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.
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.
Professor Khin Than Win is a leading academic in health informatics and digital health at the University of Wollongong (UOW), holding appointments as Professor in the School of Computing and Information Technology, Head of Postgraduate Studies, and Deputy Head (Research). She also serves as Academic Program Director for UOW's Master of Health Informatics and Graduate Certificate in Health Analytics programs. Her research focuses on applying information technology to healthcare, particularly in behavior change support systems, persuasive technology, and ethical AI applications. She has supervised over 20 PhD students and holds leadership roles including Deputy Chair of UOW's Health and Medical Research Ethics Committee, and membership in international committees like the Persuasive Technology Steering Committee. Education: MBBS from Rangoon University, Master's and PhD in IT from Assumption University (Bangkok) and UOW (Australia) Research Interests: Health data analytics, AI in healthcare, privacy/security of health systems Leadership: Program/General Chair roles at ACIS and Persuasive Technology conferences Awards: Best Paper Awards (2023, 2018) Her extensive funding portfolio includes ARC grants and NHMRC projects, totaling over 24 funded initiatives. Current research explores blockchain in medical passports, AI ethics, and culturally tailored health interventions.
Professor Amin Abbosh is a faculty member at the School of Electrical Engineering and Computer Science, University of Queensland. His research focuses on Medical Microwave Imaging and Millimeter-wave Engineering, with contributions to advanced imaging systems, antenna design, and communication technologies. He leads projects in electromagnetic medical sensing, including portable brain scanners and wearable diagnostic systems. His work integrates applied electromagnetics with AI-driven algorithms, addressing challenges in stroke detection, liver health monitoring, and deep vein thrombosis diagnosis. With over 16 patents and collaborations across biomedical and engineering domains, his research bridges clinical needs with cutting-edge electromagnetic techniques. Key projects include the development of low-cost healthcare monitoring systems and reconfigurable antennas for satellite communications. Research interests span medical imaging systems, antenna array design, and signal processing for healthcare applications. His team innovates in areas like phased arrays, dielectric property analysis, and non-invasive diagnostics. Recent advancements include synthetic microwave focusing techniques and self-supervised deep learning models for clutter removal in imaging. Publications highlight contributions in IEEE journals and conferences, emphasizing clinical applications and device prototyping. Collaborations with institutions like the University of Queensland’s medical faculty and industry partners ensure practical implementation of his research.
Professor Judy Kay is a leading academic in Computer Science at the University of Sydney, holding a Chair in Human-Centred Technology Research within the Faculty of Engineering and IT. Her work focuses on designing personalized systems that empower users to control their data, particularly in lifelong learning and health contexts. She leads the Human Centred Technology Research Cluster and is Editor-in-Chief of the International Journal of Artificial Intelligence in Education (IJAIED). Education: Studied at the University of Sydney, inspired to pursue academia due to its multidisciplinary environment and student collaboration opportunities. Research interests include user modeling, scrutable AI systems, and interfaces for personal informatics. Current projects span virtual reality learning environments, health data dashboards, and ethical AI transparency. Her work has led to commercialized technologies like the Personis system. Publications include over 200 peer-reviewed works in top venues like CHI, UbiComp, and AIED conferences. Awards highlight her contributions to computing education and AIED, including the 2023 Lifetime Achievement Award. Advises 4 current PhD/Master’s students focusing on topics like Open Learner Models in VR and health decision-making tools. Collaborates with multidisciplinary teams across computer science, health sciences, and education. Labs/Teams: Leads research groups in the School of Computer Science and collaborates with the Charles Perkins Centre on health-related projects.
Yang Song is an ARC Future Fellow and Scientia Associate Professor at the School of Computer Science and Engineering , University of New South Wales (UNSW) . She serves as Associate Head of School (Research) and Co-Director of iCinema , focusing on AI and Computer Vision applications for social good. Education: BEng in Computer Engineering (Nanyang Technological University, Singapore), PhD in Computer Science (UNSW, 2013) Research Areas: Biomedical image analysis, human-centred AI, graph data modeling, neuro-symbolic learning, and AI trustworthiness. Her work develops domain-specific deep learning models for radiological segmentation, histopathology cancer analysis, and 3D reconstruction. Recent projects address explainability in LLMs, fairness in AI, and human-robot interaction frameworks. With over 200 peer-reviewed publications in top venues like CVPR , MICCAI , and NeurIPS , her research spans biomedical imaging, robotics, and general multimodal AI. Scientific Awards include: 2024: ARC Industrial Transformation Research Hub for Human-Robot Teaming 2023: Google Inclusion Research Award 2022: NHMRC Ideas Grant for computational brain imaging 2021: UNSW Engineering Research Excellence Award 2020: Scientia Fellowship (UNSW) 2019: ARC Future Fellowship She supervises 24 current PhD/MPhil students and has graduated 15 advisees, including placements at Harvard University and Siemens Healthineers. Her grants include collaborations with Surf Life Saving Australia and industry partnerships for AI-driven solutions.
