جست‌وجوی استاد و پژوهشگر

• Data Privacy
• Evolutionary Computing
• Recommendation Systems
• +۵ مورد دیگر

Dr. Jinli Cao is a full-time Associate Professor in the Department of Computer Science and Information Technology at La Trobe University. She holds a BSc from Hebei University, China, and a PhD from the University of Southern Queensland, Australia (1997). Her research focuses on evolutionary computing, data privacy, deep learning for vulnerability assessment, and decision support systems. She has published over 150 papers in top venues such as VLDB and IEEE Transactions series. Dr. Cao leads an ARC-funded project on software vulnerability risk discovery and has secured three ARC grants. She has supervised 11 PhD, 2 Master’s, and 57 Honours students, many of whom work in academia and industries like Oracle and Commonwealth Bank. Teaching contributions include developing courses in databases, data warehouses, and artificial intelligence. Research Interests: Privacy-preserving data publishing and optimization Evolutionary algorithms for dynamic data partitioning Deep learning applications in cybersecurity and healthcare Graph-based machine learning for access control and anomaly detection Decision support systems and top-k query processing Her recent articles explore cutting-edge topics like privacy-preserving spatial crowdsourcing tasks, graph neural networks for traffic prediction, and cybersecurity frameworks for vulnerability prioritization. Awards include competitive ARC grants totaling $450,000 (2023-2025). She actively serves as an Associate Editor for Health Information Science and Systems and has examined over 100 PhD theses across Australian universities. Teaching highlights: Developed 20+ courses including Database Management Systems, Data Warehousing, and Artificial Intelligence. Coordinates units like Decision Support Systems and Intermediate Programming in Java.

Andrew Luiمشاهده پروفایل

پژوهشگر

• Computational Intelligence
• Spatial-Temporal Data Modelling
• Evolutionary Computing
• +۳ مورد دیگر

Dr. Andrew Lui is a Researcher at the QUT Centre for Robotics, Queensland University of Technology. Prior to this, he spent 21 years as an academic and administrator at the Open University of Hong Kong (OUHK), where he received awards such as the OUHK Teaching Excellence Award and IBM Faculty Award. He holds a PhD in spatial data modelling (Australian National University) and a BSc (Hons) in computer science and physics (University of Sydney). His research focuses on computational intelligence, spatial-temporal data modelling, educational technology, and robotics applications. Notable areas include pedestrian trajectory prediction, traffic flow modelling, and automated grading systems. Lui is skilled in programming languages (Python, Java, etc.) and frameworks like Spring MVC, with contributions to educational technology and robotics. Recent work emphasizes urban complex analysis (e.g., pedestrian intent classification, weather-time-trajectory fusion networks) and education technology innovations (e.g., automated grading systems, mindfulness in academic contexts). His publications span 30+ years, addressing diverse topics from neural networks to nursing education. Scientific Awards: OUHK Teaching Excellence Award, IBM Faculty Award Advising: Coached award-winning student teams in competitions and programming contests Lui’s work bridges robotics, urban systems, and education, with a focus on practical applications like accident black spot prediction and immersive learning environments.

Simon Farrellمشاهده پروفایل

استاد

• Cognition
• Short-term memory
• Working memory
• +۳ مورد دیگر

Simon Farrell is a Professor in the School of Psychological Science at the University of Western Australia (UWA), where he also served as Head of School from 2020 to 2022. He holds a BSc (Hons) and PhD in Psychology from UWA, and completed postdoctoral research at Northwestern University with Roger Ratcliff. His research integrates experimental psychology and computational modeling to study memory, judgment, and decision-making. Key contributions include the Serial-Order-in-a-Box (SOB) model of serial order memory and investigations into decision-making dynamics under uncertainty, particularly involving agent interactions. He teaches Cognitive Neuroscience and contributes to advanced statistics and computational modeling courses. Research interests focus on serial order memory mechanisms, temporal context in episodic memory, and goal-directed decision-making. Current projects explore how humans encode/retrieve critical information, navigate competitive decision contexts, and integrate memory with reinforcement learning. His work also addresses social utility preferences, inequality aversion, and the role of working memory in goal pursuit. Notable publications include studies on automation reliability learning (2024), episodic memory sampling (2023), and cultural responsiveness in psychology education (2025). Awards include the 2024 UWA Excellence in Teaching Award and the 2009 Paul Bertelson Award for cognitive psychology contributions. Active grants involve transport modeling, decision architectures in tactical systems, and automation reliability research. His lab collaborates internationally on projects like climate change belief segmentation and body image perception. Teaching and research emphasize bridging cognitive science with real-world applications through interdisciplinary approaches.

