Find Academic Supervisors

• Artificial Intelligence
• Bayesian Networks
• Healthcare Informatics
• +3 more

Ann Nicholson is a Professor and Dean of the Faculty of Information Technology at Monash University. She has held various senior academic roles, including Deputy Dean (Research) from 2018–2020 and Associate Dean (Education) from 2014–2016. Her work focuses on Bayesian networks, AI applications in healthcare, environmental science, and education technology. She has contributed to numerous projects addressing complex societal challenges, such as pandemic modeling, medical decision support, and sustainable development goals. Her research interests include causal modeling, Bayesian network development, and AI-driven solutions for real-world problems. She has led collaborative initiatives like the MAGNET project studying neurodevelopmental disorders and contributed to environmental modeling of Port Phillip Bay. Nicholson has also pioneered tools like iOOBN for object-oriented Bayesian networks and BARD for structured group reasoning. Her publications span over three decades, with significant contributions to AI applications in medicine, ecology, and policy analysis. She has been involved in projects ranging from wind energy assessment in Mexico to fog forecasting at Melbourne Airport.

• Bayesian Statistics
• Financial Econometrics
• Computational Statistics
• +3 more

Wilson Chen is a Senior Lecturer at the University of Sydney. He holds a PhD in Financial Econometrics from the same institution. Prior roles include a post-doctoral fellowship at the University of Technology Sydney and an Assistant Professorship at the Institute of Statistical Mathematics in Japan. His research focuses on advancing computational Bayesian methods and statistical tools for financial time series analysis. Education: PhD in Financial Econometrics, University of Sydney Research Interests: Wilson develops efficient computational techniques for Bayesian inference, with applications to financial data analysis. His work emphasizes MCMC optimization, quantile function models, and the integration of machine learning with statistical methodologies. Recent efforts include improving sampling efficiency in Bayesian frameworks and exploring variational approaches for complex posterior distributions. Publications: His work spans themes in Bayesian computation, financial econometrics, and machine learning. Notable contributions include optimizing MCMC thinning, semiparametric GARCH models, and Stein-based sampling techniques for probabilistic inference. Awards: No scientific awards explicitly mentioned. Advising & Grants: Currently supervising three PhD students: Peiwen JIANG (Modelling Complex Posteriors in Bayesian Inference), Wen PENG (Bayesian Neural Networks for Volatility Dynamics), and Yuning ZHANG (Stochastic Loss Reserving). Grant details are not specified in the provided texts. Labs/Teams: No specific lab or collaborative team affiliations mentioned.

• Computer Vision
• 3D Reconstruction
• Neural Rendering
• +5 more

Dr. Miaomiao Liu is a Research Fellow at the School of Computing, The Australian National University. Her research focuses on computer vision, 3D reconstruction, and neural rendering, with applications in robotics, autonomous systems, and renewable energy forecasting. She leads multiple projects including Next-Generation Aviation Safety Net Air Traffic Management, Machine Vision Techniques for Solar Power Forecasting, and 3D Vision Geometric Optimization in Deep Learning. Her work integrates cutting-edge techniques such as neural radiance fields, depth estimation, and self-supervised learning to address challenges in dynamic scene reconstruction, motion forecasting, and image deblurring. She has pioneered methods for mining supervision signals in dynamic regions and developing language-driven deblurring networks. Key Projects: Aviation safety systems, solar irradiance prediction, and geometric optimization in deep learning Research Themes: 3D scene understanding, human motion prediction, and neural rendering Technical Expertise: Neural networks, multi-view stereo, and physics-based modeling Dr. Liu's research has been published in top-tier venues like CVPR and IEEE conferences, with over 2,450 citations. While not explicitly listed as part of a lab, her work demonstrates strong collaboration with industry partners such as CSIRO and aerospace stakeholders. She actively supervises research students in areas like 3D vision and energy systems.