Richard Garner is a lecturer at Macquarie University's School of Mathematical and Physical Sciences, Faculty of Science and Engineering. He specializes in teaching mathematics to engineering and computing students in units like MATH2055 and MATH1007, focusing on problem-solving and real-world applications. His teaching philosophy emphasizes authentic mathematical experiences, blending abstract concepts with practical examples, such as connecting multivariable calculus to AI technologies. School: School of Mathematical and Physical Sciences University: Macquarie University Teaching Areas: Mathematics for engineering and computing, convolution, multivariable calculus Richard won a Student Nominated Award in the 2023 Vice Chancellor’s Learning and Teaching Awards, reflecting his commitment to student-centered education. He prioritizes clarity in course design, using visual tools and accessible materials to enhance learning, and fosters a supportive environment where students feel comfortable asking questions. Key Teaching Strategies Organized iLearn layouts following Macquarie University's standards Multiple formats for lecture materials (diagrams, color-coded slides) Weekly task clarity and real-world problem framing Live worked examples and transparent success criteria His research spans category theory, computational effects, and homotopy theory, with publications on topics like comodels, monoidal bicategories, and enriched categories. Richard's work bridges abstract mathematics with applications in computer science and logic. Scientific Awards 2023 Vice Chancellor’s Learning and Teaching Award (Student Nominated) Students praise his ability to make complex concepts intuitive, his enthusiasm for mathematics, and his dedication to explaining the 'why' behind the subject. His teaching design, including time-sensitive banners and structured weekly content, has been highlighted as exemplary.
Professor Arcot Sowmya is a distinguished academic at the University of New South Wales, serving as Professor in the School of Computer Science and Engineering. With a strong background in both computer science and mathematics, she has established herself as a leading researcher in machine learning and computer vision applications, particularly in medical imaging and diagnostics. Dr. Sowmya earned her PhD in Computer Science from the Indian Institute of Technology, Bombay, along with an MTech in Computer Science, MSc in Mathematics, and BSc in Mathematics from the same institution. Her academic journey has positioned her at the intersection of theoretical computer science and practical medical applications. Her research interests span multiple domains with a primary focus on Machine Learning for Computer Vision . She has made significant contributions to learning object models, feature extraction, segmentation, and recognition techniques. Her work extends into medical image analysis, computer-aided diagnostics, high-resolution remote sensing, and biomedical informatics. More recently, she has applied similar techniques to social sciences domains, developing improved forecasting models for genocide and politicide. Her earlier work also includes contributions to real-time, concurrent, and embedded systems. Analyzing her recent publications reveals a strong trend toward medical applications of computer vision and deep learning. Her work spans from OCT-based glaucoma diagnosis to tumor segmentation, lung disease detection, and breast cancer prognosis. She has successfully bridged computer science with clinical medicine, developing practical tools for disease diagnosis and prediction that incorporate explainable AI approaches. Professor Sowmya's collaborative approach is evident in her extensive publication record across multiple journals and conferences. She has worked with researchers from diverse fields including ophthalmology, oncology, neurology, and public health, demonstrating the interdisciplinary nature of her research. Her laboratory work focuses on developing robust deep learning architectures for medical image analysis, with particular attention to segmentation networks, transformer models, and multimodal data fusion techniques. Her team has developed specialized networks for lung segmentation, tumor detection, and disease classification that address specific challenges in medical imaging.