• Ecology
• Curriculum and Pedagogy
• Electrical Engineering
• +۴ مورد دیگر

Dr. Jessica Rivera Villicana is a Research Fellow in Computational and Geospatial Data Science at RMIT University's School of Global, Urban and Social Studies (GUSS). Her work integrates interdisciplinary approaches across ecology, education technology, and biomedical engineering. She leads projects like liveability scorecards analyzing urban inequities in Australian local government areas (LGAs), using geospatial and machine learning methodologies. Her research also focuses on EEG-based seizure detection, contributing to both technical advancements and clinical professional insights. Collaborating with global teams, she explores AI applications in healthcare (e.g., caregiver support systems) and cybersecurity (e.g., MLGuard framework). Key research areas include: Urban policy analysis through geospatial data modeling Machine learning interpretability in medical diagnostics Embodied conversational agents for caregiving AI safety and security in critical systems Behavioral modeling in interactive narratives Her recent work highlights include: 2025 Burwood & Canada Bay LGA liveability reports 2023 seizure detection survey with medical professionals MLGuard cybersecurity tool for ML models Dr. Villicana holds an ORCID identifier (0000-0003-1955-3310) and is based at RMIT's City Campus in Melbourne, Australia.

Bikesh Raj Upretiمشاهده پروفایل

مدرس

• Text Mining
• Machine Learning
• Behavioral Analytics
• +۱ مورد دیگر

Dr. Bikesh Raj Upreti is Lecturer in Business Information Systems at University of Queensland. His research applies machine learning and text-mining methods to interdisciplinary phenomena including marketing, finance, and political discourses. Developed applications for financial news analytics, political bot detection, and tourism impact analysis using large-scale data analytics. Recognized with best paper awards at international conferences.

• Machine Learning
• Computer Vision
• Deep Learning
• +۴ مورد دیگر

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
• +۳ مورد دیگر

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.

• Spatial Statistics
• Neural Networks
• Time Series Analysis
• +۳ مورد دیگر

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.

• Environmental Statistics
• Spatio-Temporal Modeling
• Bayesian Hierarchical Models
• +۳ مورد دیگر

Michael Bertolacci is a Senior Research Fellow at the School of Mathematics and Applied Statistics, University of Wollongong. He holds a PhD from the University of Western Australia (2016–2020). His research focuses on large-scale spatio-temporal problems in environmental statistics, including hierarchical Bayesian models for rainfall analysis, nonstationary time series methods, and flux inversion for trace gases. Current work emphasizes offshore engineering applications. He has secured funding from the University of Wollongong and NASA, collaborating on projects like WOMBAT v2.0, a Bayesian flux-inversion framework. His GitHub repositories reflect computational contributions to statistical methods and geospatial modeling.

Shandy Liمشاهده پروفایل

استاد

• Planetary and Environmental Health
• Child Health
• Public Health and Epidemiology
• +۱ مورد دیگر

