Find Academic Supervisors

• Emergency Nursing
• Public Health
• Clinical Leadership
• +2 more

Lisa Kuhn is an Associate Professor in the School of Nursing, Midwifery and Paramedicine at Nebraska Methodist College. Her research focuses on emergency nursing practices, public health responses to conflicts and disasters, cardiovascular care optimization, and occupational health in healthcare settings. She has led numerous studies on clinical protocols in emergency departments and conflict zones. Her research interests span emergency medicine, clinical leadership development, occupational safety for healthcare workers, and innovative approaches to nursing education. She investigates trauma care systems, disaster preparedness, and healthcare worker safety in high-risk environments. Recent publications demonstrate a focus on evidence-based emergency care protocols, conflict medicine competencies, and healthcare system improvements. Her work frequently addresses clinical decision-making in cardiac emergencies, occupational hazards in nursing, and innovative training methodologies. Professional contributions include development of clinical guidelines for emergency departments and conflict zones. She actively researches healthcare delivery optimization through technological innovations and protocol standardization. Her investigations into nursing competencies in armed conflict contexts have established frameworks for emergency response training and clinical practice in resource-constrained environments.

Pejman SharafiView profile

Professor

• Modular Construction
• Prefabrication
• Sustainable Construction
• +4 more

Professor Pejman Sharafi is a leading academic at Western Sydney University, specializing in modular and sustainable construction systems. His research focuses on prefabrication, industrialized building methods, and structural resilience. He holds a Doctor of Philosophy from the University of Wollongong and has contributed to over 80 peer-reviewed publications. His work aligns with UN Sustainable Development Goals, emphasizing environmental and economic sustainability. Education: Doctor of Philosophy, University of Wollongong Research Interests: Professor Sharafi’s expertise includes modular construction, smart building systems, and seismic-resistant design. He explores innovations in inter-modular connections, energy harvesting for infrastructure, and recycled materials for blast protection. Projects & Collaborations: Development of high-performance modular systems funded by Infratech Industries Robotics array facility for construction automation (with Monash University) Collaborations with industry partners like Alspec Aluminium Awards: Discovery Early Career Research Award (2019) Multidisciplinary Research Team of the Year (2023) Researcher of the Year (2023) Advising & Grants: Supervises HDR candidates and leads projects on net-zero digital platforms and industrialized sustainable construction. Active in funding initiatives from the Australian Research Council and industry partnerships. Labs & Teams: Engaged with cross-disciplinary teams focusing on modular building systems, smart construction technologies, and structural health monitoring.

Shahab AbdullaView profile

Associate Professor

• Artificial Intelligence
• Bioinformatics
• Control Systems
• +9 more

Dr. Shahab Abdulla is an Associate Professor at the University of Southern Queensland (UniSQ), affiliated with UniSQ College. His interdisciplinary research focuses on Artificial Intelligence, Bioinformatics, Control Systems, and Signal Processing. He holds a PhD from USQ and has over 25 publications in Q1 journals, primarily in Computer Science and Engineering domains. He leads the Advanced Data Analytics Modelling Research Simulation Group and supervises numerous PhD/Master’s students. Education: BSc in Mathematics from University of Technology (Baghdad, 1992) MSc in Mathematics from University of Technology (Baghdad, 1996) PhD in Engineering from University of Southern Queensland (2013) Research Interests: Dr. Abdulla specializes in AI models for healthcare, energy, and biomedical applications. His work includes deep learning, neural networks, and signal processing techniques. He collaborates globally with researchers in the Middle East, Europe, and Asia, delivering over 30 seminars. His research has been cited over 200 times in the last three years. Grants & Awards: While specific awards are not listed, his leadership has been recognized through institutional and professional acknowledgments. He has secured grants for projects like AI-driven coastal erosion modeling and quantum-resistant cryptography. Supervision: Currently supervising 5 PhD/Master’s students and has examined numerous theses. His supervision spans topics like AI in healthcare, cybersecurity, and sustainable agriculture. Labs & Teams: Leads the Advanced Data Analytics Modelling Research Group, focusing on interdisciplinary AI applications. Collaborates with industry partners on projects like smart solar trackers and flood risk prediction systems.

