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.
Professor Hala Zreiqat AM is a leading biomedical engineer at The University of Sydney , serving as the Director of the ARC Training Centre for Innovative BioEngineering . A Fellow of all major Australian academies (AAS, ATSE, FAHMS, FRSN), she develops 3D printed bioceramics for bone regeneration while championing diversity through initiatives like the IDEAL Society and BIOTech Futures mentorship program. Her work bridges academia, clinical practice, and industry in musculoskeletal research . Research Focus: Her lab creates synthetic bone scaffolds that mimic natural bone architecture, strength, and porosity, enabling non-rejected bone regeneration via patient-matched implants. Key applications include orthopaedic, dental, and maxillofacial repair , with over $18M in competitive funding and multiple patents. Current projects explore AI-driven scaffold performance prediction and anti-senescence strategies for aging-related bone loss. Scientific Trends: Recent publications highlight 3D printed nanovoxelated ceramics , antisenescence biomaterials , and multifunctional theranostic platforms . Her team integrates machine learning for scaffold design, atom probe tomography for interface analysis, and two-photon imaging for cellular monitoring in 3D environments. 2021-2022 Fulbright Senior Scholar 2018 NSW Premier's Woman of the Year 2019 Eureka Prize for Innovative Use of Technology Fellow of Australian Academy of Science (2021) Over $18M in research funding Teaching & Leadership: She designed core courses like Tissue Engineering and Nanomaterials in Medicine , mentoring 158 students in 2020 alone. As Chair of CAAR (2020-2023), she strengthens Australia-Arab collaborations. Her lab trains early-career researchers , with alumni now in academia and industry.
Professor Jun Zhang is a leading academic in cybersecurity at Swinburne University, Australia, where he directs the Cybersecurity Lab. He has been honored as Australia's top cybersecurity researcher and instrumental in establishing Swinburne as a globally recognized cybersecurity research institution. His work includes high-impact papers and multi-million-dollar R&D projects, culminating in awards like the 2021 'Top Cybersecurity Research Institution' accolade. As course director of the Bachelor of Cyber Security, he pioneered an industry-driven teaching model with Deloitte and CSIRO, significantly boosting course enrollment. His collaborations extend to Adobe's Curriculum Innovation Program and the Australian P-TECH initiative, promoting STEM education and cybersecurity awareness. He supervises doctoral candidates and leads grants focused on AI-driven cybersecurity, smart home security, and blockchain-based edge computing. His research spans vulnerability detection, GAN forensics, IoT security, and privacy preservation in OSNs. Research interests include cybersecurity fundamentals, data science applications, and distributed systems. Notable achievements include the PTFix framework for Java vulnerabilities, the IoTFuzz smart home testing system, and CTI mining methodologies. Awards reflect his mid-career research excellence and industry partnerships. His grants with CSIRO and defense organizations emphasize real-world impact, addressing challenges from malware detection to adversarial machine learning. The Cybersecurity Lab and collaborative projects like Artchain demonstrate his commitment to bridging academia and industry. Professional activities include supervising over 20 HDR students and securing grants totaling millions. His work on blockchain-based edge storage (CSEdge) and SDCCP congestion control highlights innovation in networking. Future directions include advancing AI for design collaboration with CSIRO and enhancing privacy in smart energy technologies. His contributions span technical, educational, and community outreach domains, positioning him as a pivotal figure in cybersecurity's evolution.
Prof. Xiaojing Huang is a Professor of Information and Communications Technology at the University of Technology Sydney (UTS), serving as Head of Discipline for SEDE Communications and Electronics within the School of Electrical and Data Engineering. He leads the Mobile Sensing and Communications program at the Global Big Data Technologies Centre. With over 30 years of experience, he has authored over 300 publications and 31 patents, focusing on wireless communications, signal processing, and antenna technologies. Education: PhD (Electrical Engineering, Shanghai Jiao Tong University, 1989). Previous roles include Principal Research Scientist at CSIRO (2009-2014), Associate Professor at University of Wollongong (2004-2009), and key industry roles at Motorola and Shanghai Yang Tian Science and Technology Corporation. Research interests include full-duplex wireless systems, millimeter-wave and terahertz communications, massive antenna arrays, and mixed-signal processing platforms. His work on the CSIRO Ngara backhaul system earned multiple awards, including the 2012 CSIRO Chairman's Medal and Australian Engineering Innovation Award. Recent grants include $4.2M (AUD) for projects like 'Radio Frequency Camera for Radar Imaging' (ARC DP220101158) and 'Terabit mm-Wave Backbones for Integrated Space Networks' (ARC DP200101532). He has supervised numerous students in high-speed communication systems and full-duplex technologies. Awards include: 2013 CSIRO Leadership Achievement Award, 2012 Australian Engineering Innovation Award, and IEEE Sumner Award (nominee). Active in IEEE standards (802.11/802.15) and collaborations with institutions like Tsinghua University.
