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.
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.
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.
Professor Janet McColl-Kennedy is a leading figure in marketing and service science at The University of Queensland's Business School. She serves as Program Lead for Innovation Pathways (FaBA) and co-founded the Service Innovation Alliance (SIA) research hub, focusing on customer experience, AI, digital transformation, and sustainability. With Fellow status in the Academy of the Social Sciences in Australia and the Australian and New Zealand Marketing Academy, she holds international recognition including Research.com's World's Best Business Scientists ranking and the Christopher Lovelock Career Contributions to the Services Discipline Award (2025). University of Queensland Business School Honorary Visiting Professor, Cambridge Service Alliance Research Collaborator, University of Cambridge Her research spans customer experience management, service ecosystems, digital transformation, and healthcare services. She has pioneered frameworks for customer value co-creation and service recovery strategies, integrating AI and behavioral science. Her 2019 paper on customer experience insights was implemented by a major B2B organization, while her work on value co-creation improved outcomes at Lutheran Community Care. Recent publications focus on AI impacts in food and beverage services, sustainable service ecosystems, and digital health interventions. She has secured over $89 million in competitive grants, including multiple ARC Linkage and Discovery Projects, and leads cross-disciplinary teams with institutions like Cambridge University and Arizona State University. 2024 - ARC College of Experts appointment 2023 - Bo Edvardsson Industry Impact in Services Award 2022 - Elected Fellow of Academy of the Social Sciences in Australia 2021 - Clarivate Highly Cited Researcher 2018 - Ranked most influential marketing academic in Australia With over 220 publications and a Google Scholar H-index of 62, she has mentored 15 PhD students and examined theses at multiple universities. Her teaching spans 30+ years across undergraduate, postgraduate, and executive programs in Australia, USA, Italy, China, and Korea, with awards for blended learning and corporate education.
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.
Dr. Teresa Wang is a Senior Lecturer in Data Science at Monash University's Faculty of Information Technology, specializing in entity/user modeling, relational/structural machine learning, and graph/network analysis. She holds a Ph.D. from the University of Queensland and degrees from Nanjing University. Currently, she directs the Master of Data Science Program and teaches courses like FIT5201 Machine Learning. Her research focuses on social, e-commerce, and health data modeling, with notable projects including the Knowledge Enriched Approach for Effective Personalization (2025–2027) and collaborations on AI in Mental Health and Site Safety. Dr. Wang has co-authored over 59 publications, emphasizing areas like ontology matching and multimodal data analysis. She actively supervises PhD students and contributes to initiatives like the CSIRO Next Generation Graduates Program for clean energy and sustainability. Education: Ph.D. in Computer Science (2017), University of Queensland Master of Computer Science (2013), Nanjing University Bachelor of Software Engineering (2010), Nanjing University Research Interests: Entity modeling, spatio-temporal data analysis, graph mining, recommender systems, and health/medical records mining. She explores applications in social media, e-commerce, and healthcare sectors. Projects: "Knowledge Enriched Approach for Effective Personalization" (2025–2027) "AI for Clean Energy and Sustainability" (2023–2027) "CSIRO Next Generation Graduates Program: AI in Mental Health" (2023–2027) "Large-scale multimodal knowledge management" (2022–2025) Grants & Collaborations: Engaged with CSIRO, Crank Group, and Pola Practice Pty Ltd. Her work aligns with UN SDGs in education and sustainable energy systems. Labs/Teams: Part of the Monash Energy Institute and Monash Data Futures Institute, contributing to interdisciplinary AI and energy research.
