جست‌وجوی استاد و پژوهشگر

• Data Mining
• Knowledge Engineering
• Artificial Intelligence
• +۷ مورد دیگر

Professor Yuefeng Li is a leading academic in Knowledge & Data Engineering, Artificial Intelligence, and Information Systems at Queensland University of Technology (QUT). He holds a PhD from Deakin University and has been affiliated with QUT's Faculty of Science and School of Computer Science since 2007. His research focuses on data mining, machine learning, natural language processing, and web intelligence, with 250+ refereed papers and 8,100+ citations (h-index=46). He has secured 5 ARC Discovery Grants, 1 ARC Linkage Grant, and led 6 CRC projects. His work bridges theoretical advancements and real-world applications in domains like healthcare, transportation, and geotechnical engineering. Research interests include: Data Mining & Knowledge Discovery, Machine Learning Algorithms, Natural Language Processing (NLP), Web Search & Information Retrieval, Deep Learning, and Knowledge Representation. He has pioneered methods like Semantic-LDA for topic modeling and adaptive multi-kernel learning for blast prediction. Key achievements include 10 Best Paper Awards, 14 articles with >100 citations, and editorial roles (e.g., Editor-in-Chief of Web Intelligence). He has supervised over 20 PhD/Masters students and currently oversees research training as Academic Lead HDR. His current projects include 5G/IoT ontologies, AI-driven medical diagnostics, and deep learning for imaging modalities. Teaching includes courses on search engine technology, machine learning for NLP, and data analytics. He actively participates in international conferences as a PC chair and conference organizer.

• Animal Science
• Ruminant Physiology
• Animal Nutrition
• +۱۱ مورد دیگر

Professor Michael Rose is a leading animal scientist and currently serves as Professor of Animal Science and Director of the Tasmanian Institute of Agriculture (TIA) at the University of Tasmania, Australia. He has held this position since October 2019, leading research and innovation in sustainable agriculture. Prior to this, he was a Reader in Animal Science at Aberystwyth University, UK, from 2000 to 2019, and a Research Officer at the National Institute of Grassland and Livestock Research in Japan from 1993 to 2000. His educational background includes a PhD from the University of Newcastle upon Tyne (UK), awarded in 1993, focusing on bovine somatotropin and milk yield response in dairy cows. Michael Rose's research is centered on ruminant health, nutrition, physiology, and welfare. Key areas include metabolic diseases such as hyperketonemia and mastitis, lameness, endocrinology, animal behavior, and the application of smart farming technologies. He integrates molecular biology, microbiome analysis, and machine learning to develop innovative solutions for livestock management. His work spans dairy cows, sheep, and equines, with a strong emphasis on practical applications in pasture-based systems. His recent publications reveal a strong trend in precision livestock farming, combining sensor data, GPS tracking, and AI for behavior modeling and disease detection. He also investigates probiotics for mastitis prevention, equine gut microbiota, and environmental DNA for parasite control, reflecting a multidisciplinary and translational research approach. No specific scientific awards were mentioned in the provided text. Michael has supervised 12 PhD students to completion and is currently supervising doctoral research in smart-farm data analytics and equine microbiome analysis. He has secured significant research funding, including a $115.8 million long-term grant and leadership in the Zero Net Emissions from Agriculture CRC, demonstrating strong grant acquisition and project leadership. His teaching expertise covers ruminant nutrition, physiology, ethics, and welfare, and he has designed curricula for BSc, MSc, and PhD programs. He leads research initiatives at TIA, collaborating with government, industry, and commercial partners through demonstration farms and contractual agreements. His work is highly collaborative, involving numerous co-authors across institutions and disciplines.

• Data Analytics
• Machine Learning
• Evolutionary Game Theory
• +۵ مورد دیگر

Raymond Chiong is an Associate Professor at the School of Electrical Engineering and Computing , University of Newcastle, Australia. He holds a PhD in Computer Science (University of Melbourne) and an MSc in Computer Science (University of Birmingham, UK). His research spans machine learning , agent-based modeling , and evolutionary optimization , with applications in depression detection , energy-efficient scheduling , malicious domain identification , and financial forecasting . Education PhD, University of Melbourne MSc, University of Birmingham (UK) Research Interests Automated intelligent computing methods Agent-based modeling of societal dynamics Machine learning for health and finance Evolutionary algorithms for industrial optimization Scientific Awards 2019 Faculty Award for Research Supervision Excellence 2016 Vice Chancellor's Early Career Research Award 2015 NSW Young Tall Poppy Science Award 2015 Faculty Award for Research and Innovation Industry Collaborations Scientific Advisory Board of Complexica (Australia) International Scientific Advisory Board of Zio (Spain) Projects with Komatsu and SOS Technology Editorial Roles Editor-in-Chief, Journal of Systems and Information Technology Editor, Engineering Applications of Artificial Intelligence Former Editor-in-Chief of Interdisciplinary Journal of Information, Knowledge, and Management Former Associate Editor for IEEE Computational Intelligence Magazine