Professor Ryan Ko is a leading academic in Cyber Security at the University of Queensland (UQ), serving as Chair and Director of the UQ Cyber Research Centre, Director of Research at the School of Electrical Engineering and Computer Science, and a member of UQ's Academic Board. He holds a BEng (Computer Engineering) and PhD from Nanyang Technological University, Singapore. His research focuses on data provenance, industrial control systems (ICS) security, privacy-preserving technologies, and AI-driven cybersecurity solutions. Ko has pioneered initiatives such as the UQ Cyber Centre, interdisciplinary cybersecurity education programs (MCyber, PGDipCyber), and the Oceania Cybersecurity Challenge. He has attracted over AUD 20 million in competitive grants as lead investigator, including the NZ$12.2M STRATUS project. Ko’s work spans industry and policy, including advisory roles with INTERPOL, governments, and NGOs. He has authored over 100 publications, contributed to ISO standards (e.g., ISO/IEC 21878), and co-founded cybersecurity startups. Awards include Fellowships from the Australian Computer Society and Queensland Academy of Arts and Sciences, and the CSA Ron Knode Service Award. Education: Bachelor of Engineering (Computer Engineering) (Hons.), Nanyang Technological University (2005) PhD in Computer Science, Nanyang Technological University (2011) Awards: Fellow, Australian Computer Society (2016) Fellow, Queensland Academy of Arts and Sciences (2020) Young Professional Award, Singapore Government (2018) Grants & Leadership: AUD 20M+ lead investigator grants; NZ$12.2M STRATUS project (2014–2018) Established UQ Cyber Centre (2019) and NZ’s first cybersecurity graduate program (2012) His research emphasizes returning data control to users, with applications in agriculture, energy, and critical infrastructure. Ko’s contributions include patents, open-source tools (e.g., Kali Linux), and frameworks like TrustCloud for cloud accountability.
Professor Gary Edmond is a law professor at the University of New South Wales School of Law, directing the Program in Expertise, Evidence and Law. Holding a BA(Hons) from the University of Wollongong, LLB(Hons) from the University of Sydney, and PhD from the University of Cambridge, he bridges legal scholarship with forensic science expertise through extensive research grants and international collaborations. Education: BA(Hons), LLB(Hons), PhD Institutions: University of New South Wales, Australian Academy of Forensic Sciences His research focuses on the intersection of law and forensic science, examining expert evidence reliability, forensic reporting practices, and the adversarial legal system's limitations. With over $1.4 million in research funding since 2007, he leads interdisciplinary projects involving policing agencies and forensic institutions across Australia and international partners. Recent publications analyze judicial handling of expert evidence, cognitive biases in courtroom identification, and forensic science reform. His work has shaped evidence law understanding through the 6th edition of 'Australian Evidence: A principled approach to the common law and the uniform acts' and advisory roles in high-profile inquiries like the Goudge Inquiry. Awards: Fellow of the Royal Society of New South Wales As Chair of the Evidence-based forensics initiative and member of Standards Australia’s forensic science committee, he continues to influence policy while teaching core legal subjects including Courts, Procedure, Evidence and Proof, and Introducing Law and Justice.
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.
Associate Professor Fengling Han is affiliated with RMIT University's School of Computing Technologies in Melbourne, Australia. He holds the rank of Associate Professor since January 2022. His research focuses on complex networks, industrial electronics, AI/machine learning, and network security. Notable contributions include steganography frameworks for healthcare data, sliding mode control for energy systems, and blockchain applications in surveillance and voting systems. His work spans interdisciplinary areas such as renewable energy integration, battery management systems, and privacy-preserving recommendation systems. He has supervised numerous projects, including AI-driven chatbots, medical imaging watermarking, and peer-to-peer energy trading systems. Han's service roles include conference reviewing and committee memberships in international conferences like IEEE and ISMST. Research Interests: His expertise spans electrical engineering, control systems, and AI applications. Key areas include battery management, cybersecurity, and smart manufacturing. Recent projects emphasize Industry 5.0 technologies, blockchain for data integrity, and deep learning for steganalysis. Teaching and Supervision: Teaches network security, data communication, and IT infrastructure. Current supervision includes AI-powered business modeling, medical imaging tampering detection, and renewable energy sharing systems. Over 14 research projects are documented, reflecting his interdisciplinary impact. Awards and Recognition: While specific awards are not listed, his extensive publications (over 150 outputs) and high citation counts (e.g., 119 citations for the Industry 5.0 survey) highlight his scholarly contributions.
Dr. Jiang Qian is a Lecturer at the University of Sydney. He holds a PhD in Marketing from the University of Houston, a Master’s in Finance from Johns Hopkins University, and an undergraduate double major in Information Systems and Finance from the Southwestern University of Finance and Economics. His research focuses on leveraging quantitative models and machine learning techniques to extract insights from large-scale data in marketing and healthcare contexts, particularly in social media, online search, and healthcare markets. Current research supervision includes Jennifer Ye’s project on Audio Data Analytics: A New Dimension in Customer Service Excellence . Dr. Qian’s recent work spans AI applications in breast cancer detection, medical imaging analysis, and reinforcement learning for autonomous systems. His studies address challenges like AI model calibration, training data quality, and radiologist-AI collaboration in clinical settings. Notable contributions include analyzing video cover image impacts on advertisement engagement and exploring multiresolution techniques for medical imaging segmentation. His interdisciplinary approach bridges marketing analytics and healthcare technology, emphasizing practical clinical translation of AI systems.
