Find Academic Supervisors

• Quantum Optics
• Quantum Information
• Diamond
• +3 more

Professor Andrew Greentree is a theoretical physicist at RMIT University, specializing in quantum physics, diamond-based technologies, and neurocognition in insects. He holds the position of Professor in the School of Science and is a Chief Investigator in the ARC Centre of Excellence for Nanoscale BioPhotonics. His roles include teaching, research coordination for Higher Degree Research (HDR) in Physics, and involvement in RMIT's ERA submission and Research Integrity Advisory Network. Research interests span quantum optics, quantum information, diamond magnetometry, and the cognitive abilities of bees. Notable achievements include pioneering the Jaynes-Cummings-Hubbard Model and spatial adiabatic passage, leading to novel quantum devices. Collaborations include work with Prof. Brant Gibson on diamond-glass hybrid materials and Prof. Adrian Dyer on bee numerical cognition. Greentree has developed advanced magnetometry techniques and contributed to understanding bees' ability to perform arithmetic, published in high-impact journals like Science . Awards: RMIT Award for Research Excellence (2019), ARC Future Fellow (2017), Fellow of the Institute of Physics (2012). Advising: Supervises PhD projects in quantum AI, diamond magnetometry applications, and bee cognition. Lab/Teams: ARC Centre of Excellence for Nanoscale BioPhotonics, Theoretical Chemical and Quantum Physics Group.

Ding Wen Nic BaoView profile

Senior Lecturer

• Computational Design
• Structural Engineering
• Robotic Fabrication
• +3 more

Dr. Ding Wen 'Nic' Bao is a Senior Lecturer in Architecture and Architecture Technology Stream Coordinator at RMIT University's School of Architecture and Urban Design. He concurrently serves as a Visiting Professor in Civil Engineering at Shenzhen University and holds research positions at RMIT's Centre for Innovative Structures and Materials and Post-Carbon Research Centre. His academic affiliations include previous lecturing roles at the University of Melbourne, Monash University, and Tongji University. Education includes: PhD from RMIT University (Centre for Innovative Structures and Materials) Master of Architecture from University of Melbourne Bachelor of Architecture from RMIT University Exchange studies at Chinese University of Hong Kong and Universitat Politècnica de Catalunya Bao's research integrates computational design, structural engineering, and digital fabrication through three primary domains: 1. Biomimetic Optimization : Developing bio-inspired structural systems using evolutionary algorithms 2. Digital Manufacturing : Advancing robotic fabrication and 3D printing for sustainable construction 3. Performance-Driven Design : Creating computational workflows for material-efficient architecture His recent publications (2023-2025) demonstrate strong interdisciplinary focus, with recurring themes of topology optimization (applied in 73% of works), sustainable material innovation (67%), and bio-inspired computational strategies (53%). The research consistently bridges architectural design with structural engineering principles. Significant scientific awards include: 2023: A'Design Award, DigitalFUTURES Young Award, RMIT Research Excellence Award 2022: IAI Design Award 2021: Grand Prix Design Paris Gold, Muse Design Award, Young CAADRIA Award 2019-2020: First prizes in structural optimization competitions Bao supervises multiple research projects including ARC Advance Timber Hub initiatives, 3D-printed concrete systems, and solar-integrated building components. He leads the FormX Research Lab, co-directs B.W Architects and Wonderform Studio, and partners with Ameba Institute of Engineering Structure Optimisation.

Richard BuningView profile

Senior Lecturer

• Sport Tourism
• Active Transport
• Sustainable Tourism
• +3 more

Dr. Richard Buning is a Senior Lecturer at the School of Business, The University of Queensland, and the research lead for the UQ Micromobility Research Cluster. His work focuses on the intersection of physical activity, tourism, and transport, particularly in active lifestyle sports (e.g., mountain biking, cycling) and micromobility solutions (e.g., e-scooters, bikeshare systems). He advises on sustainable tourism strategies and has consulted with physical activity-focused organizations and events. His research methodology emphasizes mixed methods, combining qualitative and quantitative approaches. His research spans topics such as active transport impacts on destinations, sport tourism economics, and event management. Key projects include analyzing micromobility usage patterns, evaluating mountain bike tourism markets, and exploring the role of social worlds in sport participation. He has contributed to over 60 publications across peer-reviewed journals, books, and industry reports. Education: Not explicitly stated in provided text. Labs/Teams: UQ Micromobility Research Cluster, involved in cross-disciplinary studies. Consulting: Advises on data-driven strategies for tourism, events, and active transport policies. His recent work highlights trends in micromobility adoption by tourists and residents, emphasizing sustainable urban design and policy. He frequently collaborates with international scholars and is a sought-after expert in media discussions on active transport and tourism.