Professor Shandy Li works at the School of Public Health and Preventive Medicine at Monash University, where she joined in March 2017. She currently serves as the Deputy Head of the Climate, Air Quality Research (CARE) Unit and the Group Leader of Children's Environmental Health Research, leading an interdisciplinary research team focused on human health in the context of global environmental change. Her work spans planetary health, environmental epidemiology, and child health, with significant contributions to understanding the impacts of air pollution, climate change, and extreme weather events on human health. Professor Li holds a PhD in Public Health (awarded in 2014) from The University of Queensland, a Master of Medical Sciences (awarded in 2010) from Peking University, and a Medical Degree (awarded in 2006) from Capital Medical University in China. Following her PhD, she completed a two-year postdoctoral training supported by a NHMRC Centre of Research Excellence at The University of Sydney. Her research interests focus on four primary areas: Planetary and Environmental Health (including Environmental Epidemiology, Air Pollution, Climate Change, and Exposure and Health Risk Assessment); Child Health (including Perinatal Health, Birth Outcomes, and Children's Respiratory Health); Public Health and Epidemiology (including Methodological Design and Global Health); and Biostatistics (including Quantitative Analysis and Spatiotemporal Modelling). Her recent publications analyze wildfire-related PM2.5 exposure, heat and humidity events, flood health impacts, and urban greenness effects, demonstrating her focus on climate-health interactions with global implications across multiple countries and territories. Dean's Award for Excellence in Research - Team Award (2024) FMNHS Early Career Researcher Fellows Publication Prize for Non-Laboratory Based Research (2021) SPHPM Early Career Excellence Award (2019) As a NHMRC Emerging Leadership (Level 2) Fellow, Professor Li has successfully secured competitive grants and currently leads multiple active projects including Real-Time Prediction of CVD Healthcare Utilization Driven by Environmental Stressors and Developing a Global Air Quality Alert System. She serves as Academic Editor of PLoS Climate and Associate Editor of BMC Public Health, and regularly reviews for prestigious journals and funding bodies. Professor Li leads the Children's Environmental Health Research group, maintaining extensive international collaborations that produce high-impact evidence informing healthcare policy. Her work has been adopted by governmental authorities to develop guidelines for preventing environmental health impacts, and she is currently accepting PhD students whose research aligns with UN Sustainable Development Goals in planetary health and environmental sustainability.

Patrick Laubمشاهده پروفایل

مدرس ارشد

• Actuarial Science
• Data Science
• Artificial Intelligence
• +۳ مورد دیگر

Patrick Laub is a Senior Lecturer at the UNSW School of Risk and Actuarial Studies, where he teaches courses in artificial intelligence and machine learning with a focus on risk and insurance applications. His academic work bridges the gap between advanced computational methods and practical actuarial problems. Patrick holds a joint PhD in computational applied probability completed between the University of Queensland and Aarhus University. He also possesses degrees in software engineering and mathematics, providing him with a strong interdisciplinary foundation for his research. His research focuses on computationally challenging problems in actuarial data science, with particular emphasis on natural catastrophe modeling and artificial intelligence applications. Key areas of investigation include Hawkes processes for modeling contagion in insurance claims, Approximate Bayesian Computation for fitting complex insurance loss models, and Empirical Dynamic Modeling for analyzing complex temporal dependencies. His work addresses critical challenges in risk assessment, particularly for extreme events where traditional statistical methods may be inadequate. Analysis of Patrick's recent publications reveals a strong trend toward integrating advanced statistical methodologies with practical actuarial applications. His work spans from theoretical developments in point processes and Bayesian inference to practical implementations in computational environments. A notable theme is the application of machine learning techniques to traditional actuarial problems, particularly in the areas of catastrophe modeling and risk prediction. Patrick has developed and taught innovative courses since 2022, including 'Artificial Intelligence & Deep Learning and their Applications to Risk and Insurance' (ACTL3143 and ACTL5111) and 'Statistical Machine Learning for Risk and Actuarial Applications' (ACTL5110). These courses reflect his commitment to preparing students for the evolving landscape of data-driven risk management in the insurance industry. His research is supported by UNSW's strong infrastructure for computational research, though specific lab affiliations are not explicitly mentioned in the available information. Patrick maintains an active research program with numerous publications across statistics, actuarial science, and computational methods.

Yan Zhuمشاهده پروفایل

پژوهشگر ارشد

• Photovoltaic systems
• Solar cell technology
• Semiconductor characterization
• +۳ مورد دیگر

Dr. Yan Zhu is a Research Fellow at the University of New South Wales (UNSW), affiliated with the School of Photovoltaic and Renewable Energy Engineering. She is currently part of the ACDC research team, focusing on advanced characterization techniques for photovoltaic materials and devices. Dr. Zhu holds a PhD in Photovoltaic Engineering from UNSW, complemented by international education including a Master's from École Polytechnique (France) and a Bachelor's from Shanghai Jiao Tong University. Her research specializes in photovoltaic systems and semiconductor characterization, with expertise in: Development of novel characterization methods for solar materials Defect analysis in silicon and semiconductors using photoluminescence Performance optimization of monocrystalline and multijunction solar cells Advanced metrology for photovoltaic device quality improvement Her publication portfolio demonstrates strong focus on photovoltaic innovation (60% of recent works), complemented by interdisciplinary contributions in computer vision applications for biometrics (25%) and medical imaging (15%). The research shows consistent emphasis on measurement accuracy, material reliability, and efficiency enhancement in renewable energy systems. Awards and Honors: Dean's Award for Outstanding PhD Theses (UNSW) Best Student Award - 7th World Conference on Photovoltaic Energy Conversion Steve Robinson Memorial Prize (UNSW) Dr. Zhu leads research within the ACDC photovoltaics characterization laboratory at UNSW, collaborating on projects developing next-generation solar cell diagnostic techniques. Her work bridges fundamental semiconductor research and industrial applications for renewable energy solutions.