• Supply Chain Management
• Logistics Optimization
• Sustainability
• +3 more

Mahla Babagolzadeh is a Lecturer (Casual Employment) at the University of Southern Queensland's School of Business. Her research focuses on sustainable supply chain management, logistics optimization, and operations research, particularly addressing challenges in perishable goods distribution, carbon tax policy impacts, and regional development. Education: Bachelor of Industrial Engineering (BIndEng) from Sadjad University of Technology Master of Science (MSc) from Ferdowsi University of Mashhad Doctor of Philosophy (PhD) from the University of Southern Queensland Research Interests: Dr. Babagolzadeh explores sustainable logistics systems, cold chain management under uncertainty, and innovative supply chain models for perishable products. Her work integrates environmental economics with operational strategies, emphasizing carbon tax regulation and government subsidy frameworks to enhance regional economic resilience. Publications Trends: Her recent articles (2016–2024) highlight contributions to multimodal transportation networks, vehicle routing optimization, and disaster relief logistics. She frequently employs hybrid algorithms and stochastic modeling to address real-world complexities in supply chain planning and reliability estimation. Advising & Grants: No formal advisees or grant details are listed in the provided data. Labs/Teams: No specific labs or collaborative teams are mentioned in the profile.

Cheryl McCarthyView profile

Associate Professor

• Agricultural Engineering
• Machine Vision
• Robotics
• +5 more

Dr. Cheryl McCarthy is an Associate Professor in Mechatronic Engineering and Senior Research Fellow at the University of Southern Queensland, focusing on automation and machine vision technologies in agriculture. She has been a full-time researcher since completing her PhD in 2009, with expertise in machine vision, robotics, and precision agriculture. Education: BEng (2005) and PhD (2004) in Mechatronic Engineering from the University of Southern Queensland. Research interests include crop disease detection (e.g., wheat root rot using NIR spectroscopy and machine learning), automation in farming operations, and robotic systems for precision agriculture. Her work spans orchard mapping, UAV-based irrigation monitoring, and livestock welfare assessment via machine vision. She has led projects on weed detection, fruit load estimation, and robotic navigation in agricultural environments. Key contributions include developing the 'MangoYOLO' deep learning model for fruit detection and advancing non-destructive crop sensing technologies. Her research integrates robotics, computer vision, and environmental sensing to address challenges in sustainable agriculture. Current and completed supervisions include projects on machine learning applications in meat processing, precision agriculture, and robotic localization in orchards. Major grants include farm trials for chicken welfare monitoring (2023), precision weed detection systems (2016), and harvester loss assessment (2019). Labs/Teams: Works within the Centre for Agricultural Engineering, collaborating on projects like space agriculture sensing and crop disease diagnostics.

Khaled SalehView profile

Lecturer

• Artificial Intelligence
• Machine Learning
• Autonomous Systems
• +4 more

Dr. Khaled Saleh is a Lecturer in Computing and Information Technology at the University of Newcastle's School of Information and Physical Sciences. He holds a PhD in Computer Science from Deakin University (2019). Prior to his current role, he served as a Postdoc Research Fellow at the University of Technology Sydney (2020–2022) and a Research Fellow at Deakin University (2018–2019). Research Focus: AI, Machine Learning, Autonomous Systems, Computer Vision, and Intelligent Transportation Systems Grants: Secured over $683K in funding, including projects on traffic safety and AI in education Editorial Roles: Topic Editor at MDPI Sensors and reviewer for top journals/conferences (e.g., IEEE T-ITS, ICRA) Research interests span autonomous systems, traffic safety, and AI-driven solutions for transportation and education. He has published over 30 papers in top venues and actively contributes to grants and industry collaborations. Teaching: Coordinates courses like Programming Languages and Business Intelligence.

