جست‌وجوی استاد و پژوهشگر

Kane Kohمشاهده پروفایل

مدرس

• Digital Marketing
• Marketing Technology
• Customer Experience
• +۱۳ مورد دیگر

Dr. Kane Koh is a Lecturer in the Department of Economics Finance & Marketing at RMIT University, Melbourne, Australia. He previously served as an Associate Lecturer at the University of Melbourne from July 2023 to February 2025. He holds a PhD from the University of Melbourne, specializing in marketing science methodologies, including analytics, machine learning, and econometric modeling. His research explores phenomena such as pet adoption, monetization strategies, live streaming services, and customer valuation, supported by over $26,000 in grants from the Marketing Science Institute. Education Completed PhD at The University of Melbourne, focusing on marketing science applied to structured and unstructured data. Research Interests Substantive: Digital Marketing, Marketing Technology, Customer Experience, Creator Economies, Content Monetization, Online Toxicity, Live Streaming, Charities, Crowdfunding, Sensory Experiences Methodological (Structured Data): Quantitative Marketing, Time Series Modelling (Panel Vector Autoregression) Methodological (Unstructured Data): Machine Learning (Video/Audio/Text), Text Analyses Awards Runner-up, AMS Review/Sheth Foundation - Doctoral Competition (2025) Winner, ANZMAC Conference Honorable Mention (2024) Winner, University of Melbourne Doctoral Best Paper Award (2024) Finalist, SMA Dissertation Competition (2024) Highly Recommended Runner-Up, ACSPRI Fellowship (2023) Winner, Melbourne Doctoral Poster Sessions (2023) Finalist, 3MT Competition (2023) Winner, RMSIG Doctoral Award (2023) Winner, AMS Review/DoCCA (2023) Teaching & Collaboration Coordinates MKTG1415 Digital Marketing at RMIT. Open to PhD supervision, industry partnerships, and data-driven collaborations. Expertise in leveraging analytics for digital marketing strategies.

Kh Tohidul Islamمشاهده پروفایل

پژوهشگر ارشد

• Medical Image Processing
• Artificial Intelligence
• Machine Learning
• +۴ مورد دیگر

Dr. Kh Tohidul Islam is a Research Fellow at Monash Biomedical Imaging, Monash University, Australia. He holds a PhD in Computer Vision and AI from the University of Melbourne (2022) and earlier degrees in Electrical & Electronic Engineering (2013, Prime University), MBA (2015, Lincoln University College), and Master of Computer Science (2017, University of Malaya). His research focuses on medical image processing, deep learning, and low-field MRI analysis. Pre-joining Monash, he held roles as a Research Assistant at the University of Malaya, Edith Cowan University, and the University of Melbourne. He has contributed to UN Sustainable Development Goals related to healthcare innovation and education through his work in AI-driven medical imaging solutions. Research interests include ultra-low-field MRI reconstruction, deep learning for medical image synthesis, and AI applications in neurological disease diagnosis. He has received awards such as the Melbourne Research Scholarship (2018) and RTP Scholarship (2020). Key contributions include developing diffusion models for MRI synthesis (e.g., MU-Diff, D2Diff) and improving CSF volume measurement accuracy using synthetic imaging. His work bridges portable MRI accessibility with high-field precision via AI techniques.

Nicolas Younes Cardenasمشاهده پروفایل

پژوهشگر ارشد

• Vegetation Phenology
• Remote Sensing
• Forest Flammability
• +۶ مورد دیگر

Nicolas Younes Cardenas is a Research Fellow at the Fenner School of Environment & Society, Australian National University. He specializes in remote sensing, vegetation phenology, and flammability analysis, with a focus on Australian eucalypt forests and global ecological systems. His work spans local-to-global scales, integrating hyperspectral sensors, machine learning, and biochemical analysis. Education: PhD in Earth and Environmental Sciences Master’s in Oil and Gas Exploration Master’s in Natural Resource Management Environmental Engineering Degree Research Focus: • OzFuel satellite mission development (world’s first for eucalypt flammability monitoring) • Vegetation phenology and environmental economics • Forest loss analysis in Upper Guinea and pygmy hippopotamus habitats • Intertidal ecosystem monitoring via geospatial technologies Publications & Projects: He has contributed to 15+ research outputs since 2016, including studies on fire behavior modeling, oil palm monitoring, and satellite system design. Active in the Hyper-NUE project (2023–2025) and a 2022 collaboration on carbon sequestration analysis.