Dongming Xu is an Associate Professor in Business Information Systems at the University of Queensland Business School. She holds a PhD from the City University of Hong Kong in Information Systems and has established herself as a prominent researcher in the field of information systems with over 100 publications in top-tier journals and conference proceedings. Her educational background includes a PhD from City University of Hong Kong in Information Systems, though specific details about earlier degrees are not provided in the available text. Dr. Xu's research focuses on the confluence of information technology use and innovation, with particular emphasis on IT entrepreneurship, social media applications in business contexts, and business intelligence systems. Her work explores how information systems influence society and business performance, with applications spanning disaster management, eFinance, eHealth, and knowledge management. She combines theoretical model building with laboratory and field experiments, often developing prototype systems to validate her research. Her publication record demonstrates consistent high-quality output across multiple domains of information systems research, with recent work emphasizing digital disruption, platform ecosystems, social media in disasters, healthcare technology, and micro-learning applications. Her research shows a clear trajectory from foundational work on intelligent agents and decision support systems toward contemporary topics in digital transformation and platform-based innovation. Associate Editor, Information & Management Associate Editor, Journal of Electronic Commerce Research Associate Editor, Australasian Journal of Information Systems Dr. Xu has supervised numerous PhD students to completion, with research topics spanning digital disruption, IT startup development, social media in disasters, conceptual modeling, and environmental management. She has received multiple research grants, including current funding for 'Empowering Australia's Visual Arts via Creative Blockchain Opportunities' (2023-2026) and past projects on 'Smart micro learning with open education resources' (2018-2022). Her research has been supported by various agencies including the Hong Kong Government Research Grant Council, The National Natural Science Foundation of China, The University of Queensland, and City University of Hong Kong. She leads research in several key areas including IT entrepreneurship, business intelligence systems, and social media applications across multiple domains. Her work often involves developing innovative systems such as web-service-agent-based family wealth management systems, decision support systems for securities exception management, and knowledge management systems for disaster management.
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.
A/Pr Steven Goh is an Associate Professor in Mechanical and Mechatronic Engineering at the University of Southern Queensland (USQ), affiliated with the School of Engineering. He holds advanced degrees including a DEng from USQ and is a Fellow of Engineers Australia. His research focuses on engineering education, practice, management, and biomedical engineering. He has received notable awards such as the 2015 Australian Government OLT Citation for Outstanding Contribution to Student Learning and multiple USQ accolades. Education: BEng(Hons) in Manufacturing & Materials (UQ), MBA (Deakin), MProfAcc (USQ), DEng (USQ), and a Diploma in Company Directorship (AICD). Research Interests: Engineering education innovation, sustainable energy systems, and biomedical applications. He actively contributes to professional bodies like the Australasian Association of Engineering Education and serves as Editor (Strategic) for the Australian Journal of Mechanical Engineering. Awards: Multiple teaching excellence awards from USQ (2008-2010) and the 2015 national OLT Citation. Advising/Grants: Not explicitly detailed in text; his roles include supervising students and leading research projects on engineering education and asset management. Labs/Teams: Associated with the Centre for Future Materials and Centre for Health Research at USQ.
Prof Ghassan Beydoun is a Professor and Head of Discipline (Information Systems) at the School of Computer Science, University of Technology Sydney (UTS). He leads the Information Systems discipline and is affiliated with the Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS). His research focuses on AI-driven systems, agent-based modelling, ontologies, and disaster management, with notable contributions to knowledge graphs, enterprise architecture, and IoT applications. Beydoun actively supervises Masters and PhD students in these domains. His research interests span metamodelling, agent systems, and AI applications in disaster management (e.g., flood, landslide, and earthquake risk assessment), health systems, and smart infrastructure. He has pioneered frameworks for reproducible machine learning solutions, digital identity systems, and cloud migration strategies. Beydoun’s work integrates interdisciplinary methods, such as bibliometric analysis for journal evolution and XAI for spatial hazard prediction. Recent publications highlight his expertise in AI for climate-induced hazard modelling, agent-based knowledge transfer mechanisms, and metaverse applications in education. His funded projects include AI-powered circular economy initiatives, smart beach safety systems, and health data querying frameworks. Beydoun collaborates with industry partners like CSIRO, Capsicum Business Architects, and Data Zoo, translating research into practical solutions for enterprise architecture, cybersecurity, and public health.