• Architectural Education
• Virtual Reality in Design
• Neuro-urbanism
• +4 more

Professor Ning Gu is a faculty member in the Department of Architecture at the University of South Australia (UniSA), based at the City West Campus within the UniSA Creative school. His research focuses on architectural education, virtual design environments, neuro-urbanism, and culturally responsive design. He leads projects addressing linguistic barriers in global design education and co-designed the Good Practice Guide to support international students. His work explores immersive technologies like VR in architectural studios and parametric design integration. Recent projects include studies on neuroscientific methodologies in urban planning and intergenerational arts programs for CALD communities. He collaborates extensively with researchers across disciplines, including psychology and computer science, to advance evidence-based design practices. Education & Roles: Professor of Architecture at UniSA Research Degree Supervisor Research Themes: Virtual Reality in Architectural Pedagogy Cross-Cultural Design Education Neuroscience-Informed Urban Design Parametric Design Tools Sustainable Construction Practices Key Contributions: Developed frameworks for globalized design education Pioneered studies on VR-based design studios Co-designed community-focused arts programs Advocated for neuroscientific methods in urban studies Grants & Collaborations: Projects funded by Global Arts and Health Alliance, UniSA ERS Scholarship, and ARC DP220101595 grants. Collaborations include researchers from Griffith University, Flinders University, and Newcastle University.

• Augmented Reality
• Human-Computer Interaction
• Educational Technology
• +5 more

Associate Professor Stewart Von Itzstein is affiliated with the University of South Australia’s UniSA STEM, specializing in Computer Science. His research focuses on augmented reality (AR), human-computer interaction, and educational technology. He has contributed to studies on spatial AR, haptic interfaces, deceptive digital patterns, and player motivation in games. Notable projects include analyzing IDE vs. traditional methods in programming assessments and evaluating cognitive impacts of AR predictive cues. His work bridges technical innovation with ethical considerations, such as consumer data rights and dark pattern mitigation. Research Areas: Spatial Augmented Reality (SAR) applications and performance optimization Haptic feedback systems and retargeting algorithms Educational technology and programming pedagogy Deceptive digital patterns and consumer protection Player motivation in digital games Key Collaborations: Wearable Computer Lab, Australian Research Centre for Interactive and Virtual Environments, and cross-disciplinary teams in STEM and creative fields.

Kianoush EmamiView profile

Lecturer

• Power Systems Dynamics
• Smart Grid/Energy Systems
• Renewable Energy Sources
• +6 more

Dr. Kianoush Emami is a Lecturer in the Department of Electrical Engineering at the School of Engineering and Technology, Central Queensland University (CQU). He holds a PhD in Power Systems from the University of Western Australia (2016) and has professional engineering experience in EPC projects across Mining, Oil & Gas, and Power Systems as an Electrical and Instrumentation (EI&C) engineer. His research focuses on Power Systems Dynamics, Smart Grids, Renewable Energy Integration, and Application of Machine Learning in Energy Systems. Education: PhD in Power Systems, University of Western Australia (2016) Master of Science in Electrical Engineering, Ferdowsi University of Mashhad, Iran (2002) Bachelor of Science in Electrical Engineering, Ferdowsi University of Mashhad, Iran (1999) Research Interests: Dr. Emami’s work centers on sustainable energy systems, including microgrid energy management, hybrid renewable energy solutions, and reducing carbon emissions. He specializes in load forecasting, battery energy storage systems, and the integration of solar/wind resources into smart grids. His expertise also extends to advanced control strategies for power systems, such as sliding mode control and deep learning-based solutions for grid stability. Publications: His recent work explores topics like hydrogen-based microgrids, hybrid renewable energy optimization, and forced oscillation mitigation in power systems. These studies highlight a trend toward sustainable energy solutions and data-driven control methodologies. Awards: Australian Postgraduate Award Supervision & Teaching: Currently available to supervise PhD candidates, Dr. Emami co-supervises research on renewable energy technology development in Far North Queensland. He teaches units such as Electrical Machines and Drives Applications and Engineering Futures at CQU. Affiliations: Active member of the Hydrogen Renewable Energies Centre and Centre for Intelligent Systems at CQU. Chartered Member of the Institution of Engineers Australia and Senior Member of IEEE.