Christopher Ferrie is an Associate Professor at the University of Technology Sydney (UTS), where he is affiliated with the Faculty of Engineering and Information Technology and the Centre for Quantum Software and Information (QSI). His academic career spans quantum information science, machine learning, and scientific education, with a strong emphasis on both theoretical research and public engagement through science communication. Full-time faculty member at UTS Active researcher in quantum information science Director of the Centre for Quantum Software and Information Author of numerous scientific publications and popular science books Dr. Ferrie earned his PhD in Applied Mathematics from the Institute for Quantum Computing and University of Waterloo in Canada in 2012. His doctoral work focused on quantum information and laid the foundation for his subsequent research career in quantum computing and related fields. Dr. Ferrie's research interests span several interconnected domains within quantum information science. His primary focus is on quantum estimation and control, with particular emphasis on applying machine learning techniques to solve statistical problems in quantum information science. He investigates how quantum systems can be characterized, controlled, and optimized for practical applications. His work bridges theoretical quantum physics with practical implementations, exploring how quantum phenomena can be harnessed for computational advantage. Recent research directions include quantum machine learning, quantum neural networks, and quantum optimization algorithms, with applications ranging from quantum state tomography to solving combinatorial optimization problems. Analysis of Dr. Ferrie's recent publications reveals a strong focus on practical quantum computing challenges. His work consistently addresses the intersection of quantum information theory and machine learning, with particular emphasis on making quantum algorithms more efficient, interpretable, and robust against noise. A significant portion of his recent research explores variational quantum algorithms and their optimization, reflecting the current priorities in near-term quantum computing. His publications also demonstrate growing interest in quantum machine learning applications and the development of techniques for quantum error mitigation and characterization. Dr. Ferrie has secured multiple research grants supporting his work in quantum computing and related fields. His funded projects span quantum control, quantum probability, quantum machine learning, and statistical decision theory, reflecting the breadth of his research program. While specific major awards aren't detailed in the available information, his sustained funding and publication record indicate significant recognition within the quantum information science community. Dr. Ferrie is actively involved in research supervision and teaching, with current funding supporting multiple PhD students and postdoctoral researchers. His teaching responsibilities include courses on quantum computing, where he introduces students to the fundamentals of quantum information processing. His research group at the Centre for Quantum Software and Information focuses on developing novel quantum algorithms and exploring the practical implementation challenges of quantum computing. The Centre for Quantum Software and Information at UTS serves as the primary research environment for Dr. Ferrie's work. This center brings together researchers working on various aspects of quantum computing, from hardware development to algorithm design and applications. Dr. Ferrie's team within the center focuses specifically on quantum software development, quantum algorithm design, and the application of machine learning techniques to quantum information problems. The collaborative environment enables interdisciplinary research that bridges theoretical quantum physics with practical computing applications.
Dr. Thilina Halloluwa is a Teaching Focused Lecturer in the Department of Human-Centred Computing at The University of Queensland (UQ). He holds a PhD in Human-Computer Interaction from Queensland University of Technology (2019) and a Computer Science undergraduate degree from the Sri Lanka Institute of Information Technology. With over 15 years of academic and industry experience, his research emphasizes real-world impact in education technology, financial inclusion, smart agriculture, and HCI. Educational Background: PhD in Human-Computer Interaction, Queensland University of Technology (2019) Bachelor of Computer Science, Sri Lanka Institute of Information Technology Research Interests: Education for All: Leveraging technology to enhance collaborative learning and social experiences in education. Human Money Interaction: Designing ethical AI solutions for financial services, particularly for underserved communities. Smart Agro: Developing AI-driven tools for crop disease detection, yield optimization, and precision agriculture. Software Project Estimation: Improving effort estimation accuracy through explainable AI (Metrix project). Key Contributions: Developed UrbanAgro (tomato disease detection) and BellCrop (bell pepper disease datasets). Pioneered Dhana Labha , a financial management tool for rural Sri Lankan communities. Advanced online exam proctoring systems for low-resource settings. Previous Roles: Lecturer at University of Sydney (2023) Senior Lecturer at University of Colombo (2013–2023) Lab/Team Affiliations: Smart Agro Project: AI-driven agricultural solutions Metrix Initiative: Software project estimation frameworks
Jon McCormack is a Professor jointly appointed in Monash University's Faculty of Art, Design & Architecture (MADA) and Faculty of Information Technology. He founded and directs SensiLab, a research facility focusing on computational creativity, human-machine interfaces, and generative systems. His work spans electronic media art, evolutionary music, and artificial life. McCormack holds a PhD in Computer Science from Monash University, along with degrees in Computer Science, Applied Mathematics, and Film/Television. Research interests include computational creativity, tangible interfaces, and cybernetic systems. Notable projects include 'Explainable Artificial Creativity' (ARC-funded) and 'Building 4.0 CRC,' addressing architectural innovation through AI. He has been recognized with awards for collaborative projects like the Blundstone Intelligent Footwear for Healthcare. McCormack's recent articles explore AI-driven art, generative systems, and interdisciplinary design. His work bridges artistic practice with technical innovation, emphasizing ethical and creative dimensions of human-AI collaboration. SensiLab serves as a hub for practice-based research in digital media and interactive systems. Education: PhD in Computer Science, Monash University (2004) Bachelor of Science (Honours), Computer Science/Applied Mathematics, Monash University (1987) Graduate Diploma in Film/TV, Swinburne University (1986) Bachelor of Science, Computer Science/Applied Mathematics, Monash University (1985) Key Projects: Lead investigator on 'Explainable Artificial Creativity' (2022–2026) Co-investigator in 'Building 4.0 CRC' (2020–2027), exploring AI-driven architectural design Awards: 2022 Designers Australia Award for Blundstone Footwear 2020 'On the Machine Condition' Prize McCormack's lab, SensiLab, fosters collaborations across disciplines, producing exhibitions, software, and theoretical frameworks for computational creativity. He actively supervises PhD students in practice-based research, emphasizing the intersection of art and technology.