Thuy Frakkingمشاهده پروفایل

مدرس ارشد

• Speech-Language Pathology
• Pediatrics
• Swallowing Disorders
• +۴ مورد دیگر

Dr. Thuy Frakking is a Senior Lecturer at the Child Health Research Centre, Faculty of Health, Medicine and Behavioural Sciences, The University of Queensland. Her research focuses on pediatric speech-language pathology, swallowing disorders, and healthcare service optimization for children with chronic conditions. Bachelor (Honours) of Speech Pathology, The University of Queensland Doctor of Philosophy in Paediatrics, The University of Queensland Her work applies advanced methodologies like cervical auscultation for aspiration detection, automated swallow sound analysis using AI, and evaluates care coordination models for chronic pediatric conditions. Publications span Dysphagia , JAMA , and Clinical Otolaryngology , covering clinical trials and health outcomes research. Current research projects include external validation of aspiration detection classifiers and standardization of swallowing sound analysis for neonatal care. She supervises PhD candidates investigating community-based aspiration diagnostics and respiratory muscle strength in children.

Paul Kennedyمشاهده پروفایل

استاد

• Biomedical Data Analytics
• Bioinformatics
• Machine Learning
• +۴ مورد دیگر

Professor Paul Kennedy is the Head of the School of Computer Science at the University of Technology Sydney (UTS) Faculty of Engineering and Information Technology. He has made significant contributions to data analytics education, receiving a 2013 OLT Citation for Outstanding Contributions to Student Learning and multiple UTS teaching awards. His research focuses on biomedical data analytics, particularly in pediatric cancer treatment outcomes, bioinformatics pipelines for vaccine discovery, and visual analytics for explainable AI in healthcare. His recent publications highlight advancements in multi-criteria decision-making for vaccine candidates, edge-cloud frameworks for omics data, and immersive VR environments for genomic data visualization. He leads the Biomedical Data Science Laboratory at UTS Australian Artificial Intelligence Institute and co-directs the UTS-Queens University AI-driven clinical tools initiative. A key collaborator with the Children’s Hospital at Westmead, he has secured over $2.4 million in research funding and served on international data mining conference committees since 2006. Scientific Awards: 2013 OLT Citation for Outstanding Contributions to Student Learning 2012 UTS Learning and Teaching Award 2020 Team Teaching Award 2019 Team Teaching Citation His methodological innovations span topological data analysis for stress detection, feature-ranking in RNA sequencing, and domain-adaptation techniques in machine learning. He also contributes to global AI ethics standards through ISO/IEC SC42 committee membership.

Fang Chenمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Human-Centered Computing
• +۷ مورد دیگر

Fang Chen is a Professor and Executive Director of the Data Science Institute at the University of Technology Sydney . With a career spanning academia, industry, and government, she has held leadership roles including Dean at Beijing Jiaotong University and senior positions at Intel, Motorola, and CSIRO. Research Interests include: Artificial Intelligence and Ethical AI Human-Computer Interaction and Cognitive Modeling Structural Engineering and Infrastructure Analytics Digital Transformation and Cybersecurity Optimization Algorithms and Multimodal Learning Article Trends demonstrate expertise in AI fairness frameworks, structural optimization, digital twins for transportation systems, causal reasoning in LLMs, and cybersecurity applications. Her work bridges theoretical innovation (e.g., ASM framework, SPFP algorithm) with real-world deployments (rail networks, illicit marketplace detection). Scientific Awards include: 2018 Eureka Prize for Excellence in Data Science 2021 NSW Premier's Science and Engineering Prize Women in AI Award (Australia & New Zealand) Intelligent Transport Systems Australia National Awards (2014-2018) iAwards (2017-2024) Leadership & Grants : She has supervised over 60 PhD students and leads major funded projects including ImpleMATE Responsible AI , ChatECG for cardiac monitoring, and Beihive Health Data for agricultural productivity. Her 400+ publications and 30+ international patents reflect her global impact across eight countries.