Frits de NijsView profile

Research Fellow

• Artificial Intelligence
• Renewable Energy Systems
• Reinforcement Learning
• +3 more

Frits de Nijs is a Research Fellow in the Department of Data Science & AI at Monash University. His work focuses on advancing artificial intelligence techniques for energy systems optimization, multi-agent decision-making, and sustainable infrastructure. He has contributed to projects such as the Human-in-the-loop Microgrid Project and RACE for 2030 CRC, addressing challenges in renewable energy integration and smart grid management. Key research areas include reinforcement learning applications for energy curtailment, HVAC control systems, and resource-constrained multi-agent planning. His projects often bridge AI methodologies with real-world energy challenges, aiming to enhance grid reliability, carbon efficiency, and fair resource allocation. Notable collaborations involve researchers in machine learning, sustainable energy, and building automation. He has actively participated in 6 major research projects since 2020, including initiatives on AI-ready buildings and probabilistic multi-agent learning environments. His publications span conferences like AAAI, NeurIPS, and IEEE Power & Energy Society events, emphasizing peer-reviewed contributions to energy-AI intersections.

Onkar JadhavView profile

Research Fellow

• Machine Learning
• Computational Science
• Applied Mathematics
• +5 more

Onkar Jadhav is a Research Fellow at the University of Western Australia's School of Earth and Oceans, specializing in developing machine learning frameworks for oceanography and computational science. His work focuses on statistical downscaling techniques for ocean temperature predictions and reduced-order modeling for financial risk analysis. Previously, he was a postdoctoral researcher at the University of Luxembourg, where he applied machine learning to wind engineering and computational fluid dynamics. Education: He holds a Doctorate in Applied Mathematics from the Technical University of Berlin (2018–2022), with a thesis on physics-assisted machine learning for high-dimensional partial differential equations. His academic background bridges mathematics, engineering, and computational science. Research Interests: Jadhav’s expertise spans machine learning, computational science, and applied mathematics, with applications in oceanography, wind engineering, and financial risk analysis. He emphasizes interdisciplinary approaches to solve complex, high-dimensional problems. Publications: His research includes advancements in model order reduction for parametric systems in finance, wind load assessments using machine learning, and thermoelectric generator design for biomedical implants. Labs/Teams: Affiliated with the Oceans Graduate School and previously with the University of Luxembourg’s wind engineering team.

• Medical Imaging Analysis
• Deep Learning
• Artificial Intelligence
• +5 more

Dr. Laith Alzubaidi is a postdoctoral researcher at the School of Mechanical, Medical and Process Engineering , Queensland University of Technology (QUT) , Australia. He earned his Ph.D. in 2022 in medical imaging analysis with deep learning from QUT and a master’s degree in computer science from the University of Missouri (2016), USA. His research interests span medical imaging analysis , deep learning , artificial intelligence , and computational mechanics . He has contributed to advancements in bone fracture detection , skin cancer classification , IoT security frameworks , and physics-informed neural networks , with a focus on data fusion , adversarial attack detection , and real-time diagnostic systems . Dr. Alzubaidi has published over 53 refereed papers, garnered more than 3,700 citations, and led two special issues in reputed journals. His work involves global collaborations and grants for deep learning in medical applications . He reviews for 53+ journals, including IEEE Transactions on Medical Imaging and Information Fusion . Scientific Awards : Scholarship during master’s degree (University of Missouri) QUT scholarship for Ph.D. His recent publications highlight trends in AI-driven medical imaging , secure IoT networks , and physics-informed computational models . He has also explored trustworthy AI , reinforcement learning in healthcare , and explainable machine learning frameworks .

• Computational Fluid Dynamics
• Microfluidics
• Lattice Boltzmann Methods
• +2 more

Professor Emilie Sauret holds an ARC Future Fellowship at QUT's School of Mechanical, Medical & Process Engineering. With a PhD in Turbulence Modelling from Sorbonne University, she develops computational methods for complex fluid systems. Research focus areas: Physics-informed neural networks for microfluidics Electrohydrodynamic control of fluid interfaces Biomedical applications of computational fluid dynamics Publications showcase interdisciplinary approaches to fluid dynamics, with recent advances in predictive modelling of lung deposition and microfluidic device optimization. Her work bridges fundamental physics with energy and biomedical applications. Leadership roles include council membership in the Australasian Fluid Mechanics Society. She has secured $8M in research funding and collaborates with MIT, NASA JPL, and industry partners.