Dr Ehsan Nabavi is a Senior Lecturer in Technology and Society at the College of Asia and the Pacific (CPAS), Australian National University (ANU). He leads ANU’s Responsible Innovation Lab, focusing on responsible computing, modeling, and AI ethics. His interdisciplinary work bridges technical and social sciences, particularly addressing wicked problems in sustainability and water governance. Current Affiliation: ANU (CPAS), since 2020 Former Roles: Research Fellow at ANU School of Cybernetics (2018–2020), Harvard Kennedy School (2016–2017) Visiting Positions: SOAS University of London, University of Bonn (ZEF) His research spans responsible AI , transdisciplinary modeling , and socio-technical systems , as reflected in his publications across journals like Nature Humanities and Social Sciences Communications , IEEE Transactions on Technology and Society , and Water Alternatives . His recent articles emphasize ethical AI deployment, human-water systems, and integrating social aspects into modeling. Labs: Responsible Innovation Lab (ANU) Future Work: Developing frameworks for responsible AI in sustainability and policy contexts
Professor Eduardo Velloso is an academic staff member at the School of Computer Science , University of Sydney . He is a member of the Centre for AI Trust and Governance and holds a PhD in Computer Science from Lancaster University and a Bachelor of Computer Engineering from Pontifical Catholic University of Rio de Janeiro. Teaches COMP4447/5047 - Pervasive Computing and INFO1111 - Computing Professionalism Research Interests focus on distributed collaboration in mixed reality , human-AI interaction , and HCI theory and methodology . His work integrates Engineering, Design, and Psychology to explore gaze interaction, adaptive agents, and multimodal interfaces. Key projects include Blended Whiteboard for remote MR collaboration and GazeGrip for mobile accessibility. Publication Trends show expertise in Virtual Reality , Mixed Reality , and Human-AI Interaction , with recent work on Algorithmic Recourse and Immersive Educational Tools . Awards include ACM Best Paper Awards at CHI, UIST, TOCHI, and DIS venues. Scientific Awards 2024 ACM CHI & DIS Honorable Mentions 2022 UoM-FEIT Teaching & Learning Award 2019 UoM-CIS Excellence in Research Award 2015 ACM UIST Best Paper Award Advising includes supervision of research students Marvin, Tinghui LI, and Wendi YU in projects on asynchronous MR collaboration , situationally-induced impairments , and physical environment integration . His lab explores AI-assisted interaction and context-aware computing through projects like SpinalLog and LiftSmart .
Professor Ibrahim Khalil is a faculty member in the School of Computing Technologies at RMIT University, Melbourne, Australia. He holds a PhD in Computer Science from the University of Bern (2003) and has extensive industry experience in Silicon Valley focusing on secure network protocols. His research spans Security, Privacy, Federated Learning, Blockchain, Quantum Computing, and Distributed Systems. He leads high-impact projects funded by ARC grants (DP250100582, DP220100215, etc.) and international initiatives like the EU’s SELFY project. His work addresses challenges in secure AI data analytics, privacy-preserving systems, and critical infrastructure protection. Khalil supervises PhD/Masters students on topics ranging from federated learning security to quantum-enhanced machine learning. Education: PhD in Computer Science (University of Bern, 2003); prior roles at EPFL, Osaka University, and industry tech hubs. Research Interests: Privacy-Preserving Technologies Blockchain Applications in Healthcare and Supply Chains Quantum Computing for Machine Learning Secure Edge Computing and Federated Learning IoT Security and Critical Infrastructure Protection Grants & Collaborations: Over 10 major grants since 2017, including ARC Discovery/Linkage Projects and international partnerships (QNRF, EU). Notable projects include Privacy-Aware Digital Twins for Critical Infrastructure and Federated Learning frameworks for GenAI models. Advising & Labs: Active supervisor of 25+ research projects since 2013, focusing on anomaly detection, secure data analytics, and blockchain-based systems. Collaborates with industry partners on defense and healthcare tech.