Dr. Ghazal Bargshady is a Lecturer at the University of Canberra , with expertise in Affective Computing , Artificial Intelligence , and Healthcare Technology . Her roles include teaching units such as Computer Vision, Data Analytics, and Soft Computing, as well as supervising PhD and Master by Research students in AI-driven projects for healthcare and road safety. Education: She earned her PhD in Artificial Intelligence and Computer Vision from the University of Southern Queensland in 2020. Research Interests: Dr. Bargshady specializes in Computer Vision Deep Learning Biosignal Processing Facial Expression Analysis Human Factors in AI Wearable Sensors Multimodal Data Fusion Brain–Computer Interfaces Her work addresses real-world challenges in pain assessment, depression recognition, and driver safety using cutting-edge AI models. Article Trends: Her recent publications focus on Transformer architectures , fNIRS signal analysis , multimodal pain detection , and depression severity estimation via facial video data. These studies highlight her contributions to AI in healthcare , transportation safety , and biomedical signal processing . Teaching Activities: Dr. Bargshady has lectured units including Programming for Data Science , Computer Vision , and Soft Computing , emphasizing practical AI applications.
Yuan-Fang Li is an Associate Professor in the Department of Data Science & AI at Monash University's Faculty of Information Technology. He also serves as Associate Dean International. His research focuses on knowledge graphs, natural language processing, multimodality, and graph representation learning. He holds a PhD from National University of Singapore (2006) and a Bachelor of Computing (Honours) from the same institution (2002). Affiliations: Monash University (since 201?), National University of Singapore (PhD 2002-2006) Key Projects: Leading research on neuro-symbolic systems (HARNESS project), large-scale multimodal knowledge management, and maritime knowledge graphs Teaching: Taught courses including FIT4002, FIT4004, and supervised over 20 PhD students Research interests include complex question answering over knowledge graphs, knowledge extraction from text/images, and structural/temporal graph learning. He has published 152+ works with notable contributions to scene graph generation, event extraction, and LLM-based reasoning. Key awards include the 2020 Best Student Paper Award and 2017 Kurzweil Prize. Grants: ARC Discovery Projects, industry collaborations (e.g., Outotec Oy) Labs/Teams: Active in Monash's Data Science & AI research groups, leading neuro-symbolic AI initiatives
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.
Associate Professor Mathias Baumert is affiliated with the University of Adelaide, where he holds a position in the School of Electrical and Mechanical Engineering under the Faculty of Sciences, Engineering and Technology. He leads the Health Technology research theme in the School of Electrical Electronic Engineering and specializes in biomedical signal processing, focusing on dynamic electrocardiography and sleep-related phenomena. His work integrates clinical applications and technological advancements to address challenges in cardiology and sleep disorders. His research interests include the physiological underpinnings of ventricular repolarization variability and its clinical implications, particularly in post-myocardial infarction patients and those with sleep-disordered breathing. He also develops brain-computer interface (BCI) systems for stroke rehabilitation, leveraging real-time EEG analysis and motor function recovery techniques. Collaborations with clinical partners such as the Women’s and Children’s Hospital, Adelaide Institute of Sleep Health, and the Victor Chang Cardiac Research Institute highlight his translational research focus. His recent articles emphasize signal processing applications for risk stratification in cardiovascular disease, sleep apnea, and diabetes. Key themes include nocturnal hypoxemic burden prediction, REM sleep dynamics, and the development of novel diagnostic markers using ECG and EEG data. His work often bridges engineering and medicine, aiming to translate findings into clinical tools like adaptive servo-ventilation treatment optimization and personalized BCI systems. No scientific awards or fellowships are explicitly listed in the provided texts. He is eligible to supervise Masters and PhD students but current advisee names are not available. His research projects are supported by grants such as ARC DP110102049 (as noted in some articles). He teaches courses including Biomedical Instrumentation and Introduction to Medical Technology . His facilities include ECG equipment, polysomnogram repositories, and a BCI workstation with 64-channel EEG capabilities. He collaborates on lab-based and clinical partner studies to advance cardiac sensing algorithms and sleep-related diagnostic technologies.