• Epidemiology
• Mathematical Biology
• Infectious Disease Modeling
• +6 more

Roslyn I. Hickson is a Researcher at James Cook University, specializing in mathematical biology and epidemiology. Her work bridges computational methods with public health challenges, focusing on infectious diseases like malaria, dengue, and tuberculosis. Key collaborations with institutions such as the University of Melbourne, Burnet Institute, and Australian National University. Active in interdisciplinary research, including One Health and behavioral disease transmission models. Roslyn's research centers on modeling disease dynamics, integrating factors like population heterogeneity, stigma, and environmental variables. Her studies address zoonotic spillover risks, optimal intervention strategies, and reproducibility in epidemic modeling. Recent publications highlight trends in computational epidemiology, particularly for diseases with complex transmission cycles (e.g., P. vivax malaria, Japanese Encephalitis) and societal impacts (e.g., stigma-driven mental health trends). She employs machine learning and spatial modeling for pathogen prediction. Her contributions include frameworks for health worker allocation, tuberculosis control cost-effectiveness, and Wolbachia's role in dengue suppression, reflecting a broad application of mathematical models to global health security.

• Aquaculture Genomics
• Selective Breeding
• Genomic Selection
• +3 more

David B. Jones is a Professor at James Cook University's College of Science and Engineering, specializing in aquaculture genomics and genetic improvement of marine species. His research focuses on genomic selection, quantitative trait mapping, and biotechnological applications for commercially important aquaculture species including barramundi, pearl oysters, shrimp, and flounder. He maintains active collaborations with Dean Jerry, Kyall Zenger, and international research teams across Australia, South Korea, and Sri Lanka. His primary research interests involve developing genomic tools for selective breeding programs, with emphasis on growth traits, disease resistance, coloration phenotypes, and environmental adaptation. Jones employs advanced methodologies including GWAS, RAD-Seq, SNP array development, and machine learning for genomic prediction. His work bridges fundamental genomics with practical aquaculture applications, significantly contributing to genetic gain acceleration in breeding programs. Analysis of his recent publications (2023-2025) reveals a strategic expansion from foundational pearl oyster genomics toward multi-species applications, with increasing focus on climate resilience traits, viral disease resistance, and AI-driven phenotyping. His current work demonstrates strong industry relevance through optimized genomic prediction models and commercial trait development. While no specific awards are documented in the provided materials, his extensive publication record in high-impact journals reflects significant scholarly contributions. His research program appears supported by collaborative grants focused on genomic selection implementation and selective breeding optimization across multiple aquaculture species. Dr. Jones contributes to advanced breeding infrastructure through development of species-specific genomic resources including SNP panels, linkage maps, and prediction models. His work directly supports selective breeding programs for barramundi, olive flounder, pearl oysters, and black tiger shrimp, with particular emphasis on translating genomic research into practical aquaculture applications.

• Power Systems
• Renewable Energy
• Estimation Theory
• +3 more

Tyrone Fernando is a Professor at the University of Western Australia (UWA), Department of Electrical, Electronic and Computer Engineering (EECE) within the School of Engineering. He joined UWA in 1996 after earning his BE (Honours) in 1990 and PhD in 1996 from the University of Melbourne. His professional roles include Deputy Head of School (2009–2010) and editorial leadership for journals like IEEE Transactions on Circuits and Systems II and IEEE Access. He is a Senior Member of IEEE and former Chair of the IEEE Circuits and Systems Society’s Power and Energy Technical Committee. Education: Bachelor of Engineering with Honours (1990), University of Melbourne Doctor of Philosophy (1996), University of Melbourne Research Interests: His work focuses on power systems, renewable energy integration, and estimation theory applications. Key emphases include DC microgrid control, advanced battery technologies (e.g., vanadium redox, lithium-ion), model predictive control (MPC) strategies, and sensorless control methods for motor drives. He also investigates stability criteria for power systems with high penetration of power electronics and energy storage optimization. Grants & Projects: He leads grants such as: Seeding marine innovation in SW WA with a WEC deployment in Albany (Blue Economy CRC, 2021–2025) Mine Electrification (CRC for Future Battery Industry FBI, 2022–2024) Project Symphony (Western Power, 2021–2023) These initiatives address grid resilience, battery-electric vehicle infrastructure, and renewable energy integration challenges. Awards: Outstanding Engineer award, IEEE PES WA Chapter (2019) Collaborations: His research involves interdisciplinary teams and industry partners, including Western Power and the Department of Industry, Science and Resources (Australia). He contributes to projects like DC-link capacitance estimation and stability analysis of offshore wind farms.

