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.
Professor Catherine Greenhill is a faculty member at the School of Mathematics and Statistics, UNSW Sydney , where she serves as Professor and head of the Combinatorics group. Her academic career spans institutions including the University of Queensland, University of Oxford, University of Leeds, University of Melbourne, and Australian National University. D.Phil., University of Oxford (1996) M.Sc. (Research) in Combinatorics (1992) B.Sc. (Hons) in Pure Mathematics (1991) Her research focuses on the intersection of discrete mathematics , theoretical computer science , and probability , particularly in asymptotic combinatorics , probabilistic methods , and analysis of algorithms . Her work includes asymptotic enumeration of combinatorial structures and design of randomized algorithms for graph sampling and counting. Her recent publications (2025–2021) center on random graphs and hypergraphs , with key contributions to switch Markov chains , degree sequence analysis , and chromatic number bounds . These works reflect her expertise in probabilistic combinatorics and algorithmic complexity . Scientific Awards: Fellow of the Australian Academy of Science (2022) Christopher Heyde Medal in Pure Mathematics (2015) June Griffith Fellowship (2013) Hall Medal (2010) Advising and Grants: She has supervised numerous PhD/Masters students and secured multiple ARC Discovery Grants (2019–2021, 2014–2016, 2012–2014). Her grants address topics like hypergraph modeling, random discrete structures, and network analysis in illicit drug trafficking.
Shoudong Huang is a Professor at the School of Mechanical and Mechatronic Engineering , University of Technology Sydney, and Deputy Director of the UTS Robotics Institute. His research focuses on mobile robot navigation , SLAM , nonlinear state estimation , and surgical robotics . He has published over 200 papers and is recognized as one of the 100 Most Influential Scholars in Robotics (Aminer, 2018). PhD in Automatic Control, Northeastern University (China) Postdoctoral Research Fellow, University of Hong Kong (1998-2000) Research Fellow, Australian National University (2001-2003) Full-time academic roles at UTS since 2004 His work addresses challenges in robot localization across extreme environments (underwater, underground mining, surgical settings) and develops globally optimal SLAM algorithms with guaranteed performance. He has secured over $4 million AUD in external funding, including ARC Discovery grants and industry partnerships. Recent publications emphasize cross-modal calibration (camera-LiDAR), interval analysis for bounded noise , and template-based deformable surface reconstruction . These span applications in autonomous driving, surgical navigation, and UAV guidance. Chancellor’s Medal for Research Excellence (2020) Supervisor of the Year (2023) Best Paper Award (2016 ICARCV) Huang serves as Associate Editor for IEEE Transactions on Robotics and International Journal of Robotics Research , and has held leadership roles in top robotics conferences like IROS and RSS. His collaborations span MIT, USC, Zhejiang University, and industry partners including PMSW Research Pty Ltd and Multiplex Constructions Pty Ltd.
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 .
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.
Ron Steinfeld is an Associate Professor in the Department of Software Systems & Cybersecurity at Monash University, Australia. He holds editorial roles in Designs Codes and Cryptography (since 2017) and has served on technical committees for top-tier conferences like ASIACRYPT, CRYPTO, and EUROCRYPT. His research focuses on quantum-safe cryptography, lattice-based cryptography, and blockchain security, with over 80 refereed publications and AUD$4M+ in research funding. Education: BSc Mathematics and Physics (Monash University, 1998) BE (Hons., First Class) Electrical and Computer Systems (Monash University, 2000) PhD Computer Science (Monash University, 2003) Research Interests: Design and analysis of cryptographic algorithms, quantum-safe protocols, lattice-based security foundations, and blockchain applications. His work underpins NIST standard algorithms like Kyber and Dilithium through structured lattice problem research. Recent Articles Trends: Focus on privacy-preserving blockchain protocols, post-quantum signature schemes, encrypted data search, and cryptographic applications in adversarial AI. Recent work includes fair Bitcoin watchtower schemes and genomic database privacy solutions. Awards: ASIACRYPT 2015 Best Paper Award Advising & Grants: Supervises PhD projects on quantum-safe cryptography and blockchain security. Leads initiatives like the AUD$4M ARC-funded Quantum Information Technology project. Collaborates with industry partners including CSIRO/Data61. Labs/Teams: Key contributor to Monash’s cybersecurity and cryptography research groups, involving interdisciplinary projects on materials discovery ( Æinstein initiative) and post-quantum blockchain security.
