Chris Speed is a Professor of Design for Regenerative Futures at RMIT University, Australia, and Director of Regenerative Futures. Previously, he held academic roles at the University of Edinburgh, including leading the Institute for Design Informatics and co-designing the Edinburgh Futures Institute. His research focuses on design-driven solutions for social, environmental, and economic challenges, leveraging data and emerging technologies. Key areas include blockchain applications, sustainable design, and regenerative systems. Affiliations: RMIT University (Current), University of Edinburgh (2012–2024) Grants: £7.4m Creative Informatics (UK), £5m DECaDE (Digital Economy) Publications: Over 50 peer-reviewed articles on design informatics, regenerative futures, and blockchain ethics. Research interests span Design Informatics, Human-Computer Interaction, and Sustainable Design. He has supervised 23 PhD/MPhil students and pioneered projects like OxChain (blockchain for charitable giving) and Creative Informatics (data-driven creative industries). Awards: Fellow of the Royal Society of Edinburgh (2020), Chancellor’s Award for Research (University of Edinburgh, 2020). Collaborations include BBC, Oxfam, and the Digital Catapult. His work emphasizes co-creation with communities to address systemic challenges, such as climate action and cultural value measurement.
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. 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.
Associate Professor Tongliang Liu is affiliated with the School of Computer Science at the University of Sydney, serving as Director of the Sydney Artificial Intelligence Centre and Trustworthy Machine Learning Lab. He holds a BEng and PhD, and is an ARC Future Fellow. His research focuses on trustworthy machine learning, including adversarial defense, causal representation learning, and robust AI systems. He has authored over 200 papers in top venues like NeurIPS and ICML, and serves as co-Editor-in-Chief of Neural Networks. Research Interests: Developing reliable algorithms for machine learning, emphasizing generalizability and safety. Specific areas include learning with noisy labels, causal inference, and foundational model ethics. He aims to bridge theoretical guarantees and practical applications in computer vision and data mining. Awards: 2024 CORE Award, 2023 IEEE AI's 10 to Watch, 2022 ARC Future Fellowship. Notable recognitions include Eureka Prize shortlist and DECRA. Advising & Grants: Supervises 12 PhD/Master’s students on topics like trustworthy AI, causal discovery, and quantum machine learning. Leads grants on robust learning and AI safety. Labs: Sydney AI Centre and Trustworthy Machine Learning Lab.
Professor Brendan Choat is a leading plant physiologist and Professor at the Hawkesbury Institute for the Environment, Western Sydney University. With a distinguished career in plant hydraulics and water relations research, he has established himself as a global expert in understanding how plants respond to drought stress. His work spans both natural ecosystems and agricultural systems, with particular emphasis on Australian native forests and crop species. Choat's research focuses on the intricate relationship between plant water transport systems and environmental stressors, particularly drought. His work examines how the xylem tissue functions as a hydraulic system that must balance water delivery to leaves while avoiding cavitation (embolism) that can lead to plant mortality. His groundbreaking research has demonstrated that many woody plant species operate close to their physiological safety margins with respect to drought, making them vulnerable to future climate changes. His laboratory employs cutting-edge non-invasive imaging techniques, including X-ray Micro Computed Tomography (microCT) and Magnetic Resonance Imaging (MRI), to directly visualize xylem function in living plants. This approach has allowed his team to address fundamental questions about how cavitation forms and spreads through plant vascular systems during drought stress. Analysis of Professor Choat's extensive publication record reveals a consistent focus on plant drought responses, with particular emphasis on Eucalyptus species and mangrove ecosystems. His research has increasingly incorporated large-scale monitoring approaches, remote sensing data, and trait databases to understand vegetation responses to climate extremes across broader spatial scales. Clarivate Highly Cited Researcher (2018-2024) ARC Future Fellowship (2013) Humboldt Fellowship for Experienced Researchers (2010) Thomson Reuters Citation and Innovation Award (2015) Professor Choat leads multiple significant research projects examining tree dieback in Australian forests, particularly focusing on Eucalyptus species. His work with citizen scientists through the 'Dead Tree Detective' project has provided valuable data on drought impacts across diverse forest biomes. He maintains active collaborations with researchers across Australia and internationally, contributing to large-scale initiatives like the AusTraits plant trait database. His research has direct implications for forest management, conservation strategies, and predicting ecosystem responses to climate change.