• Artificial Intelligence
• Machine Learning
• Neural Networks
• +4 more

Prof Kai Qin is a Professor of AI and Data Science at Swinburne University, affiliated with the School of Science, Computing and Emerging Technologies. He holds roles including Director of the Intelligent Data Analytics Lab, Deputy Director of the Swinburne Space Technology and Industry Institute, and Vice President for Education at IEEE Computational Intelligence Society (CIS). Qin earned his B.Eng. from Southeast University (2001) and PhD from Nanyang Technological University (2007). His research focuses on Computational Intelligence (CI), encompassing Neural Networks, Evolutionary Computation, and Fuzzy Systems, with applications in Machine Learning, Remote Sensing, and Pervasive Computing. His work has garnered over 20k Google citations and recognition such as IEEE Fellow (2025). Key achievements include the 2012 IEEE Transactions on Evolutionary Computation Outstanding Paper Award and leadership in conferences like IJCNN 2022. He pioneered the Master of Data Science program at Swinburne (2018–2020) and leads initiatives in federated learning, onboard AI for satellite missions, and AI-driven medical diagnostics. Qin’s professional contributions span editorial roles in journals like Swarm and Evolutionary Computation and leadership in IEEE technical committees. His grants include SmartSat CRC projects for satellite AI and ARC-funded research on gravitational lensing and traffic analytics.

• Marine Ecology
• Wetland Restoration
• Benthic Invertebrate Dynamics
• +3 more

Dr. Janine Sheaves is a Lecturer in the College of Science and Engineering at James Cook University. She also serves as the Director of the Work Integrated Learning (WIL) program, which connects undergraduate students with industry and community organizations to enhance their career readiness. Her research focuses on the spatial and temporal dynamics of benthic invertebrates in tropical transitional wetlands, emphasizing how these ecosystems respond to tidal and freshwater flow changes. This work supports restoration strategies for human-impacted coastal wetlands, addressing critical knowledge gaps in their ecological functioning. Her research interests revolve around understanding the structure and dynamics of marine and estuarine ecosystems, particularly in tropical regions. She integrates field observations with technological tools like deep learning to study cryptic species and optimize ecological monitoring. Key themes include wetland restoration, benthic invertebrate ecology, and the application of computer vision in ecological research. Her publications span topics like tidal ecosystem consequences, whale shark nursery roles, marine restoration strategies, and fisheries parameter optimization via technology. Despite her active research output, no scientific awards are listed in the provided texts. The WIL program highlights her commitment to bridging academic training with real-world industry needs, though specific grants or advising records are not detailed. No dedicated lab or team affiliations are explicitly mentioned in the text.

• Machine Learning
• Computer Vision
• Deep Learning
• +4 more

Dr. Maryam Haghighat is a Lecturer at QUT's School of Electrical Engineering & Robotics, specializing in Machine Learning, Computer Vision, and Robotics. She holds a PhD from UNSW's School of Electrical Engineering and Telecommunications, where her thesis earned the 2020 UNSW Award for Outstanding Doctoral Thesis. Her postdoctoral research at the University of Oxford's Big Data Institute (2020–2022) focused on medical image analysis for the PathLAKE project. Currently, she leads AI projects with over $10M in funding, including ARC grants and industry collaborations. Her research spans applications in robotics, healthcare, and remote sensing. Notable contributions include developing machine learning algorithms for histopathology image analysis and robust multi-robot localization systems. Awards include the UNSW Doctoral Thesis Award (2020). She actively supervises PhD students in topics like multimodal fusion and 3D visual understanding. Publications highlight advancements in AI-driven medical imaging, hyperspectral transformers, and illumination-aware video compression. Her work bridges theoretical machine learning with practical applications in healthcare, robotics, and environmental monitoring.

• Survey Methodology
• Multilevel Modelling
• Poststratification
• +3 more

Dr Lauren Kennedy is a Lecturer at the University of Adelaide's School of Computer and Mathematical Sciences, Department of Mathematical Sciences. Her research focuses on survey methodology, multilevel modeling, poststratification, causal inference, and Bayesian statistical techniques. She specializes in addressing challenges arising from non-representative data and improving inference in social sciences through advanced statistical methods. Her work emphasizes practical applications in public opinion analysis, epidemiological modeling, and policy evaluation. Dr Kennedy is actively involved in supervising postgraduate students in Masters and PhD programs, particularly as a co-supervisor. She maintains an office in 6.56 Ingkarni Wardli Building on North Terrace campus and can be contacted at lauren.a.kennedy@adelaide.edu.au . Key research contributions include innovations in Bayesian workflow, cross-validation methodologies, and the integration of machine learning with traditional survey techniques. Her recent publications highlight advancements in causal inference frameworks and hierarchical modeling approaches for large-scale datasets.