• Big Data
• Social Network Mining
• Stream Processing
• +۱۲ مورد دیگر

Dr. Thanh Tam Nguyen is a Lecturer at the School of Information and Communication Technology, Griffith University, Gold Coast Campus. He holds a PhD in Computer Science from École Polytechnique Fédérale de Lausanne (EPFL), Switzerland, and is a Fellow of the Higher Education Academy (FHEA). His academic and research profile is centered on advancing Big Data and Smart technologies through efficient and trustworthy AI systems. His educational background includes a Doctor of Science and a Master of Science, both from EPFL. He is actively involved in high-impact research and has published over 65 papers in top-tier venues such as SIGMOD, VLDB, SIGIR, ICDE, IJCAI, VLDBJ, TKDE, and Pattern Recognition, with over 35 in CORE A* journals and conferences. His work has attracted more than 4,200 citations (h-index 35+). Dr. Nguyen's research focuses on Big Data Analytics, Social Network Mining, Stream Processing, Privacy-Preserving Machine Learning, Recommender Systems, Explainable AI, and Graph Neural Networks . He aims to bridge human insights with data models to ensure transparency and trust in data-driven decisions. His recent work explores misinformation management, machine unlearning, and federated learning, with applications in social networks, healthcare, and sustainable agriculture. The 15 most recent publications reflect a strong trend in privacy-preserving AI, explainability, federated learning, and graph-based modeling . Key themes include machine unlearning, adversarial robustness in recommender systems, heterogeneous graph representations, and trustworthy AI deployment. His work is frequently published in ACM and IEEE venues, indicating sustained excellence in computer science and AI research. Fellow of the Higher Education Academy (FHEA) Dr. Nguyen has secured over $1 million in research funding from government (DFAT, A4I, AKF), industry (CSIRO, Ubitech, Johnson & Johnson), and international bodies (NAFOSTED, ETRI, KARI). He serves as a guest editor for IEEE Journal of Biomedical and Health Informatics, area chair for ACL and EMNLP, and reviewer for top journals like TKDE, JVLDB, and CSUR. He supervises multiple PhD students and collaborates with leading international researchers such as Prof. Karl Aberer (EPFL), Prof. Björn Schuller (Imperial College), and Prof. Hongzhi Yin (UQ). He is a key contributor to the Responsible Big Data Lab at Griffith University and leads projects on misinformation management, AI safety, and sustainable agriculture through AI-powered traceability. His impact extends to policy and industry, particularly in cybersecurity and trustworthy AI adoption.

Felipe Werndl Trevizanمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Automated Planning and Scheduling
• Reasoning under Uncertainty
• +۹ مورد دیگر

Felipe Werndl Trevizan is a Senior Lecturer in the School of Computing at the Australian National University (ANU), where he conducts research at the intersection of Artificial Intelligence, Operations Research, and Machine Learning. His work focuses on automated planning and scheduling, reasoning under uncertainty, heuristic search, and machine learning for planning. He is actively involved in research projects related to uncertainty-centric pilot assistance systems and probabilistic planning. His research interests include: Automated Planning and Scheduling Probabilistic and Multi-Objective Planning Heuristic Search and Admissible Heuristics Deep Learning for Generalized Planning Constraint-Based and Risk-Aware Planning Learning Heuristics from Data His recent publications demonstrate a strong trend toward integrating machine learning with classical planning techniques, particularly in learning reliable, domain-independent heuristics using both deep and classical machine learning methods. He has developed novel algorithms for constrained and stochastic shortest path problems, multi-objective probabilistic planning, and generalized planning using neural networks. His scientific awards include: Best Paper Award at ICAPS 2016 Best Paper Award at ICAPS 2017 2016 Kikuchi-Karlaftis Best Paper Award from the Transport Research Board Felipe Werndl Trevizan is registered to supervise research students and has advised several successful projects in AI planning. He has secured research grants for projects such as 'Integrated Planning for Uncertainty-Centric Pilot Assistance Systems' and 'Chance-Constrained POMDP for Pilot Assistance Systems' at ANU. He is also organizing key community events including the 'Reliability In Planning and Learning (RIPL)' workshop and a tutorial on 'Learning for Planning' at ICAPS 2025. He is affiliated with the School of Computing at ANU and previously worked as a Senior Research Scientist at NICTA and Data61/CSIRO. His research is supported by active collaborations with leading researchers such as Sylvie Thiébaux, Hanna Kurniawati, and others.