Dr. Pulin Gong is an Associate Professor in the School of Physics at the University of Sydney. His research focuses on understanding the self-organizing mechanisms of neural circuits' spatiotemporal dynamics and their computational principles. He investigates distributed dynamic computation via propagating neural waves, irregular neural activity variability, and coherent spatiotemporal patterns in large-scale neural data. His work combines experimental and computational approaches to unravel neural coding principles. Research interests include: Distributed dynamic computation (e.g., visual feature integration) Irregular neural dynamics and membrane potential fluctuations Coherent spatiotemporal wave patterns (e.g., spiral waves) Recent projects involve analyzing cortical wave patterns in mice and primates, fractional neural sampling, and Lévy walk dynamics in neural systems. Collaborators include institutions like Fudan University and Kyoto University. Current research student: Andrew LY, working on cortico-cortical loop dynamics and AI applications.
Dr. David Sewell is a Senior Lecturer and Deputy Head of School (Teaching & Learning) at the School of Psychology, The University of Queensland. His research focuses on attention, learning, memory, and decision-making, with a strong emphasis on formal mathematical models of human cognition. He is affiliated with the Centre for Perception and Cognitive Neuroscience within the Faculty of Health, Medicine and Behavioural Sciences. Education: Bachelor (Honours) of Arts and Doctor of Philosophy, both from the University of Western Australia. David's research explores the intersection of cognitive psychology and computational modeling. Key areas include perceptual decision-making, attentional mechanisms, and the application of diffusion models to understand cognitive processes. His work also extends to sustainability and collective self-regulation through cognitive frameworks. The 15 most recent articles highlight his contributions to modeling decision thresholds in memory prioritization, analyzing gaze cueing effects, and investigating neural correlates of confidence in multisensory decisions. Collaborative projects frequently involve interdisciplinary approaches, combining neuroscience, psychology, and computational methods. He has supervised multiple PhD candidates, serving as Principal or Associate Advisor, with research topics ranging from visual categorization to metacognition in children. Current and past funding includes ARC Discovery Projects on collective self-regulation and category learning constraints.
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.
Didar Zowghi is a Senior Principal Research Scientist and Science Team Leader at CSIRO Data61, Australia's national science agency. His work focuses on advancing ethical AI systems, data quality frameworks, and requirements engineering methodologies. He leads research initiatives addressing challenges in AI governance, diversity/inclusion in technology, and human-centered AI development. Research interests include AI ethics, data completeness in healthcare systems, and the application of machine learning in requirements engineering. His contributions span theoretical frameworks for responsible AI patterns, empirical studies on user perceptions of AI tools like M365 Copilot, and analysis of AI's role in global diplomatic practices. Zowghi has published extensively on topics ranging from blockchain in supply chains to pedagogical innovations in software engineering education. His work often bridges technical systems and societal impacts, emphasizing real-world implementation challenges through collaborative industry-academia projects. Notable outputs include the Responsible AI Pattern Catalogue and studies examining barriers to data quality in IoT platforms. He has pioneered frameworks linking innovation initiatives to occupational skill requirements and developed tools like Elica for dynamic requirements knowledge extraction in agile teams.
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.
