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Andrew GunstoneView profile

Professor

• Reconciliation
• Truth-telling and Truth-listening
• Indigenous Politics and History
• +3 more

Professor Andrew Gunstone is the inaugural Associate Deputy Vice-Chancellor Reconciliation and Professor Indigenous Studies at Federation University, leading the National Centre for Reconciliation, Truth, and Justice established in 2023. His roles include advancing reconciliation through academic research, policy development, and community engagement. He holds a PhD from the University of Queensland and advanced degrees in public policy and indigenous studies from Deakin and University of South Australia. His research focuses on reconciliation frameworks, truth-telling mechanisms, and reparative justice. Key areas include Reconciliation Action Plans (RAPs), systemic racism analysis, and Indigenous political representation. He has secured over $8 million in grants, including a landmark 2023-2028 Canadian SSHRC project on truth and reconciliation. Gunstone's 170+ publications include nine books and foundational texts like Unfinished Business: The Australian Formal Reconciliation Process . He co-chairs Reconciliation Victoria and edits the Journal of Australian Indigenous Issues . His recent book Reflections on the Voice analyzes the 2023 Referendum and its implications for Australia's reconciliation journey.

Daniel SchmidtView profile

Senior Lecturer

• Bayesian Inference
• Information Theory
• Statistical Genomics
• +7 more

Daniel Schmidt is a Senior Lecturer in Data Science at Monash University's Faculty of Information Technology, specializing in Bayesian inference, information theory, and statistical genomics. He holds a PhD in Computer Science (2008) and a Bachelor of Digital Systems (Honours) from Monash University (2003). His research focuses on high-dimensional Bayesian regression, Minimum Message Length (MML) principles, and applying machine learning to medical risk prediction, particularly in breast cancer via mammography analysis. He leads projects on scalable time series forecasting, quantum computing applications, and epileptic seizure prediction. Teaching commitments include developing and lecturing in units like FIT2086 (Modelling for Data Analysis) and FIT3154 (Advanced Data Analysis). He collaborates with institutions like the University of Melbourne (Adjunct Senior Research Fellow) and has contributed to open-source tools like the BayesReg package and GitHub repositories for statistical methods. His work contributes to UN Sustainable Development Goals related to health and innovation. Key research projects include efficient time series classification (MONSTER repository), seizure forecasting using EEG data, and quantum computing applications. He has authored over 119 publications, with recent work addressing adversarial attacks on time series models and Bayesian shrinkage priors for regression. Notable collaborations involve international teams in cancer genomics, statistical epidemiology, and materials science. His GitHub contributions include tools for random correlation matrix generation and fast AR model estimation.

• Deep Learning
• Machine Learning for Biological and Medical Data
• Artificial Intelligence
• +4 more

Dr Benjamin Mashford is a Research Fellow at the John Curtin School of Medical Research, Australian National University (ANU), specializing in interdisciplinary applications of deep learning, machine learning, and nanoelectronics to biomedical challenges. His work bridges computational methods with immunology, infectious diseases, and advanced diagnostics. Affiliation: Division of Immunology and Infectious Diseases, ANU Research Group: The Enders Group - Models of Human Primary Immunodeficiencies Research Focus: Mashford's work spans: Deep learning for biological data analysis (flow cytometry, EEG, genomic sequences) Neuromorphic and low-power computing systems Quantum dot technology for optoelectronics Energy-efficient health-monitoring devices Medical signal processing and predictive analytics Publication Trends: Recent work emphasizes hyper-dimensional cytometry representations, automated phenotyping systems, and cross-domain applications of machine learning from immunology to mineral processing. He also explores hardware implementations for biomedical diagnostics, including neurostimulation and fall detection systems. Supervision: Registered as a supervisor for graduate research students.

• Mobile Robotics
• Computer Vision
• Machine Learning
• +7 more

Dr. Tobias Low is a Senior Lecturer in Mechanical Engineering (Mechatronics) at the School of Engineering, University of Southern Queensland. He holds a BEng and PhD from Queensland institutions. With a career spanning over 14 years at USQ, he specializes in Mobile Robotics, Computer Vision, and Mechatronics. His research integrates autonomous systems, tactile sensing, and robotic navigation in agricultural and industrial contexts. Key research areas include robotic obstacle avoidance, deep learning applications in vision systems, and sensor technologies for precision agriculture. He has published extensively on topics like tactile feedback in robotic meat processing, stereo vision for leaf segmentation, and autonomous robot navigation in livestock environments. His work often bridges mechanical engineering with interdisciplinary fields such as nanotechnology (e.g., biodegradable nanofiber materials for fertilizer delivery) and environmental monitoring. Notable projects include developing tyre strain sensing systems for slip detection and integrating WSNs with mobile robots for agricultural applications. Dr. Low’s contributions span theoretical advancements (e.g., transfer learning in crack detection) and applied engineering solutions, reflecting a commitment to practical robotics and sensor innovation.

