Dylan Campbell is a Lecturer in Computing at the Australian National University (ANU), affiliated with the ANU College of Systems & Society. His research focuses on computer vision, optimization, and robotics, particularly in 3D vision and deep learning applications. He has held prior roles as a Research Fellow at the University of Oxford’s Visual Geometry Group and ANU’s Australian Centre for Robotic Vision. Campbell holds a PhD from ANU (2018) and a BE in Mechatronic Engineering from UNSW (2012). Research interests include geometric sensor alignment, neural radiance fields, and differentiable optimization layers. He actively supervises students (7 PhD/DPhil, 3 MEng, 9 honours) and teaches advanced courses in computer vision and robotics. Notable awards include the Marr Prize Honourable Mention (2017) and the IEEE Australia Council Postgraduate Student Paper Competition (2018). He has organized workshops at ECCV and CVPR, served as a reviewer for top conferences like CVPR/ICCV/ECCV, and contributed to datasets like SEED4D and RefRef. His work emphasizes efficient training of neural networks and leveraging symmetries in data for long-range connections.
Dr. Ehsan Abbasnejad is an Associate Professor at Monash University's Department of Data Science and Artificial Intelligence, and holds adjunct positions at the Australian Institute for Machine Learning (AIML, University of Adelaide) and the Centre for Augmented Reasoning (CAR). He specializes in foundational AI, focusing on vision-language tasks, adversarial machine learning, and reinforcement learning. His work bridges theory with real-world applications in agriculture, energy, healthcare, and sports. Education: PhD in Computer Science from Australian National University (ANU). Research Interests: Machine Learning Theory and Adversarial Defenses Neural Network Robustness and Generalization Multimodal Learning (Vision-Language) Continual and Transfer Learning Applications in Energy, Healthcare, and Robotics Awards: Finalist for Australian AI Academic/Researcher of the Year (2024) Multidisciplinary competition wins (e.g., OzMineral Explorer Challenge) Advising & Grants: Australian Research Council (ARC) Discovery Project on Reinforcement Learning CSIRO's Next Generation Graduate Fund Accepting PhD students in foundational AI and applications Labs & Teams: Director of Foundational Machine Learning & Reasoning at Monash, leading global teams in AI competitions and industry collaborations (Microsoft Research, NEC Labs America).
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.
George Athanasopoulos is Professor and Head of the Department of Econometrics and Business Statistics at Monash University, a position he has held since 2022. He was appointed Professor in 2019 and has established himself as an internationally recognized expert in forecasting, time series analysis, and applied econometrics. He serves as Past President (since 2024) and former Director (2014-2024) of the International Institute of Forecasters, and is Associate Editor of the International Journal of Forecasting since 2014. His research focuses on hierarchical and grouped time series forecasting, where he has pioneered methods for forecast reconciliation and cross-temporal coherence. His work has significantly influenced forecasting practices across diverse fields including national statistics offices, energy markets, and public health. He is particularly renowned for his contributions to tourism forecasting and macroeconomic modeling in big data environments. Awarded the Australian Awards for University Teaching in 2022 for outstanding contributions to student learning, Professor Athanasopoulos has also received multiple Dean's Awards from Monash Business School for research excellence, teaching innovation, and publication quality. His research output includes over 49 publications and leadership of six major research projects, including the ARC-funded 'Macroeconomic forecasting in a Big Data world' and the RACE for 2030 CRC project on clean energy forecasting. His work contributes to UN Sustainable Development Goals through applications in economic forecasting, energy modeling, and sustainable tourism development. He has supervised numerous research students and collaborated extensively with institutions including Australian National University, Griffith University, and international partners across multiple continents.
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.