Massimo Piccardi is a Professor of Natural Language Processing (NLP), Computer Vision, and Machine Learning at the University of Technology Sydney (UTS) , where he has been since 2002. He currently serves as the Head of the School of Electrical and Data Engineering and leads the Big Data Analytics program at the Global Big Data Technologies Centre. His research focuses on advancing NLP, machine learning applications in healthcare, and cybersecurity in IoT systems. He has authored over 200 journal papers and conference proceedings, secured significant ARC and CRC grants, and holds the IEEE Computer Society Distinguished Contributor Award (2022). Education & Professional Roles: Joined UTS in 2002, progressing from Associate Professor (2002–2007) to Professor (2008–present). Serves as Associate Editor for IEEE Transactions on Big Data and Editor for Artificial Intelligence in Medicine. Active in professional societies including IEEE, ACL, and ALTA (President, 2023–2024). Research Interests: Core areas include NLP (translation, summarization, adversarial attacks), healthcare informatics (clinical NLP, health service analysis), and cybersecurity (IoT security, privacy-preserving systems). Cross-cutting themes include generative models, cross-lingual systems, and ethical AI. Grants & Projects: Principal Investigator on ARC Discovery/Linkage projects and CRC grants. Recent projects include controllable machine translation (Amazon), privacy-preserving digital agriculture, and STEM innovation (ASTRID project with NBN Co). Labs & Collaborations: Leads the UTS Global Big Data Technologies Centre, collaborating on projects like adversarial NLP attacks, medical machine translation, and secure IoT frameworks.
Marcus Smith is an Associate Professor in Law at the Charles Sturt University , where he teaches LAW222 Technology Law and directs the Bachelor of Laws program. He holds advanced degrees from the Australian National University (PhD, LLM) and the University of Cambridge (MPhil). His research spans technology law and regulation , focusing on genomic data governance biometric identification AI ethics blockchain policy cybersecurity surveillance law He leads the Contemporary Threats to Australian Security research group and serves as Chief Investigator on an NHMRC-funded project (MRF2015531) addressing genomic dataset governance. His recent work analyzes algorithmic bias in facial recognition AI in healthcare blockchain's regulatory challenges post-pandemic cybercrime surveillance ethics data security frameworks He actively supervises PhD and honours students in technology law and contributes to law reform through submissions to international bodies like the UN Human Rights Council .
Professor Ernest Foo is a distinguished academic at Griffith University's School of Information and Communication Technology, specializing in cybersecurity with a focus on industrial control systems and cryptographic protocols. With over 15 years of experience in computer networking, he has established himself as a leading expert in SCADA security and smart grid cybersecurity. His research has significant practical applications in critical infrastructure protection, and he has developed hands-on security training programs that have trained professionals from major Australian utilities and government agencies. Professor Foo's educational background includes: Bachelor of Engineering with Honours in Electronic and Computer Engineering from University of Queensland Doctor of Philosophy from Queensland University of Technology Professor Foo's research interests center around secure cryptographic protocols with specific applications in industrial control system security and cyber physical systems. His work spans SCADA security, smart grid protection, wireless sensor network security, and post-quantum cryptography. He has made significant contributions to understanding vulnerabilities in industrial protocols like Modbus and DNP3, and has pioneered the application of process mining and data mining techniques for attack detection in critical infrastructure systems. His research bridges theoretical security concepts with practical implementations in real-world industrial environments. Professor Foo's recent publications demonstrate a clear trajectory toward increasingly sophisticated security frameworks for critical infrastructure. His work shows a progression from foundational SCADA security research to advanced applications of artificial intelligence, machine learning, and formal methods in cybersecurity. A notable trend is the integration of zero trust principles with industrial control systems, alongside growing emphasis on post-quantum cryptographic solutions. His publications span high-impact journals and conferences in cybersecurity, with increasing focus on anomaly detection in cyber-physical systems and the application of graph-based machine learning techniques to network security challenges. Professor Foo's notable scientific achievements include: Best paper award at the 2nd International Cyber Resilience Conference for "Gap analysis of Intrusion Detection in Smart Grids" Professor Foo has secured significant research funding including an ARC Linkage grant with Powerlink Queensland focused on cyber security for electricity sub-stations. He currently leads multiple research projects including Westpac Micro-Credentials in Financial Crime Investigation, Digital Banking Micro-credentials with ANZ, and research on quantum-safe cryptography. As a dedicated educator, he serves as Program Director for multiple cybersecurity programs including the Master of Cyber Security, and has supervised numerous doctoral and masters students. His Cyber Security: Industrial Control System course, conducted annually from 2013-2018, featured innovative hands-on training with real-world participants from major Australian utilities. Professor Foo has been instrumental in establishing the SCADA security research laboratory with multiple vendor system miniatures running industrial PLCs. His work bridges theoretical security concepts with practical implementations, making significant contributions to the security of industrial control systems and critical infrastructure worldwide.
Prof Christina Lim is a Professor at the Department of Electrical and Electronic Engineering, University of Melbourne, Australia. She serves as the Associate Dean of Research for the Faculty of Engineering and Information Technology (FEIT) and manages the Tucker Lab. Previously, she held roles as Research Group Leader of the Electronics and Photonics System group and Deputy Head of Department (Teaching and Operations) Education: PhD and Bachelors from University of Melbourne Research Interests: Radio-over-Fibre, Optical Wireless Communications, Microwave Photonics, Augmented Reality Displays, Reservoir Computing, Optical Crosshaul Networks Recent publications demonstrate expertise in optical waveguide design for AR, underwater optical wireless communications, photonic switching, and network optimization. Her projects focus on next-generation wireless infrastructure, including Photonics Computing Enabled Ultra-Broadband Wireless Communications (2024-2027, $598k ARC grant) and Additive Manufacturing of Optical Elements (2025). She has secured significant funding, including ARC Discovery Projects and Future Fellowships. Scientific Honors IEEE Fellow (2022) Optica Fellow (2018) ARC Future Fellow (2009-2013) ARC Australian Research Fellow (2004-2008) Professional Service Vice-President of Conferences, IEEE Photonics Society Deputy Editor, IEEE/Optica Journal of Lightwave Technology ARC College of Experts (2014-2016)
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.