Zifan LinView profile

Lecturer

• Image Enhancement
• Power Electronics
• Inverter Technology
• +3 more

Zifan Lin is a Lecturer and PhD student in the Multi-Discipline Doctor of Philosophy program at the School of Engineering, University of Western Australia. His research focuses on power electronics, underwater imaging, and control systems, with notable contributions to inverter technology, DC microgrid systems, and machine learning applications in vision systems. He actively collaborates on projects spanning Australia and other regions, addressing challenges in renewable energy integration and advanced image processing. His academic background includes multi-disciplinary training in engineering, complementing his teaching duties. Research interests emphasize practical solutions for energy systems (e.g., DC-DC converters, NPC inverters) and underwater image enhancement networks like TANet and ReX-Net. Recent work explores semantic feature refinement for mask detection and robust control schemes for electric motors using reinforcement learning. Publications highlight innovations in space vector modulation strategies, finite-set model predictive control, and agricultural drying technologies. While no specific awards are listed, his prolific output in top journals like IEEE Transactions and LWT-FOOD SCIENCE AND TECHNOLOGY reflects academic recognition. His advising and grant activities remain unspecified in available data.

Mark JessellView profile

Professor

• Tectonics
• Metallogenesis
• 3D Geological Modelling
• +5 more

Mark Jessell is a Professor and Western Australian Fellow at the School of Earth and Oceans, University of Western Australia. He leads the WAXI & SAXI Projects and co-leads the MinEx CRC's Automated 3D Geological Modelling Project with Dr. Mark Lindsay. His research focuses on tectonics, metallogenesis, and 3D geological modelling, particularly in the West African and Guyanese Cratons. He has developed open-source QGIS plugins for geological analysis and digital mapping, including Map2Loop and GEOL-QMAPS. Education: MSc (UK), PhD (USA), Postdoc (Melbourne), and positions at Monash University and IRD (France). Research Interests: Microstructure studies, geology-geophysics integration, and structural geophysics. Industrial Relevance: Projects funded by AMIRA, MinEx CRC, and ARC, with a focus on resource exploration and risk analysis. Awards: GSA Bruce Hobbs Medal, Western Australian Fellowship, and MinEx CRC Publication Prizes. Engagement: Co-founded Agate Project Ltd to enhance Earth science education in Africa. Recent work includes multi-disciplinary studies of Paleoproterozoic tectonics in West Africa and advancements in 3D modelling algorithms. His publications span geophysical data fusion, machine learning applications, and lithospheric mantle analysis.

• Optimization
• Signal Processing
• Machine Learning
• +3 more

Dr. Minh Dao is a Senior Lecturer in the Department of Mathematics at RMIT University's School of Science. His research focuses on optimization theory, algorithm design, and their applications in signal processing, machine learning, and wireless communications. He has contributed to advancements in distributed optimization, nonconvex programming, and federated learning frameworks. Key research interests include operator splitting methods, DC programming, and mathematical modeling for energy-efficient systems. He has published extensively in top-tier journals and conferences such as the European Journal of Operational Research and IEEE Transactions on Intelligent Transportation Systems. Dr. Dao supervises research projects on distributed optimization for federated learning, reboot sensing MIMO systems, and DC programming approaches for clustering problems. His work integrates theoretical analysis with practical applications in telecommunications and energy networks.

• Cryptography
• Machine Learning
• Database Systems
• +3 more

Michael Brand is an Adjunct Professor at RMIT University's School of Computing Technologies. His research focuses on cryptography, privacy-preserving technologies, machine learning, and algorithm design. Key areas include zero-knowledge proofs, secure financial systems like FinTracer, and computational theory. His work bridges theoretical foundations with practical applications in data security and real-time processing systems. Research interests span advanced cryptographic protocols, privacy-enhancing machine learning techniques, and distributed computing paradigms. Notable contributions include innovations in blockchain tracing mechanisms and foundational work in statistical estimation theory. He actively publishes in top-tier venues, addressing challenges in secure computation, network analysis, and database systems. Collaborations emphasize interdisciplinary solutions to modern computational and security challenges.