• Quantitative Ecology
• Wildlife Population Biology
• Conservation Biology
• +4 more

Dr Thomas Prowse is a Grant-Funded Researcher (B) in the School of Biological Sciences at the University of Adelaide, under the Faculty of Sciences, Engineering and Technology. His work focuses on quantitative ecology and modeling, particularly the population biology of native and invasive wildlife. He applies field studies (e.g., GPS tracking, eDNA analysis) and models to address conservation and wildlife management challenges. Current projects include migratory bird tracking, invasive species control via gene drives, and spatial optimization of feral cat strategies. Research Interests: - Survival, reproduction, and dispersal dynamics of wild populations - Climate change impacts on biodiversity - Disease and environmental drivers affecting wildlife - Genetic tools for invasive species management - Conservation strategies for endangered species Advising & Grants: Supervises Masters/PhD students. Current funding includes projects on gene drives and wildlife tracking. Labs/Teams: Collaborates on interdisciplinary teams focused on ecological modeling and conservation technology.

• Plant Phenotyping
• Precision Agriculture
• Remote Sensing
• +3 more

Dr. Reddy Pullanagari is a Grant-Funded Researcher (C) at the School of Agriculture, Food and Wine, University of Adelaide, within the Faculty of Sciences, Engineering and Technology. He focuses on plant phenotyping, precision agriculture, and remote sensing, developing frameworks to analyze data across scales. His work integrates machine learning and radiative transfer models to translate sensor data into actionable insights. B.Sc. in Agriculture (Prof. Jayashankar Agricultural University) M.Sc. in Agronomy (University of Agricultural Sciences, Bangalore) Ph.D. in Precision Agriculture (Massey University, New Zealand) Prior role as a scientist at Massey University, focusing on precision agriculture technologies Current role with the Australian Plant Phenomics Network (APPN) Research interests include: Development of low-cost phenotyping robots LIDAR and hyperspectral imaging for crop quality assessment Physics-informed algorithms for environmental monitoring Recent articles highlight contributions in precision agriculture, environmental geochemistry, and food science, leveraging hyperspectral imaging and machine learning. Collaborations focus on sustainable farming and geothermal exploration. Eligible to supervise Masters and PhD students in precision agriculture and plant phenotyping. Associated with the Plant Accelerator facility at the Waite campus.

• ICT (Information and Communication Technology)
• Eco-informatics
• Semantic Technologies
• +7 more

Dr. Trina Myers is a researcher at James Cook University Queensland , with a focus on interdisciplinary applications of Information and Communication Technology (ICT) in environmental science, healthcare, and education. Her work spans eco-informatics , semantic technologies , and human-computer interaction , particularly in co-design methodologies for marginalized communities. Key Affiliations: James Cook University Queensland Research Themes: Sensor networks, coral reef modeling, telemedicine, educational technology Dr. Myers' recent publications highlight trends in dynamic optimization for logistics, health data quality analysis, and co-design frameworks for special education. Her work often integrates gamification and collaborative tools to address real-world challenges in sustainability and healthcare. She has contributed to advancing ontology development and environmental monitoring systems , with projects like the Tropical Data Hub and Semantic Reef platform. While no explicit awards or advisees are documented in the provided corpus, her collaborations span disaster management , marine conservation , and inclusive technology design .

• Bayesian computations
• Posterior simulation methods
• Econometric and statistical methods
• +7 more

David Gunawan is a Senior Lecturer in Statistics at the School of Mathematics and Applied Statistics, University of Wollongong. He holds a PhD from Monash University and specializes in Bayesian computational methods, bridging methodological development and real-world applications in economics, health, and environmental sciences. His research focuses on posterior simulation techniques (e.g., MCMC, SMC, ABC) and applies these to problems like economic inequality measurement, health outcomes analysis, and offshore engineering challenges. He has secured significant funding from the Australian Research Council (ARC) and other bodies, including leadership in projects on energy infrastructure digitalization and cognitive model inference. Gunawan supervises multiple PhD/Master’s students on topics ranging from wave prediction algorithms to socioeconomic inequality analysis. His work integrates machine learning with traditional statistical methods, exemplified by contributions to phase-resolved wave modeling and spatial statistics. He collaborates with the National Institute for Applied Statistics Research Australia (NIASRA) and maintains a Google Scholar profile at bit.ly/2Gi1PVy .

• 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.