Professor Brett Hayes is a distinguished cognitive psychologist at the University of New South Wales, serving in the School of Psychology. He is the founding Director of the Sydney Thinking and Reasoning (STAR) Laboratory, which he has led for over 15 years, securing more than $4 million in competitive research funding. Professor Hayes has previously held the position of Head of the School of Psychology and served as a member of the Australian Research Council (ARC) College of Experts. His research expertise spans reasoning, concept learning, memory, and developmental changes in these cognitive processes. Professor Hayes employs both experimental investigation and computational modeling in his work, with a particular focus on applying fundamental cognitive research to practical problems in forensic and clinical decision-making, early childhood education, and climate change science communication. His research has significant interdisciplinary applications across psychology, education, and environmental science. Professor Hayes has published extensively in top cognitive science journals, with his most recent work focusing on inductive reasoning, sampling assumptions, learning traps, and consensus perception. His research demonstrates consistent innovation in understanding how people process information, make decisions under uncertainty, and develop reasoning abilities across the lifespan. His scientific contributions include numerous journal articles, book chapters, and co-authored textbooks on developmental psychology. Professor Hayes has also contributed to teaching through courses such as PSYC3341 Developmental Psychology (which he chairs), PSYC3221 Cognitive Science, and PSYC2061 Developmental and Social Psychology. Professor Hayes maintains an active research program with ongoing collaborations across multiple institutions, as evidenced by his numerous co-authored publications. His laboratory continues to advance our understanding of human cognition through rigorous experimental work and theoretical development.
Tao Zou is an Associate Professor at the Research School of Finance, Actuarial Studies and Statistics, Australian National University. His research spans covariance regression modeling, network data analysis, and applications in financial and environmental statistics. He earned a Ph.D. in Statistics in 2016. Ph.D. in Statistics, 2016 Dr. Zou’s work pioneers covariance regression, where covariances are modeled as functions of covariiates. Key contributions include robust estimation techniques, spatio-temporal modeling, missing data imputation via semi-supervised learning, and distributed data aggregation. His methods address challenges in high-dimensional and non-Euclidean data analysis. Recent publications (2025–2023) explore quasi-score matching for spatial autoregressive models, regularization in network regression, functional principal component analysis for complex data, and environmental applications like PM2.5 pollution studies. These works emphasize robustness, scalability, and interdisciplinary relevance in economics, finance, and environmental science. Dr. Zou collaborates on projects like the 2023 Data Analysis App to Empower Assessment of Immunogenicity of Biologics (Co-Investigator). While his student supervision list isn’t explicitly provided, his methodological advancements influence big data and spatial statistics. He contributes to open-access software and continues expanding covariance regression for non-normal and functional data.
Andrew Zammit Mangion is an Associate Professor at the University of Wollongong , affiliated with the School of Mathematics and Applied Statistics . His research focuses on spatio-temporal statistics, computational methods, and environmental informatics, with applications in climate science and geospatial data analysis. Education : PhD in Statistics (University of Sheffield, 2012), B.Eng. (University of Malta, 2007) Research Themes : Spatio-temporal modeling, Bayesian inversion frameworks (e.g., WOMBAT v2.S), deep learning integration, and statistical software development (e.g., FRK package) Grants & Projects : ARC DECRA Fellow (2018), Chief Investigator on ARC Discovery Project (greenhouse gases), ARC Special Research Initiative (Securing Antarctica's Environmental Future), and ARC Industrial Transformation Hub (TIDE). Collaborations : University of Bristol, University of Edinburgh, ESA CCI, NASA OCO-2 data projects Scientific Awards include the prestigious Australian Research Council Discovery Early Career Researcher Award (DECRA). His work spans Antarctic ice sheet analysis, CO2 flux inversion, and scalable spatial statistical models for environmental monitoring.
Andrea Collevecchio is a Professor in the School of Mathematics at Monash University, Australia, where he has been a faculty member since 2012. His research focuses on the intersection of Probability, Mathematical Physics, and Statistical Mechanics, with particular expertise in stochastic processes and theoretical modeling. He earned his PhD in Statistics from Purdue University in 2004, followed by postdoctoral positions in Italy and Germany. In 2006, he became Assistant Professor at Ca’Foscari University in Venice before joining Monash University. Collevecchio specializes in Reinforced Processes and Large Deviations, investigating complex systems through random walk models. His work bridges abstract probability theory with applications in statistical mechanics, examining phenomena like memory effects in stochastic processes and phase transitions in lattice systems. Recent research emphasizes hypercube structures, non-reversible dynamics, and reinforcement mechanisms. His 2021-2025 publications reveal a concentrated focus on hypercube random walks, with increasing exploration of non-reversible processes, vertex-reinforced dynamics, and bootstrap methods. These works consistently apply probabilistic frameworks to problems in mathematical physics, demonstrating strong connections between theoretical probability and physical modeling. Collevecchio has secured multiple research grants including ARC-funded projects on self-interacting random walks (2023-2026) and random walks with long memory (2018-2022). He contributes to interdisciplinary initiatives like the Smart Vehicles project for dementia support (2025-2027) and actively organizes academic events including the AIM Day series connecting mathematics, AI, and industry applications.