Matt DuckhamView profile

Professor

• Geospatial AI
• Spatial Algorithms
• Geovisualisation
• +5 more

Matt Duckham is a Professor in Geospatial Sciences and Director of the Information in Society Enabling Impact Platform at RMIT University. He has held previous academic roles at the University of Melbourne as an Australian Research Council Future Fellow and at the University of Maine's National Center for Geographic Information and Analysis. His work bridges theoretical and applied GIScience, with a focus on AI integration and spatial knowledge infrastructure. Academic Leadership: Director, Information in Society EIP (2021–present); Acting Dean, STEMM Diversity and Inclusion (2020–2021) Research Themes: Geospatial AI, Qualitative Spatial Reasoning, Decentralized Geosensor Networks, Spatial Data Trusts Recent Publications demonstrate his commitment to advancing spatial computing through machine learning integration and explainable AI frameworks. His teaching resources include open-access textbook materials and practical exercises spanning relational spatial databases and data visualization. Professional Contributions include co-founding the Journal of Spatial Information Science and developing open educational resources under a Creative Commons Attribution license. His work addresses critical issues in data ethics , urban resilience , and emergency response systems .

• Machine Learning
• Artificial Intelligence
• Graph Neural Networks
• +2 more

Maoying Qiao is a Lecturer at Australian Catholic University's Peter Faber Business School, specializing in machine learning and artificial intelligence. Her research spans graph neural networks, computer vision, and probabilistic modeling, with applications in network analysis and 3D vision. Key publications focus on improving graph convolutional networks through negative sampling, adapting stochastic block models for power-law networks, and developing conditional graphical lasso methods for multi-label image classification. Marine science applications include automated catch detection systems for fisheries.

• Causation
• Artificial Intelligence
• Metaphysics
• +2 more

Professor Javen Shi is the Founding Director of the Causal AI Group at the University of Adelaide and a director at the Australian Institute for Machine Learning (AIML). His research spans causation, artificial intelligence, metaphysics, and mind theories, with global recognition as Google Scholar ranks him 4th in causation and 7th in probabilistic graphical models. Active contributor to AI ecosystem as a panellist for Responsible AI Think Tank (2022-2024) and AI Industry Forum (2024 onward) Research applications across material discovery, agriculture, mining, sports, manufacturing, bushfire prediction, health, and education Developed NOBURN bushfire prediction app (2023) with over 50 media coverages Research Focus : Causal AI methods that identify root causes, discover latent variables, and build immunity from spurious correlations. His work enables generalization across domains, models intervention consequences, answers counterfactual questions, and determines optimal intervention sequences for outcome optimization. Scientific Recognition : Open Catalyst Challenge 2023 (AI for Science) - 1st place AUS/NZ Bushfire Data Quest 2020 - Winner Gawler Challenge 2020 - Finalist (judged as 'most innovative modeling') Explorer Challenge 2019 - 2nd place SAIC Volkswagen Logistics Innovation Day 2019 - 1st place

Ian WoodView profile

Lecturer

• Statistics
• Machine Learning
• Bioinformatics
• +7 more

Dr. Ian Wood is a Lecturer in Statistics at the School of Mathematics and Physics within the Faculty of Science at The University of Queensland. He received his PhD from the same institution in 2004. His teaching responsibilities include instructing 4th-year advanced statistics, 1st-year pharmacy students, and postgraduate data science courses. Dr. Wood's research spans multiple interdisciplinary domains including: Statistical methodologies for classification and mixture models Machine learning algorithms and stochastic optimization techniques Bioinformatics applications in genomics and medical research Evolutionary computation and optimization frameworks His work frequently bridges theoretical statistics with practical applications in biomedicine and computational biology. Analysis of Dr. Wood's recent publications reveals three primary research trends: Advanced algorithm development for statistical modeling and optimization Applications in biomedical contexts including cancer research, tissue engineering, and pathogen genomics Methodological innovations in machine learning and computational statistics His research consistently demonstrates strong interdisciplinary collaboration across statistics, computer science, and life sciences. Dr. Wood maintains an active supervision portfolio, serving as principal advisor for PhD candidates working on: Evolutionary algorithms for variable-length problems MRI brain segmentation using Markov random fields Estimation of distribution algorithms He also serves as associate advisor for multiple doctoral and master's projects spanning statistics, optimization, and bioinformatics. Dr. Wood is affiliated with the Centre for Organic Photonics and Electronics and maintains collaborations across multiple research teams focused on statistical applications in health sciences and engineering.