Sapan TiwariView profile

Research Fellow

• Transport Modelling
• Agent-Based Modelling
• Sustainable Transportation
• +6 more

Dr. Sapan Tiwari is a Research Fellow at the Centre for Urban Research, School of GUSS at RMIT University, Australia. He holds a Ph.D. in Transport Modelling from the University of Melbourne and a Master of Technology in Transportation Engineering from IIT Guwahati, India. His work focuses on sustainable urban mobility, agent-based modelling, and integrating shared autonomous fleets with public transport. Education: Ph.D. in Transport Modelling, University of Melbourne (Australia) M.Tech. in Transportation Engineering, IIT Guwahati (India) B.Tech. in Civil Engineering, University College of Engineering, RTU Kota (India) Research Interests: His expertise includes agent-based modelling, active mobility strategies, shared autonomous vehicles, and sustainable transport systems. He contributes to UN Sustainable Development Goal 11 (Sustainable Cities and Communities) through projects like cycling infrastructure impact analysis and public transport optimization. Recent work emphasizes rebalancing strategies for autonomous fleets and ride-hailing integration with public transit. Collaborations: Open to collaborative projects in transport modelling and urban sustainability. Previously held visiting researcher roles at IIT Roorkee (2025) and junior fellowships at IIT Kharagpur (2019). Labs/Teams: Active in the Centre for Urban Research at RMIT, focusing on innovative urban mobility solutions.

• Mechanical Engineering
• Biomedical Engineering
• Fluid-Structure Interaction
• +7 more

Dr. Mergen Ghayesh is a Senior Lecturer in the School of Electrical and Mechanical Engineering at the University of Adelaide, Australia. His research focuses on mechanical engineering, biomedical engineering, and advanced materials science. He specializes in fluid-structure interaction (FSI), vibration analysis, and nonlinear dynamics, with applications in cardiovascular biomechanics, robotics, and energy harvesting systems. His work bridges theoretical modeling, experimental validation, and real-world applications such as rehabilitation robotics and wave energy converters. Dr. Ghayesh holds editorial roles in prestigious journals like the ASME Journal of Vibration and Acoustics. His recent studies explore metamaterials, graphene-based structures, and the biomechanics of arterial systems, emphasizing both fundamental science and translational engineering solutions. He actively publishes on topics ranging from smart materials to clinical applications of biomechanics. His research portfolio reflects a commitment to interdisciplinary innovation in mechanical and biomedical engineering.

• Spatial Statistics
• Neural Networks
• Time Series Analysis
• +3 more

Dr. Pratik Nag is a Research Fellow at the National Institute of Applied Statistical Research Australia (NIASRA), University of Wollongong. He holds a PhD from King Abdullah University of Science and Technology (KAUST), Saudi Arabia (2020–2024). His research focuses on integrating advanced statistical methods with machine learning to address challenges in predictive modeling, spatial statistics, and uncertainty quantification. He collaborates with prominent figures like Prof. Noel Cressie, Prof. Andrew Zammit Mangion, and Prof. Sumeetpal Singh. His work emphasizes coupling neural networks with traditional statistical techniques to solve real-world problems in environmental and spatial domains. Dr. Nag’s expertise includes neural networks, spatial and time series modeling, and large-scale data analysis. He has contributed to developing methods like DeepKriging for spatial interpolation and probabilistic forecasting. His peer-reviewed contributions span journals such as , , and . He actively reviews manuscripts for top-tier journals in computational statistics and environmental data science. His current research addresses extreme-scale environmental modeling, nonstationary covariance estimation, and generative adversarial networks for creative applications. Dr. Nag is affiliated with the University of Wollongong’s School of Mathematics and Applied Statistics and collaborates on high-performance computing initiatives for geospatial analysis.

Steve KerrisonView profile

Senior Lecturer

• Cybersecurity
• Internet of Things (IoT)
• Public Key Infrastructure (PKI)
• +4 more

Dr. Steve Kerrison is a Senior Lecturer in Cybersecurity at James Cook University (Singapore Campus). He holds a PhD and MEng in Computer Science from the University of Bristol, and certifications including CISSP and CCSP. His expertise spans IoT cybersecurity, embedded systems, and energy-efficient computing. Education: PhD in Computer Science, University of Bristol (2010-2015) MEng in Computer Systems Engineering, University of Bristol (2005-2009) CISSP and CCSP certifications (ISC)² Research Interests: Focuses on IoT cybersecurity, PKI for IoT, energy-efficient computing, and Industry 4.0. Collaborations include EU-funded projects (ENTRA, ICT-Energy) and Singapore’s National Cybersecurity R&D Program. Teaching: Specializes in open-source tools and community-driven cybersecurity education. Formerly a Senior Research Associate at Bristol and CTO at MICROSEC, a Singapore-based IoT security startup.