Ruben Loaiza-Maya is an Associate Professor (Research) in the Department of Econometrics and Business Statistics at Monash University. He holds a PhD in Econometrics from the University of Melbourne and an undergraduate degree in Economics from Universidad Nacional de Colombia (Medellin). His research focuses on Copula Modelling, Bayesian Estimation Methods, Time Series Analysis, and Macroeconomic/Financial Forecasting. Key contributions include advancements in variational inference, state space models, and robust forecasting techniques under model misspecification. He leads the active project 'Variational Inference for Intractable and Misspecified State Space Models' (2023–2026), funded as a Primary Chief Investigator. His work contributes to UN Sustainable Development Goals through methodological advancements in economic and financial analysis. Recent research emphasizes scalable Bayesian methods, hybrid variational approaches, and efficient computational techniques for high-dimensional models. Publications span prestigious journals like the International Journal of Forecasting, Journal of Econometrics, and Journal of Business and Economic Statistics. Notable collaborations include studies on copula-based time series forecasting and robust approximate Bayesian computation. His work bridges theoretical econometrics with practical applications in risk management and macroeconomic policy.
Professor Javen Qinfeng Shi is a faculty member at the University of Adelaide, holding the position of Professor in the School of Computer and Mathematical Sciences under the Faculty of Sciences, Engineering and Technology. He serves as Founding Director of the Causal AI Group and as one of the directors at the Australian Institute for Machine Learning (AIML), based at the North Terrace campus location. His research centers on causation, artificial intelligence, mind and metaphysics, with Google Scholar rankings placing him 4th globally in causation and 7th in probabilistic graphical models. Shi develops causal AI methods to identify root causes, discover latent variables, eliminate spurious correlations, enhance cross-domain generalization, model intervention consequences, and solve counterfactual queries. His work focuses on optimizing intervention sequences for desired outcomes under resource constraints, applied to material discovery, agriculture, mining, sports, manufacturing, bushfire prediction, healthcare, and education. Professor Shi's industry impact includes the NOBURN bushfire prediction app (released 2023 with 50+ media coverages), energy material discovery via AI catalysts, and smart manufacturing logistics solutions. His work with the Responsible AI Think Tank (2022-2024) and current AI Industry Forum panellist role (2024 onward) demonstrates active contribution to national and state AI ecosystem development. His scientific awards include: 1st place at Open Catalyst Challenge (NeurIPS AI for Science 2023) Winner of AUS/NZ Bushfire Data Quest 2020 Citizen Science Grant 2021 Finalist in SA Department of Energy and Mining Gawler Challenge 2020 (2k+ participants from 100+ countries), recognized for "The most innovative modelling" 2nd place in Explorer Challenge 2019 (1k+ entries from 62 countries) 1st place at SAIC Volkswagen Logistics Innovation Day 2019 Shi is eligible to supervise Masters and PhD students and has secured research funding including the Citizen Science Grant 2021. His industry collaborations span energy, agriculture, mining, and emergency management, translating theoretical causal AI into practical tools like NOBURN. He leads the Causal AI Group at the University of Adelaide and directs research teams at AIML, focusing on causal inference frameworks for distribution shift resilience and intervention optimization. Current projects emphasize bushfire prediction, material science applications, and AI ethics implementation through the AI Industry Forum.