Dr. Will Grant is an Associate Professor in Science Communication at the Australian National University’s Australian National Centre for the Public Awareness of Science. His work focuses on science communication, public policy, and the intersection of science with politics and technology. He holds a PhD from The University of Queensland and has been at ANU since 2008. His research explores science communication strategies, misinformation dynamics, and the societal impacts of emerging technologies. He has authored over 100 publications in outlets like Public Understanding of Science and Environmental Communication , and his work has reached 1.8 million readers on The Conversation . He co-founded PostAc, a platform analyzing PhD job markets, and has advised organizations including the Office of the Chief Scientist and CSIRO. Grant’s research interests include science communication pedagogy, the role of social media in shaping public discourse, and the ethical dimensions of geoengineering. He has organized high-impact outreach initiatives like the Long Conversations dialogue series and hosts the podcast The Wholesome Show . His awards include the Vice Chancellor’s Award for Public Policy and Outreach (2015) and the Sidney Sax Medal (2020). He supervises numerous graduate students and teaches courses on science communication ethics, digital media, and science-policy interaction.
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.
Associate Professor Mahsa Baktashmotlagh is an ARC Future Fellow at the School of Electrical Engineering and Computer Science, University of Queensland. Her research focuses on machine learning techniques applied to visual data analysis, biomedical data (e.g., antibacterial activity prediction), and cybersecurity. She holds a PhD from the University of Queensland (2014) and has contributed to over 50 peer-reviewed publications. Her research interests include domain adaptation, deep learning, and robust generalization across domains. Notable contributions include the development of DI-NIDS (a domain-invariant network intrusion detection system) and advancements in open-set domain adaptation. Her work bridges theoretical machine learning with practical applications in healthcare and computer vision. Education: PhD in Machine Learning, The University of Queensland (2014) Awards: ARC Future Fellowship (202X) Research Themes: Domain Adaptation, Cybersecurity, Biomedical AI Her recent work explores challenges in cross-domain generalization, adversarial machine learning, and scalable 3D object detection. She is actively involved in supervising graduate students and collaborates on interdisciplinary projects involving robotics and medical imaging.
Andrew Perfors is a Professor of Psychology at the University of Melbourne, leading the Computational Cognitive Science Lab and directing the Complex Human Data Hub. His research focuses on quantitative approaches to higher-order cognition, including concepts, language, decision-making, and misinformation dynamics. He holds a PhD from MIT and degrees from Stanford University. Education: PhD in Brain & Cognitive Sciences, Massachusetts Institute of Technology (2008) MA in Linguistics, Stanford University (2000) Bachelor of Science in Symbolic Systems, Stanford University (1999) Research Interests: He investigates computational models of cognition, cultural and social evolution, and the spread of misinformation. Recent work emphasizes the cognitive mechanisms underlying inductive reasoning, sampling assumptions, and trust in information. Key Projects: Understanding Information and Trust: From the Individual to the Population (2018–2025) Bridging the Meaning Gap: Computational Approach to Semantic Variation (2023–2027) Awards: Recipient of multiple best paper awards for contributions to cognitive science and computational linguistics. Labs & Groups: Leads the Complex Human Data Hub and co-leads the Computational Cognitive Science Lab, focusing on interdisciplinary research in human behavior and data science.
Associate Professor Sonny Pham leads research in artificial intelligence at Curtin University's School of EECMS. His work balances theoretical foundations with practical applications in computer vision, data mining, and deep learning. As head of the IAMAI research group, he collaborates with industry partners on security systems, healthcare AI, and sustainable technologies. His research explores: Computationally efficient deep learning architectures Compressed sensing for high-dimensional data Robust statistical methods for real-world problems Applications in computer vision and industrial automation Recent publications demonstrate a focus on medical imaging interpretation and efficient neural networks, with applications spanning radiology report generation, semantic segmentation for autonomous systems, and cybersecurity. His team's work consistently bridges theoretical AI advancements with industrial applications. Honors include: Multiple WANMA Awards (2021-2024) for industry-impactful research INCITE Award for social impact technology (2024) IEEE Young Author Best Paper Award (2010) Over $5M in competitive research funding including MRFF and DFAT grants He leads the IAMAI research group with 12+ graduate students and coordinates Curtin's Master of Artificial Intelligence program. Industry collaborations include Alcoa Australia, iCetana, and HyprFire.