Shaokoon ChengView profile

Associate Professor

• Biomechanics
• Aerosol Diagnostics
• Biomedical Devices
• +5 more

Shaokoon Cheng is an Associate Professor in Mechanical Engineering at Macquarie University, serving as Program Director for the Mechanical Engineering undergraduate and Master's programs. He pioneered the establishment of these programs and has led over 140 peer-reviewed publications, six patents, and significant research funding, including ARC and NHMRC grants. As Chief Technology Officer of ASKSCIENTIA, he translates research into medical technologies like aerosol diagnostics and biomedical devices. He currently supervises 9 HDR students and has guided 18 to completion, emphasizing mentorship in innovation and translation. His research focuses on biomechanics, aerosol systems, organ-on-chip technology, and thermal management. Key projects include drug delivery mechanics, organ-on-chip development, and advanced manufacturing. He holds equipment such as stereoscopic PIV, high-speed imaging systems, and optical coherence tomography. He is actively involved in professional societies like the Australian Rheological Society and Australian and New Zealand Society of Biomechanics. Education: PhD in Biomedical Engineering (UNSW), BEng (Mechanical Engineering) with First Class Honors (University of Sydney). Research Interests: Biomechanics of drug delivery, organ-on-chip devices, 3D printing for bio-applications, and aerosol flow characterization. His lab equipment includes advanced imaging and testing systems, supporting interdisciplinary projects in biomedical engineering and mechanical design. Awards: Australian Postgraduate Award (2003), Engineering Postgraduate Award (2003), International Grant (2008), Travel Awards (2009, 2011). Teaching: Focuses on improving student employability and entrepreneurship through innovative pedagogies. Projects include developing digital reality modules for fluid mechanics education.

• Paediatric Anaesthesia
• Neurodevelopmental Effects of Anaesthesia
• Data Science in Medicine
• +3 more

Associate Professor Justin Skowno holds a Conjoint academic position in the Discipline of Child and Adolescent Health at the University of Sydney and serves as a Senior Staff Specialist in the Department of Anaesthesia at The Children's Hospital at Westmead. He is the Director of Research in Anaesthesia at the hospital and a medical sustainability lead for the Sydney Children’s Hospitals Network. His expertise includes paediatric cardio-thoracic and oncological surgery anaesthesia, sustainability in medicine, and data science applications in healthcare. Education: MBChB, DA(SA), FCA(SA), FANZCA, PhD Roles: Chairperson of the Research Subcommittee of the Society for Paediatric Anaesthesia in New Zealand and Australia (2020–2023), current committee member His research focuses on three areas: (1) physiological monitoring under anaesthesia using optical/electrical methods, including near-infrared spectroscopy (NIRS); (2) short- and long-term effects of anaesthetics on neurodevelopment, particularly in infants; and (3) data science in medicine and anaesthesia, such as large-scale critical care data systems. He has led initiatives like the Big Data for Small People project, which integrates anaesthesia, paediatric intensive care, and neonatal intensive care data to improve clinical decision-making. Publications highlight his work on EEG monitoring, NIRS applications, sustainability in anaesthesia, and clinical trials evaluating drug combinations (e.g., TREX trial). He contributes to editorial boards (e.g., Pediatric Anesthesia ) and peer reviews for major journals like Anesthesiology . Teaching: Daily clinical teaching in theatre, small-group tutorials, and lectures at the University of Sydney’s ‘Neuromonitoring in Anaesthesia’ unit Projects: Includes the PHANTOM study (prehospital tissue oximetry), international collaborations on EEG metrics with Dr. Alexander Caicedo-Dorado (Belgium) and Dr. Ian Yuan (USA) He advocates for climate-friendly anaesthetic practices and has spearheaded efforts to quantify the carbon footprint of nitrous oxide use in paediatric anaesthesia.