• Robotics
• Autonomous Vehicles
• Machine Learning
• +4 more

Dr Charika De Alvis is a Research Fellow at the School of Computer Science , University of Sydney. Her work focuses on robotics and autonomous systems, integrating advanced machine learning and computer vision techniques. Research Interests : Robotics Autonomous Vehicles Machine Learning Computer Vision Sensor Fusion Data Science Her recent publications emphasize robust autonomy , scene segmentation using Lidar-constrained CRFs , sensor fusion for moving platforms, and uncertainty estimation in tracking systems. These studies highlight trends in autonomous vehicle perception , Lidar-camera integration , and probabilistic modeling . Grants : 2022: National Intelligence Postdoctoral Grant for "Extreme Class Imbalance Handling with Sample Characterized Margin Loss"

• High Dimensional Statistical Inference
• Machine Learning
• Data Science
• +11 more

Professor Bala Rajaratnam is a leading academic in Business Analytics at the University of Sydney Business School . His research spans high-dimensional statistical inference , machine learning , and data science , with applications in economics , finance , biomedical sciences , and environmental modeling . Key focus areas include graphical models , network analysis , and Bayesian methodology . His recent work examines partial correlation screening , covariance estimation , and climate field completion . Publications highlight interdisciplinary collaborations across climate science , financial engineering , and biomedical research . He received a 2019 Australian Research Council Discovery Project grant for Principled statistical methods for high dimensional correlation networks .

• Robotics
• Artificial Intelligence
• Task and Motion Planning
• +4 more

Rahul Shome is a tenure-track lecturer in the School of Computing at the Australian National University (ANU), specializing in Robotics & AI. His research focuses on planning algorithms for human-centric robotics applications, with expertise in task and motion planning, multi-robot systems, and collaborative automation. He holds a Ph.D. and M.S. from Rutgers University, advised by Prof. Kostas E. Bekris, and completed postdoctoral work at Rice University under Prof. Lydia E. Kavraki. Education: Ph.D. and M.S. in Robotics, Rutgers University (advisor: Prof. Kostas E. Bekris) Postdoctoral Research Associate, Rice University (advisor: Prof. Lydia E. Kavraki) Research Interests: Development of practical robotics solutions with theoretical guarantees Integration of AI techniques for real-world automation challenges Multi-robot coordination and manipulation in dynamic environments Human-centric robotics emphasizing privacy, ethics, and collaboration His publications span journals and conferences like IEEE Robotics and Automation Letters, with awards including the Best Paper Award at IEEE MRS. Shome actively serves on editorial and organizing boards for major robotics venues. Current research directions explore algorithmic foundations in stochastic decision-making, topology, and machine learning integration. Advising & Opportunities: Open PhD and research positions in robotics planning and collaborative automation Focus areas: AI-driven robotics, human-robot interaction, and perception systems

John OrmerodView profile

Associate Professor

• Statistics
• Bayesian Inference
• Data Mining
• +4 more

Associate Professor John Ormerod is affiliated with the Statistics Research Group at the School of Mathematics and Statistics , University of Sydney . His research focuses on advanced statistical methodologies, including Variational Bayes , Generalized Linear Mixed Models , Splines , and Missing Data analysis. Current research students: Rajan Shankar and Jackson Zhou Key research strengths: Precision and Digital Health , Data and Decisions , National Security His work spans computational statistics, bioinformatics, and applications in health and biological data. Recent publications emphasize scalable Bayesian inference, variable selection, and single-cell data analysis frameworks. Grants include multiple Australian Research Council (ARC) Discovery Projects (2021, 2017, 2012, 2010) supporting research in feature selection, network modeling, and computational efficiency. He has supervised projects on modern regularization techniques and fast expectation propagation , contributing to BMC Bioinformatics , Bioinformatics , and Journal of Computational and Graphical Statistics .