Hasan H. Turanمشاهده پروفایل

مدرس ارشد

• Optimization Algorithms
• Simulation Modeling
• Machine Learning
• +۵ مورد دیگر

Dr. Hasan H. Turan is a Senior Lecturer and the Research Lead at the Capability Systems Centre, University of New South Wales (UNSW Canberra) within the School of Systems & Computing. Previously, he worked as a post-doctoral research fellow at Qatar University's Mechanical and Industrial Engineering Department from 2015 to 2017. His academic journey includes a Ph.D. in Industrial and Systems Engineering from Istanbul Technical University and a master's degree from North Carolina State University. Dr. Turan's educational background reflects his strong foundation in systems engineering and optimization: Ph.D. in Industrial and Systems Engineering, Istanbul Technical University Master's in Industrial and Systems Engineering, North Carolina State University Dr. Turan's research focuses on the development and application of data-driven optimization algorithms and simulation models across various domains including service and maintenance logistics, defense applications, energy capacity expansion, and telecommunications networks. His current work emphasizes integrating machine learning (particularly reinforcement learning), artificial intelligence, and computational intelligence techniques (such as genetic algorithms) with simulation models (discrete event and system dynamics) to address complex decision-making problems. His approach combines theoretical rigor with practical applications, particularly in defense contexts where robust decision support is critical. His recent publication trends reveal a strong focus on simulation-optimization techniques applied to defense resource planning, military fleet management, and logistics. There's a clear progression toward integrating AI and machine learning with traditional optimization methods, particularly in addressing uncertainty in defense and supply chain applications. His work spans multiple methodologies including differential evolution algorithms, deep reinforcement learning, and system dynamics approaches to tackle problems in maintenance planning, workforce allocation, and rare earth supply chains. Dr. Turan has demonstrated significant leadership in his field through professional activities: Guest editor for a special issue on simulation-based optimization in the Annals of Operations Research Editorial board member of the Journal of Business Analytics Convener of the 3rd and 4th IEEE Systems Modeling Conference Organizer of special sessions on simulation-based optimization at international conferences Committee member for leading international conferences on modeling, simulation, and management science As an educator, Dr. Turan has taught 15 different courses at bachelor's and master's levels to students from diverse backgrounds. He is actively involved in mentoring research students and seeks PhD candidates with strong programming skills and understanding of optimization and simulation modeling. He has secured competitive scholarships for high-achieving students in engineering, mathematics, or related sciences. His funded research projects have been supported by prestigious organizations including the Department of Defence, Qatar National Research Fund, The Scientific and Technological Research Council of Turkey, and Balassi Institute. Dr. Turan leads research activities at the Capability Systems Centre, focusing on developing advanced methodologies for complex decision-making problems. His team works on integrating AI with simulation-optimization approaches to address challenges in defense asset management, workforce planning, and logistics. The research environment he cultivates emphasizes both theoretical innovation and practical implementation to deliver actionable insights for defense and industry partners.

Negin Nazarianمشاهده پروفایل

مدرس ارشد

• Urban Climate
• Climate Modeling
• Environmental Sensing
• +۳ مورد دیگر

Dr. Negin Nazarian is a Scientia Senior Lecturer at UNSW Sydney's School of Built Environment, with affiliations at the ARC Centre of Excellence for Climate Extremes and City Futures Research Centre. She leads the Climate-Resilient Cities (CRC) research lab, focusing on urban climate interactions through climate modeling, environmental sensing, and IoT technologies. Her work addresses urban heat exposure, ventilation, and multiscale urban canopy parameterizations.

Marde Helbigمشاهده پروفایل

مدرس ارشد

• Dynamic Multi-Objective Optimization
• Computational Intelligence
• Evolutionary Algorithms
• +۴ مورد دیگر

Dr. Marde Helbig is a Senior Lecturer at Griffith University , School of Information and Communication Technology. Previously held positions include Deputy Director of the Griffith Sciences Partnership Office (2022-2025), Senior Lecturer at the University of Pretoria, and researcher at the Council for Scientific and Industrial Research (CSIR) in South Africa. Research focuses on dynamic multi-objective optimization using computational intelligence algorithms (evolutionary and swarm intelligence). Active in decision-making visualization and algorithm design for time-varying optimization problems. Recent grants include: AI-driven decision-making for smart infrastructure (CSIRO, 2025-2028) Ocean carbon capture optimization (CSIRO, 2025-2028) She is an Associate Editor for leading journals in Swarm and Evolutionary Computation and IEEE Transactions on Evolutionary Computation , with over 15 years of editorial and committee involvement in IEEE societies. Awards include the 2023 Griffith Sciences Pro Vice Chancellor’s Excellence Award for industry engagement and the 2019 TW Kambule-NSTF Emerging Researcher Award . Supervision includes 6 PhD and Master’s students across optimization and cybersecurity domains.