Xiaotian Zheng is an Assistant Professor of Statistics at the University of Georgia. Previously, they were a Postdoctoral Research Fellow with the Australian Research Council Special Research Initiative Securing Antarctica's Environmental Future at the University of Wollongong, working under Professor Noel Cressie and Associate Professor Andrew Zammit-Mangion. They earned their Ph.D. in Statistical Science from the University of California, Santa Cruz, advised by Professors Athanasios Kottas and Bruno Sansó. Their research focuses on developing statistical and machine learning methods for analyzing complex, dependent data, particularly in ecological and environmental contexts. Key areas include spatial/spatio-temporal statistics, probabilistic downscaling, data integration, transfer learning, and statistical deep learning. Xiaotian's publications reflect their work on mixture transition distribution models, nearest-neighbor mixture models, and geostatistical frameworks for discrete-valued processes. These contributions emphasize Bayesian inference, computational efficiency, and real-world applications in environmental science and biodiversity modeling.
Dr. Jiaojiao Jiang is a Senior Lecturer in the School of Computer Science and Engineering at the University of New South Wales (UNSW). She holds a Ph.D. from Deakin University (Melbourne, Australia) and has published over 45 articles with 1,100+ citations. Her research focuses on AI-driven cybersecurity solutions, particularly misinformation detection and modeling information propagation dynamics. She is affiliated with UNSW's Sydney campus and can be contacted at jiaojiao.jiang@unsw.edu.au . Education: Ph.D., Deakin University, 2010s Research Interests: Artificial Intelligence applications in cybersecurity Misinformation detection and network analysis Machine learning for network security Data privacy in IoT systems Publications span topics like fake news detection via graph neural networks, multiplex network robustness, and cyber threat intelligence frameworks. Her work bridges theoretical network science with practical cybersecurity challenges.
Dr. Xuhui Fan is a Lecturer in Artificial Intelligence at the School of Computing, Macquarie University. He holds a PhD in Computer Science from the University of Technology Sydney (Australia) and a bachelor's degree in Mathematical Statistics from China. Prior to his current role, he worked as a project engineer at Data61 (formerly NICTA), a postdoc fellow at the University of New South Wales, and a lecturer at the University of Newcastle. His research focuses on Bayesian methods, federated learning, temporal point processes, and neural network architectures. He is affiliated with the Data Horizons Research Centre and the Frontier AI Research Centre at Macquarie University. Key research interests include developing interpretable AI models, advancing federated learning for privacy-sensitive applications, and applying Bayesian techniques to complex data analysis. His work bridges theoretical advancements in machine learning with practical applications in areas such as anomaly detection, generative models, and spatio-temporal data analysis. Dr. Fan’s publications span top-tier conferences like NeurIPS, ICML, and IJCAI, covering topics such as diffusion models, nonstationary processes, and scalable relational models. He has contributed to surveys on Bayesian federated learning and developed novel frameworks for dynamic customer segmentation and network sustainability. His research collaborations span institutions in Australia and internationally, reflecting his expertise in interdisciplinary AI applications. Current projects emphasize ethical AI practices, efficient uncertainty quantification, and scalable inference techniques for large-scale datasets.
Dr. Sahani Pathiraja is a Lecturer (tenure track assistant professor) at UNSW Sydney , specializing in Data Science . Her research bridges mathematical and statistical foundations with practical applications in environmental and biomedical sciences. Research Focus : Sequential Bayesian inference, Monte Carlo methods, stochastic analysis of non-linear filtering, uncertainty quantification, and real-time parameter estimation. Current Projects : Co-investigator in the ARC Industrial Transformation Training Centre: Data Analytics for Resources and Environment (DARE) and the Next Generation Graduate Program (NGGP) in Sports Data Science and AI . Research Supervision : Dr. Pathiraja supervises PhD students in areas including: Bayesian inference Stochastic differential equations Data assimilation Non-linear filtering Scientific Collaborations : Her work intersects with environmental science, biomedical applications, and machine learning. Projects include stochastic hydrology, SDEs, and operator learning for environmental systems. Contact Information : Email: s.pathiraja@unsw.edu.au Phone: +61 2 8065 0836 Office: Room 2070, Level 2, The Red Centre, UNSW Sydney