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.
SangHyung Ahn is a Lecturer at the School of Civil Engineering , University of Queensland (UQ), since 2017. He joined UQ as a postdoctoral research fellow in 2015 after earning his PhD in Civil Engineering (Construction Engineering and Management) from Purdue University, USA. Prior to his academic career, he worked as an assistant manager at Hyundai Engineering and Construction Co., Ltd. (2003-2007) and holds an MBA in international business from Hanyang University and a B.Sc in Civil Engineering from Korea University. Research Focus: Construction process modelling with virtual reality, decision support systems for construction, automation of data-driven simulation modelling, sensor-based operations analysis, and integration of Building Information Modelling (BIM). Teaching: Coordinates undergraduate courses Introduction to Project Management (CIVL3510) and Construction Engineering Management (CIVL4522) . Research Trends: His recent publications highlight interdisciplinary work in transportation engineering, structural design, and AI-driven simulation tools. Key themes include application of machine learning to car-following models, drone-based vehicle identification, and optimization of public transport systems using agent-based simulations. Supervision: Available for supervision, with completed supervision of PhD and Master’s theses on topics such as BIM-LCA integration, pedestrian trajectory analysis, and AI-driven driving behavior models.
Dr. Mohamed Khalifa is a Visiting Fellow at the Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney. He holds a PhD in Health Innovation from Macquarie University (2020) and an MSc in Health Informatics from the University of Edinburgh (2012). His expertise spans health informatics, AI-driven healthcare solutions, and strategic healthcare management. He has led multidisciplinary teams in developing evidence-based frameworks like GRASP for clinical predictive tools. Affiliations: Visiting Fellow, Macquarie University Director of Studies, College of Health Sciences (Education Centre of Australia) Former Digital Health Officer, Australian Digital Health Agency (2020–2021) His research focuses on AI applications in healthcare, clinical decision support systems, and health analytics. Over 20 years, he has published 60+ peer-reviewed papers and holds an innovation patent (2018). He has received awards including the IMIA Best Paper (2020) and ICIMTH Best Paper (2015). Dr. Khalifa’s work emphasizes improving healthcare efficiency through technology, including projects on predictive tools, emergency room performance, and diabetes management. He is a Fellow of the Australasian Institute of Digital Health and certified in healthcare information systems (CPHIMS).
Professor Karin Verspoor is the Dean of the School of Computing Technologies at RMIT University in Melbourne, Australia. She previously held roles as Director of Health Technologies and Deputy Head of the School of Computing and Information Systems at the University of Melbourne, and as Scientific Director of Health and Life Sciences at NICTA's Victoria Research Laboratory. Her research focuses on applying artificial intelligence methods to biomedical discovery and clinical decision support, particularly through natural language processing of clinical texts and biomedical literature. Affiliations: RMIT University (STEM College), Australian Alliance for Artificial Intelligence in Health (Victorian Node Lead) Industry Experience: Intelligenesis/Webmind Corp., Applied Semantics, Los Alamos National Laboratory, National ICT Australia Research Interests: Artificial Intelligence in Medicine Biomedical Natural Language Processing Health Informatics Computational Biology Cheminformatics Her work emphasizes cross-modal data integration, EHR analytics, and AI-driven clinical tools to address challenges in healthcare outcomes, musculoskeletal disorders, and infectious disease surveillance. Advising & Grants: Supervises research on AI-based decision-making frameworks, EHR data quality, and chemical knowledge extraction. Leads projects funded by initiatives like CANAIRI (Collaboration for Translational AI in Healthcare). Labs & Collaborations: Co-founder of the Australian Alliance for AI in Health, advancing national AI healthcare policy and translational research.
Jeeyeon Kim is a Lecturer in the Department of Management and Marketing at La Trobe Business School, La Trobe University. Prior to this, she served as an Assistant Professor at National Sun Yat-sen University in Taiwan. Her research focuses on digital marketing, omnichannel retailing, social media/influencer marketing, and digital healthcare. She holds a PhD and MA from Yonsei University, South Korea, and has held visiting roles at Yonsei University and IE University. Her work emphasizes empirical data analysis and statistical modeling to address marketing challenges. Jeeyeon has secured multiple research grants, including AUD 82,500 from Taiwan's Ministry of Science and Technology (MOST) for projects on big data-driven marketing and digital transformation. She also coordinates grants with Yonsei University and AACSB initiatives. Her editorial roles include serving on the Asia Marketing Journal and Korean Scholars of Marketing Science boards. Her research spans topics such as virtual influencers' impact on social media, omnichannel strategies, and healthcare consumer behavior. Teaching awards include the 'Outstanding Course Teaching Award' and 'Excellent Mentor Award.' She actively reviews for journals like the Asian Pacific Journal of Marketing and Logistics and conferences like the Global Fashion Management Conference.
