling. The group maintains strong industry connections and has contributed significantly to the development and standardization of image compression technologies worldwide.
Prof. Jian Zhang is a Professor in the School of Electrical and Data Engineering at the University of Technology Sydney (UTS), specializing in computer vision, pattern recognition, and multimedia signal processing. He leads the Multimedia Data Analytics Lab at the Global Big Data Technologies Centre, focusing on agri-food sector applications such as livestock monitoring and AI-driven solutions for agricultural efficiency. Education : PhD, School of Information Technology and Electrical Engineering, University of New South Wales, 1999 MSc, The Flinders University of South Australia, 1994 BSc, East China Normal University, 1982 Research Interests : His work spans 2D/3D computer vision, large-scale image/video analytics, and cross-disciplinary projects in agriculture and remote sensing. He has pioneered AI systems for livestock counting, poultry welfare monitoring, and fish quality assessment, funded by organizations like Meat & Livestock Australia and Australian Eggs. Grants & Projects : Current projects include AI-based hen health monitoring ($5M+ funding since 2011) Collaborations with industry partners like Sydney Fish Market and Fremantle Port Students & Academic Leadership : Supervised 19 PhD graduates and 5 research fellows Recruiting new PhD candidates in computer vision and data analytics Labs & Teams : Director of the Multimedia Data Analytics Lab, collaborating with global experts through UTS's Distinguished Visiting Scholars program.
Dr. Hung Yew Mun is an Associate Professor and Interim Head of the Mechanical Engineering program at Monash University Malaysia’s Malaysia School of Engineering. His expertise spans heat transfer, thermodynamics, and fluid dynamics, with a focus on micro-scale phenomena, phase-change heat transfer, and graphene-based materials. He teaches courses including MEC3454/MEC4408 (Thermodynamics and Heat Transfer) and MEC4416 (Momentum, Energy & Mass Transport). Dr. Hung’s research addresses advanced cooling solutions for electronics, energy storage, and sustainable materials. Education: PhD in Mechanical Engineering from University of Multimedia, Malaysia (2010). Research Interests: Micro-scale heat transfer, phase-change mechanisms, graphene nanostructures, and applications in thermal management. His work contributes to UN SDG goals related to affordable and clean energy (SDG7) and industry innovation (SDG9). Recent Projects: Includes studies on MXene-biochar composites for energy storage, graphene-enhanced devices for electronics cooling, and multiscale modeling of postharvest fruit water transport. He has led/co-investigated 12 projects since 2013, emphasizing interdisciplinary collaboration. Publications: Over 100 peer-reviewed articles, with recent focus on graphene-mediated heat transfer enhancement, plasma-activated cooling, and nanofluid applications. His work addresses both fundamental mechanisms and industrial applications. Labs/Teams: Active in thermal engineering and nanomaterials research groups, collaborating with institutions globally on sustainable energy and advanced materials.
Dr. Siqi Ma is a Senior Lecturer at the UNSW Institute for Cyber Security (IFCYBER) within the School of Systems & Computing at the University of New South Wales (UNSW). He previously served as a Lecturer at the University of Queensland's School of Information Technology and Electrical Engineering (ITEE). He holds a Ph.D. in Information Systems from Singapore Management University (2018) and was a Postdoctoral Research Fellow at Data61, CSIRO. He also visited Carnegie Mellon University (CMU) in 2015. Current Role: Senior Lecturer, UNSW Institute for Cyber Security Former Role: Lecturer, University of Queensland Education: Ph.D. (Singapore Management University), Postdoc (Data61, CSIRO) His research spans automated vulnerability detection, mobile security, IoT security, network authentication, and graph-based adversarial robustness. Recent work focuses on drone configuration bugs, Android malware analysis via GNNs, federated learning privacy, and credential leakage in open-source projects. Key trends in his 2024-2025 publications include automated security analysis for embedded systems, deepfake detection in multimedia, and privacy-preserving mechanisms for distributed networks. He collaborates with institutions like Purdue University, Singapore Management University, and CSIRO Data61.