Rich Littleمشاهده پروفایل

پژوهشگر

• Marine Environmental Science
• Fisheries Management
• Ecological Modeling
• +۵ مورد دیگر

Dr. Rich Little is a Senior Research Scientist at CSIRO Environment in Hobart, Australia. His work focuses on integrating biophysical models with socioeconomic analyses to address challenges in marine resource management, fisheries economics, and climate adaptation. He has developed decision-support tools like ELFSim for coral reef fisheries and leads the South East Australian Marine Ecosystem Survey (SEA-MES). Additionally, he contributes to Marine Visual Technologies, which employs AI for sustainable fisheries monitoring. Certificate in Executive Management and Development (2018), Australian Graduate School of Management PhD (2000), University of Alberta MSc (1995), University of Western Ontario BSc (1989), University of Western Ontario Diploma (1993), University of Western Ontario Rich Little’s research spans marine population dynamics, tradeable permit markets, Bayesian Belief Networks for fishing behavior, and the economics of marine protected areas. His recent work explores parametric insurance for climate adaptation, deep learning applications in electronic monitoring, and resilience-building in mixed fisheries. Publications highlight interdisciplinary approaches to reconciling ecological, economic, and social objectives. His scientific accolades include the CSIRO Julius Career Development Award (2008-2011), the Early Career Research Excellence Award (2005), and an Honorary Associate position at the University of Tasmania (2007-2010). He has also contributed to transdisciplinary initiatives like the Theo Murphy Frontiers of Science meeting.

• Health Informatics
• Medication Safety
• Clinical Deterioration
• +۶ مورد دیگر

Tracey Bucknall is an Associate Professor and Visiting Professor at Macquarie University's Australian Institute of Health Innovation, Centre for Health Informatics. Her research focuses on medication safety, clinical deterioration, health informatics, and machine learning applications in healthcare. Active in systematic reviews and benchmarking studies Key collaborator in national aged care medication research Published extensively in high-impact journals like BMJ Quality and Safety and International Journal of Medical Informatics Her work examines medication systems, electronic medical records for ethnic minority patients, and AI-driven clinical deterioration prediction. Recent projects include designing informatics infrastructure for national aged care medication frameworks. Research outputs are available via open-access platforms, with significant citations and downloads across her publications.

• Quantum Physics
• Atomic Physics
• Quantum Sensing
• +۷ مورد دیگر

Dr. Patrick Everitt is a researcher specializing in quantum physics and atomic physics, with a focus on quantum sensing, Bose-Einstein condensates, and atom interferometry. His work bridges theoretical and applied physics, particularly in robust control systems for cold-atom sensors and inertial navigation technologies.

• Transport Engineering
• Public Transit Analysis
• Smart Card Data Applications
• +۳ مورد دیگر

Dr. Mahmoud Mesbah is a Senior Research Fellow at the School of Civil Engineering , University of Queensland , specializing in transport engineering and urban mobility . His work leverages smart card data , GIS , and machine learning to analyze passenger behavior , trip purpose inference , and transit network optimization . Research Themes : Public transit modeling, traffic violation analysis, travel time reliability, and data-driven urban mobility solutions. Email : mahmoud.mesbah@uq.edu.au

• Coal Reservoir Characterization
• Methane Diffusion in Coals
• Geophysical Logging
• +۳ مورد دیگر

Dr. Zhentao Li is a Postdoctoral Research Fellow at the School of Chemical Engineering , University of Queensland , focusing on coal reservoir characterization and unconventional gas recovery . His work integrates geophysical logging , machine learning inversion , and multiscale pore structure analysis to understand methane diffusion dynamics and reservoir productivity in coal seams. Current research explores natural fracture development , adsorption mechanisms , and 3D pore-fracture network modeling using advanced imaging techniques like FIB-SEM tomography and low-field NMR . Notable methodologies include: Hybrid geophysical logging approaches for gas evaluation Machine learning applications in logging inversion Nanoindentation for micromechanical heterogeneity Fractal analysis of pore structures 3D tomographic reconstruction of coal fractures His recent publications address challenges in coalbed methane recovery , gas diffusion pathways , and reservoir parameter estimation . Despite no formal awards listed, his collaborative work appears in journals like Fuel , Energy and Fuels , and Marine and Petroleum Geology . Affiliations include the University of Queensland and collaborations with institutions in China and Australia.