• Ecology
• Botany
• Climate Change Biology
• +۷ مورد دیگر

Hilary Rose Dawson is a Postdoctoral Research Fellow at the Australian National University's Division of Ecology and Evolution. Her research focuses on how abiotic disturbances like fire and drought impact plant-soil-fungi interactions across temporal and spatial scales. She specializes in both aboveground plant traits/carbon fluxes and belowground soil carbon/mycorrhizal networks. Education: PhD in Biology (University of Oregon, 2024) Bachelor of Human Ecology (College of the Atlantic, 2018) Research emphasizes: Climate change impacts on plant functional diversity Soil biogeochemical processes under environmental stress Truffle ecology using innovative methods like truffle dog surveys Her recent work combines field botany with remote sensing technologies (Sentinel-2/Landsat) for ecological mapping. She serves as Reviewing Editor for Applications in Plant Sciences (2024-2026).

James Klarevas-Irbyمشاهده پروفایل

پژوهشگر

• Animal Behavior
• Movement Ecology
• Collective Behavior
• +۵ مورد دیگر

James Klarevas-Irby is a researcher affiliated with the Division of Ecology and Evolution at the Australian National University (ANU). He holds a Doctor of Natural Sciences (Dr. rer. nat.) from the University of Konstanz (2022). His work focuses on understanding animal behavior, movement ecology, and social dynamics in animal societies. Dr. Klarevas-Irby supervises research students and has contributed to studies on guineafowl, songbirds, and other species, examining topics such as collective movement, energetic efficiency, and social strategies. Key research interests include: Collective behavior and group dynamics in animals Energetic costs of movement and dispersal Social interactions and network structures in animal societies Applications of GPS tracking and machine learning in ecological studies His publications from 2018 to 2025 explore themes like the impact of group size on thermoregulation, the role of habitat selection in reducing energetic costs, and cryptic dialects in mate choice. He collaborates with institutions globally and actively publishes in journals such as Proceedings of the Royal Society B and Ecology Letters . No scientific awards are explicitly mentioned in the provided texts. His research methodologies include experimental fieldwork, automated tracking systems, and computational modeling to analyze animal behavior at individual and societal levels.

Vidhyasaharan Sethuمشاهده پروفایل

دانشیار

• Speech Signal Processing
• Machine Learning
• Emotion Recognition
• +۳ مورد دیگر

Vidhyasaharan Sethu is an Associate Professor at the School of Electrical Engineering and Telecommunications, UNSW Sydney. His educational background includes a PhD from UNSW, a Master of Engineering Science in Signal Processing from UNSW, and a Bachelor of Engineering in Electronics and Communication Engineering from Anna University. His research focuses on speech signal processing with emphasis on machine learning applications. Primary interests include: Speech-based emotion and mental state recognition systems Affective computing and voice biometrics Integration of machine learning with signal processing Uncertainty quantification in speech systems Adaptive audio front-ends and acoustic modeling Recent publications (2021-2025) demonstrate strong focus on: Ambiguity-aware emotion recognition using advanced ML techniques Speaker verification and biometric security systems Novel acoustic modeling frameworks Applications of large language models in speech processing Cross-disciplinary work in materials science via ML approaches Research Funding: ARC Discovery Projects (2020, 2019) ARC LIEF Grant (2019) UNSW Research Infrastructure grants (2018-2019) Huawei Innovation Research Program (2018) ARC Linkage (2017) NICTA International Postgraduate Award (2006-2009) Teaching includes courses in: Data Science for Electrical Engineers (ELEC9741) Speech Processing (ELEC9723) Digital Signal Processing (ELEC3104) Electrical Systems Design (ELEC2117)