Dr. Zhi Chen is a Lecturer in Computing at the School of Mathematics, Physics and Computing, University of Southern Queensland, specializing in Artificial Intelligence and Machine Learning with applications spanning digital agriculture and healthcare systems. Education: Master of Information Technology (MIT), University of Queensland, 2018 PhD, University of Queensland, 2023 Research Focus: His work centers on zero-shot learning, domain adaptation, and multimodal systems, addressing core challenges in computer vision and deep learning. Current projects integrate AI with agricultural risk modeling and medical diagnostics, emphasizing real-world deployment of robust algorithms under data-scarce conditions. Publication Trends: Recent output (2022-2025) shows concentrated expertise in source-free domain adaptation and generalized zero-shot learning, with significant contributions to plant disease recognition (via mobile multimodal systems) and diabetes subgroup analysis. His work consistently appears in premier venues including AAAI, CVPR, and ACM MM, demonstrating methodological innovation applied to critical domains like climate-resilient agriculture and precision medicine. Supervision: Currently serves as Associate Supervisor for a doctoral candidate developing parametric insurance models for oyster farms to mitigate climate-related risks from king tides and extreme weather events. Awards: No scientific awards were documented in the provided materials.
Dr. Mike Seymour is a Senior Lecturer at the University of Sydney Business School, specializing in Human-Computer Interaction (HCI), Digital Humans, and AI ethics. He holds a BSc, MBA, and PhD from the University of Sydney. His research focuses on photorealistic digital faces for immersive interfaces, blockchain socio-technical systems, and agile project management in creative industries. Dr. Seymour is a member of the Sydney Nano Institute and leads the Motus Lab. He has published in top journals like *Harvard Business Review*, *Communications of the ACM*, and *Information Systems Research*. His current projects include ARC-funded research on digital humans for anti-racism initiatives and adaptive AI for brain injury patients. He has received awards such as the SOAR Prize and ECR Researcher of the Year. His teaching spans CX, UX, and project management courses (e.g., INFS2040, INFS3080). Media engagements include ABC News, Sky News, and *The Australian Financial Review* for commentary on AI ethics and film industry trends. Education: BSc (University of Sydney) MBA (University of Sydney) PhD (University of Sydney) Research Themes: Real-time photorealistic avatars Deepfake ethics Agile methodologies in VFX Grants: A$450K ARC DP25 grant for anti-racism digital humans Earned $200K in industry partnerships (e.g., Epic Games) Labs/Teams: Leads the Motus Lab and collaborates with the Digital Human Research Group.
Professor Xue Li is a faculty member in the School of Electrical Engineering and Computer Science at the University of Queensland. His research focuses on machine learning, data mining, and their applications in healthcare, materials science, and computer vision. He has authored over 300 publications, including seminal works on knowledge graph completion, video quality enhancement, and alloy design using machine learning. His work bridges theoretical advancements with real-world applications, such as clinical diagnosis andTinyML systems. Key research interests include graph representation learning, medical informatics, and efficient algorithms for multimedia data. Notable contributions include developing commonsense-enhanced relation extraction models and frameworks for compressed video reconstruction. His research also addresses challenges in federated learning and privacy-preserving genomics. Prof. Li has collaborated extensively with industry and academia, contributing to projects in RFID systems, electronic nose pattern recognition, and cybersecurity. His work is published in top-tier venues like IEEE Transactions and ACM conferences. Despite no listed awards, his prolific output underscores academic impact.
Abhinav Dhall is an Associate Professor in the Department of Data Science & AI at Monash University. His research focuses on computer vision, affective computing, and human-centered AI, with a particular emphasis on deepfake detection, multimodal analysis, and ethical AI applications. He is actively involved in organizing workshops like the Multimodal and Responsible Affective Computing (MRAC) and chairs conferences such as ACCV. Dhall accepts PhD students and has contributed significantly to datasets like AV-Deepfake1M and EmotiW challenges. His work spans topics including HDR imaging, facial expression recognition, and AI ethics in multimedia systems.