Peter StuckeyView profile

Professor

• Constraint Programming
• Discrete Optimization
• Programming Languages
• +3 more

Peter Stuckey is a Professor in the Faculty of Information Technology at Monash University and project leader at Data61 CSIRO laboratory. His pioneering research focuses on constraint programming and discrete optimization. He holds B.Sc and Ph.D degrees in Computer Science from Monash University. Research interests include discrete optimization, programming languages, constraint solving algorithms, bioinformatics, and constraint-based graphics. His work extends to applications in databases, timetabling, and system security, with industry collaborations including Oracle and Rio Tinto. Recent publications focus on optimization techniques for transport routing, explainable AI, multi-agent systems, and signal processing. Awards include the Google Australia Eureka Prize and Woodward Medal.

• AI for Law Enforcement
• Community Safety
• Digital Forensics
• +4 more

Campbell Wilson is an Associate Professor in the Department of Data Science & AI at Monash University, serving as founding co-director of the AiLECS Research Centre. His research bridges technical and socio-technical aspects of AI应用于 law enforcement and community safety, with a focus on digital forensics, machine learning, and ethical AI frameworks. He has held leadership roles including Deputy Head of the Caulfield School of IT and Associate Dean (International). Education: PhD in Computer Science (Monash University, 2003), Master of Computing (1996), and Bachelor of Science in Applied Mathematics (1993), all from Monash University. Research interests include AI applications for law enforcement, cybersecurity, bioinformatics, and information retrieval. He leads projects like 'Improving Legal Frameworks for CSAM Prosecutions' and 'Detecting Threats in Temporal Networks', collaborating with agencies like Victoria Police and the British Asian Trust. Teaching commitments include chief examining roles for units such as FIT2100 Operating Systems and FIT5166 Information Retrieval Systems. He actively supervises PhD students focusing on AI for law enforcement and community safety. Labs/Teams: AiLECS Research Centre focuses on AI-driven solutions for law enforcement challenges. Projects emphasize ethical AI deployment and forensic data analysis.

• Computer Vision
• Robotics
• Intelligent Automation
• +5 more

Professor Alireza Bab-Hadiashar is a Professor of Mechatronics at RMIT University's School of Engineering, leading the Intelligent Automation Research Group. He holds a PhD in Robotics from Monash University and has held academic roles at Swinburne University and the University of Copenhagen. His research focuses on computer vision, robotics, and intelligent automation, with industrial applications in manufacturing and healthcare. He leads the Master of Robotics and Mechatronics Engineering program and has extensive experience in interdisciplinary projects involving AI, biomedical imaging, and advanced manufacturing. Research interests span computer vision, AI-driven automation, and robotics with emphasis on safety-critical systems like autonomous vehicles and medical imaging. Notable projects include defect detection in additive manufacturing and domain generalization in medical imaging. He collaborates with industry partners like Ford Australia on lightweight vehicle design and quality control. His recent publications emphasize distributed tracking systems, robotic calibration, and domain adaptation techniques. He actively supervises research projects in automated inspection, computer vision for transportation safety, and cancer detection via CT imaging. Academic memberships include IEEE, FIEAust, and professional organizations in computer vision and robotics. Labs/Teams: Director of the Intelligent Automation Research Group at RMIT. Current initiatives include developing AI solutions for Industry 4.0 and smart manufacturing systems.

Branko RisticView profile

Research Fellow

• Information Sciences
• Information Fusion
• Statistical Signal Processing
• +7 more

Dr. Branko Ristic is a Principal Research Fellow at the School of Engineering, RMIT University. His research focuses on information fusion, statistical signal processing, target tracking, and robotics, with applications in defense and security. He leads projects on autonomous systems, sensor networks, and uncertainty modeling. Ristic advises on topics like decentralized multi-platform search, machine learning for tracking, and possibility theory-based methods. His work appears in top journals like IEEE Transactions and Entropy. He offers short courses in target tracking and sensor fusion. Research Interests: Information fusion, statistical estimation, Monte Carlo methods, and field robotics. His recent work emphasizes autonomous search algorithms and threat assessment systems, leveraging possibilistic frameworks and Bayesian networks. Articles span radar technology, IoT localization, and multi-agent systems. Supervision Projects: Includes decentralized search strategies, real-data maritime tracking, and threat valuation networks. Affiliations: RMIT University, School of Engineering (City Campus).