Robert Bootsمشاهده پروفایل

دانشیار

• Critical Care Medicine
• Circadian Rhythm Dysfunction
• Virtual Reality Therapy
• +۵ مورد دیگر

Associate Professor Robert Boots is a senior academic at The University of Queensland, affiliated with the Department of Thoracic Medicine at the Royal Brisbane and Women's Hospital (RBWH) and the Burns, Trauma and Critical Care Research Centre. He holds clinical roles as a Thoracic/Sleep Physician at RBWH and an intensivist at Bundaberg Base Hospital. Previously, he served as Deputy Director of ICU at RBWH for two decades and holds leadership positions in critical care education, including with the College of Intensive Care Medicine. Education: Bachelor of Medicine Surgery (Honours), The University of Queensland Master's (Coursework), University of Newcastle Doctor of Philosophy, University of Newcastle Fellow, Royal Australasian College of Physicians Research Focus: His work centers on circadian rhythm disruptions in critical illness recovery, virtual reality applications for patient therapy, and ICU innovations. Key projects include inhaled heparin for pneumonia prevention, telemedicine effectiveness, and sepsis rehabilitation. Collaborations with engineering teams explore AI-driven clinical decision support using multi-source data analytics. Publication Trends: Recent articles (2021–2025) emphasize ICU outcomes, circadian biology, and technology-driven interventions. Themes include predictive analytics for mortality/arrhythmias, virtual reality in oncology, microplastics contamination in diagnostics, and AI in evidence-based medicine. Multi-disciplinary approaches bridge critical care, data science, and engineering. Advising & Grants: Actively supervises postgraduate students in critical care research. Secured past funding for projects like inhaled heparin trials and telemedicine assessments. Current projects include optimizing ICU telemedicine at Bundaberg Base Hospital. Labs & Teams: Member of the Burns, Trauma and Critical Care Research Centre, fostering collaborations across engineering and clinical disciplines for innovations in ICU care.

Quan Nguyenمشاهده پروفایل

پژوهشگر ارشد

• Bioinformatics
• Computational Biology
• Biomedical Engineering
• +۷ مورد دیگر

Dr. Quan Nguyen is a Senior Research Fellow at the Institute for Molecular Bioscience and affiliate of the School of Biomedical Sciences at The University of Queensland . As a National Health and Medical Research Council leadership fellow (EL2) , he leads the Genomics and Machine Learning (GML) Lab , specializing in single-cell spatial omics and machine learning for cancer and neuroinflammatory disease research. Education : PhD in Bioengineering (UQ, 2013), Postdoctoral Bioinformatics (RIKEN, 2015) Expertise : Integrating single-cell spatiotemporal data with population genomics to reconstruct gene regulatory networks and cell-cell interactions in situ. His research focuses on cancer ecosystems (melanoma, brain cancer) and neuroinflammation , using spatial transcriptomics and deep learning to uncover cell-type specific biomarkers for precision medicine . Key contributions include improving histological cancer diagnosis and identifying novel regulators of cell differentiation. His 15 most recent publications (2024-2025) span spatial omics in colorectal cancer , HPV-associated cervical neoplasia , neuroinflammation in aging, and machine learning methods for multi-omics integration . These works emphasize single-cell resolution , tissue context , and clinical translation . Scientific Awards : Australian Research Council DECRA fellowship (2019-2021) CSIRO OCE Research Fellowship (2016) IMB Fellow (2018) Predictive modeling tools for digital pathology in GigaScience (prize-winning) Supervision : Actively mentoring 10+ PhD students on topics like spatial diagnostics , cellular heterogeneity , and multi-omics data fusion . His lab develops open-source software and databases for single-cell analysis , supported by US Congressionally Directed Medical Research Programs and National Health and Medical Research Council grants.