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.
Dr. Aditya Joshi is a Senior Lecturer in the School of Computer Science & Engineering at the University of New South Wales (UNSW). He specializes in Natural Language Processing (NLP), with a focus on sarcasm detection, dialectal NLP, and ethical AI applications in public health and cybersecurity. He joined UNSW in 2023 following industry roles at SEEK, Notiv, and Fractal Analytics, where he developed NLP systems for recommendation engines and meeting analytics. His research has garnered over 3,000 citations (h-index 26) and secured $3.1M in grants, including Defence Trailblazer and Google exploreCSR awards. Education: Joint PhD (2018) from IIT Bombay (India) and Monash University (Australia); MTech in CSE (2011) from IIT Bombay. Research Interests: Making NLP models robust for non-native English speakers and the LGBTI+ community, algorithmic enhancements to transformers, and applications in public health, cybersecurity, and societal issues. His work spans epidemic intelligence (collaborations with EPIWATCH and IFCYBER), cybersecurity tools like AuditNet, and inclusive AI initiatives such as queer-inclusive workshops funded by Google. He designed UNSW's new NLP course (COMP6713) and co-authored a Wiley textbook on NLP. Notable grants include the A$1.4M 'Comprehensive Defence Data Platform' (Lead CI) and A$92K Google exploreCSR grant for benchmarking dialectal sentiment. His awards include the Best PhD Thesis from IITB-Monash and Best Paper accolades at FAccT 2023 and MoMM 2020. He supervises projects on kernel-based attention reformulation, prompt-based sarcasm detection, and multilingual small-scale LLMs. His service roles include Executive Committee Member at ALTA and arXiv moderator for computational linguistics.
Dr. Farha Sattar is a Lecturer in Education (Mathematics) at the Faculty of Arts and Society, Charles Darwin University. With over thirty years of teaching experience in Mathematics, Science education, GIS, Remote Sensing, and e-Learning technologies at undergraduate and postgraduate levels, Dr. Sattar brings extensive expertise to her academic role. She holds a PhD in Geospatial Science from Charles Darwin University, focusing on Spatial Mathematical Modelling for 3D Gully Mapping and Erosion Quantification. Her educational background includes: PhD in Geospatial Science (Three Dimensional Gully Mapping and Erosion Quantification within a Geoinformatics Framework), Charles Darwin University (2012) Prior academic experience at several Asian and European universities Dr. Sattar's research spans multiple interdisciplinary fields with a strong focus on the intersection of education and technology. Her primary research interests include Mathematics Education, STEM Education, Geoscience Education, Spatial Mathematical Modelling, Drone Technology, Remote Sensing, Geographic Information Systems, and Innovative Pedagogies such as experiential learning and inquiry-based approaches. She has developed expertise in cognitive development, particularly in fostering critical and creative thinking skills through iSTEM approaches. An analysis of Dr. Sattar's recent publications reveals a consistent trajectory toward integrating emerging technologies like drones, augmented reality, and geospatial artificial intelligence into educational contexts. Her work demonstrates a progression from foundational geospatial research on erosion mapping to innovative applications in STEM education. Recent publications increasingly focus on drone technology for educational purposes, spatial data infrastructure for policy development, and computational thinking development through technology-enhanced learning. Dr. Sattar has received numerous accolades for her contributions to education and research: NT Science Week Awards – Inspired NT STEM Hero of the Year award (2021) Long Service Recognition Award (2023) Finalist, CDU Alumni Award (2023) Multiple Best Presentation Awards (2008, 2010, 2017) Australian College of Educators - 2021 College Medal Nominee As a supervisor, Dr. Sattar mentors postgraduate research students including Ayesha Farhan, who is working on "A Framework for Cognitive Development: Fostering Critical and Creative Thinking Skills, Using iSTEM Approach." She actively secures research funding through various projects such as "DBELaSTEM: Drone-Based Experiential Learning and STEM Education," "STEM_D & VR: Youth STEM Learning and Empowerment with Drones and VR," and "CodProg Drone Lab." Her research is supported by grants from NT Government projects and other external funding sources. Dr. Sattar leads the CodProg Drone Lab, an innovative research space exploring drone technology applications in education. She is a certified drone pilot and aeronautical radio operator, which enables her to bridge theoretical knowledge with practical applications. Her work with the "Flying Forward: Drones, STEM Equity, and Indigenous Empowerment" initiative demonstrates her commitment to community engagement and addressing educational disparities through technology.
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.