Roland GoeckeView profile

Professor

• Affective Computing
• Computational Behaviour Analysis
• Social Signal Processing
• +6 more

Professor Roland Goecke is a leading academic in affective computing and head of the School of Systems & Computing at UNSW Canberra. He holds a PhD in Computer Science from the Australian National University (2004) and has extensive experience in roles such as Professor at the University of Canberra and Senior Research Scientist at Seeing Machines. His research focuses on algorithms for human-computer interaction, mental health applications, and multimodal signal processing. Key research interests include affective computing, computational behavior analysis, and AI-driven solutions for health and well-being. He has secured funding from ARC, NHMRC, and US NSF, and serves on editorial boards of IEEE Transactions on Affective Computing and Pattern Recognition . His work spans mental health, video/image analysis, and assistive robotics. Prof. Goecke is an experienced research supervisor, guiding students in areas like human-robot interaction and computer vision. He is a senior member of IEEE and actively contributes to the academic community through conferences and mentorship. Current affiliations include UNSW’s Canberra campus, where he leads interdisciplinary research initiatives. His lab focuses on translating computational methods into real-world applications for health monitoring and human performance analysis.

• Operating systems
• Distributed systems
• Computer Science
• +3 more

Professor Willy Zwaenepoel is a distinguished academic and Dean of the Faculty of Engineering at the University of Sydney. He holds Fellowships from ACM, IEEE, and ATSE. His research focuses on distributed systems, operating systems, and experimental computer science. Previously, he spent two decades at Rice University and nine years as Dean of EPFL's School of Computer and Communication Sciences before joining Sydney in 2018. Education: BS/MS, Ghent University (1979) MS/PhD, Stanford University (1980/1984) Research Interests: His work emphasizes distributed systems and operating systems, with contributions to key-value stores, transactional systems, and large-scale graph processing. Recent projects include optimizing geo-replicated systems and improving datacenter scheduling efficiency. Articles Trends: Recent publications address OS scheduling (Nest), distributed graph mining (Tesseract), and transactional systems' performance limits. He explores hardware-software co-design for multicore systems and energy-efficient data centers. Awards: Fellow of ACM (2021) Fellow of IEEE (2020) Fellow of ATSE (2019) Grants & Advising: Active grants include adaptive key-value store research (2021) and large-graph processing systems (2018). He advises PhD students and postdocs on distributed systems and storage challenges. Labs/Teams: Leads the Sydney systems research group focusing on scalable distributed systems and cloud infrastructure.

• Privacy-Preserving Machine Learning
• Reinforcement Learning
• Data Privacy and Security
• +5 more

Associate Professor Kee Siong Ng is affiliated with the School of Computing at the Australian National University (ANU). His research focuses on privacy-preserving technologies, reinforcement learning, distributed systems, and blockchain applications. He has contributed extensively to areas such as privacy-preserving machine learning, federated learning, entity resolution, and scalable database systems. His work emphasizes balancing computational efficiency with privacy guarantees in data-driven environments. Key research contributions include methodologies for secure data processing in federated learning frameworks, privacy-preserving reinforcement learning for population-level systems, and blockchain-based digital identity solutions. He has led projects like Integrated Graph Analytics and contributed to initiatives involving the Australian Medicare dataset. His research often intersects theoretical foundations with practical implementations, addressing challenges in scalability and real-world applicability. Ng has published over 24 peer-reviewed articles, with notable works appearing in venues like IEEE Transactions on Parallel and Distributed Systems and Transactions on Machine Learning Research . His articles frequently explore cutting-edge topics such as differential privacy, approximation algorithms, and multi-agent systems. Collaborations include industry partnerships and interdisciplinary efforts involving health informatics and financial intelligence. His projects include Integrated Graph Analytics (2018–2021): Focused on scalable graph-based data analysis. Translational Fellowship (2018–2022): Bridging theoretical research with practical applications. Research on Data Sets for Health and Pharmaceutical Schemes (2020): Analyzing Medicare and pharmaceutical data with privacy safeguards. Ng's work prioritizes ethical AI and privacy-by-design principles, with a focus on real-world deployment challenges in distributed and federated systems.