Gustavo Batistaمشاهده پروفایل

دانشیار

• Machine Learning
• Data Mining
• Time Series Analysis
• +۳ مورد دیگر

Associate Professor Gustavo Batista is a prominent researcher in the School of Computer Science and Engineering at the University of New South Wales, where he joined in 2018 after more than a decade at the University of Sao Paulo (USP). He previously served as a visiting researcher at the University of California, Riverside (2010-2012) working with Professor Eamonn Keogh. Education: Habilitation in Computer Science, University of São Paulo at São Carlos (2016) PhD in Computer Science, University of São Paulo at São Carlos (2003) MSc in Computer Science, University of São Paulo at São Carlos (1997) BS in Computer Science, São Paulo State University (1994) Professor Batista's research focuses on practical applications of Machine Learning, particularly in supervised machine learning, data mining, time series analysis, data streams, and imbalanced data. His work bridges theoretical computer science with real-world applications, especially in developing lightweight models for embedded devices and sensors. His research approach emphasizes identifying gaps in literature through challenging applications, leading to contributions in both Computer Science and application domains. His publication record demonstrates consistent contributions across time series analysis, data streams, and quantification, with notable emphasis on developing algorithms that function effectively in resource-constrained environments. His work on the UCR suite for time series matching under warping earned the KDD Best Research Award in 2012, while his more recent work on quantification algorithms received the Best Research Paper Award at DSAA-2020. Scientific Awards: Best Research Paper Award, IEEE International Conference on Data Science and Advanced Analytics (2020) Research Fellow, level 2, National Council for Scientific and Technological Development, CNPq (2017-2020) Research Fellow, level 2, National Council for Scientific and Technological Development, CNPq (2014-2017) Google Research Award in Latin America (2015-2016) Best Research Paper Award, ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2012) Professor Batista has successfully secured significant grant funding as principal investigator, including a $500,000 USAID Combating Zika and Future Threats Grand Challenge award and multiple FAPESP and CNPq grants. He currently supervises PhD students Tiago Pinho da Silva (working on Election Forensics) and Antonio Parmezan (working on Hierarchical Classification of Data Streams), contributing to the next generation of data science researchers. His research group has developed innovative tools like EmbML, which converts scikit-learn and Weka classifiers into C++ code for low-power microcontrollers, demonstrating his commitment to practical implementations of machine learning in resource-constrained environments.

• Artificial Intelligence
• Machine Learning
• Operations Research
• +۵ مورد دیگر

Dr. Yuan Sun is a Lecturer in Business Analytics and Artificial Intelligence at La Trobe University's La Trobe Business School. His research focuses on leveraging machine learning for combinatorial optimization, including problem reduction methods and hybrid algorithms. He has contributed to top-tier journals like IEEE Transactions on Pattern Analysis and Machine Intelligence and conferences such as ICML and NeurIPS. Sun collaborates with researchers from institutions like Monash University and Singapore Management University, and is affiliated with the ARC Training Centre in Optimisation Technologies (OPTIMA). He co-organized the ACM GECCO 2024 conference and serves on program committees for major AI conferences. Academic Position: Lecturer at La Trobe University (2022–present) Education: PhD in Artificial Intelligence (University of Melbourne), BSc in Applied Mathematics (Peking University) Research interests span machine learning, operations research, and optimization, with notable work on integrating AI with digital twins for sustainable power grids and federated learning frameworks like F3KM. Sun's methods enhance constraint programming and ant colony optimization through supervised learning, addressing complex real-world problems. Grants and collaborations include the 'Quantum Enhanced Optimisation for Energy Efficient Data Centres' project and consulting for the Australian mining workforce analysis. Teaching roles include Algorithms & Analysis and Evolutionary Computing, with joint supervision of PhD projects on combinatorial optimization and mixed-integer programming.

• Data Mining
• Rough Sets
• Granular Computing
• +۴ مورد دیگر

Georg Peters is a Professor affiliated with the School of Arts and Humanities. His research spans data mining, machine learning, and educational technology, with a focus on rough sets, granular computing, and STEM education frameworks. He collaborates frequently with researchers like Richard Weber, Jan Seruga, and Tom Rueckert on interdisciplinary projects. His work integrates computational methods with practical applications, such as credit scoring systems, dynamic data analysis tools, and educational platforms for mathematics and programming. A recurring theme is the development of frameworks that bridge theoretical computer science with real-world educational and analytical challenges. Publications emphasize data-driven decision-making, clustering algorithms, and technology-enhanced learning. Recent articles explore probabilistic rough sets for finance, R-based educational environments, and dynamic granular clustering. No awards, students, or grants are mentioned in available sources.

Daniel Schmidtمشاهده پروفایل

مدرس ارشد

• Bayesian Inference
• Information Theory
• Statistical Genomics
• +۷ مورد دیگر

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

• Deep Learning
• Machine Learning for Biological and Medical Data
• Artificial Intelligence
• +۴ مورد دیگر

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

Abigail Koayمشاهده پروفایل

پژوهشگر ارشد

• Cyber Security
• Machine Learning
• Industrial Control System (ICS) Security
• +۳ مورد دیگر

Dr. Abigail Koay is an Honorary Research Fellow at the University of Queensland's School of Electrical Engineering and Computer Science. Her research focuses on cybersecurity, machine learning applications in industrial systems, and healthcare technology. She has contributed to projects such as real-time cyber-attack detection using weakly supervised learning, supported by UQ Cyber Seed Funding (2021–2022). Her work spans multiple disciplines including: Cybersecurity for Industrial Control Systems (ICS) IoT network anomaly detection using fog-assisted frameworks Machine learning for medical imaging (e.g., glaucoma detection) AI-driven cybersecurity strategies for smart grids Recent publications highlight advancements in: Irregular time series analysis using GNNs Positive-unlabeled learning with random forests Domain generalization in retinal image analysis Dr. Koay has authored/co-authored 15+ peer-reviewed articles across journals like Frontiers of Computer Science , IEEE Access , and conferences including NeurIPS and ISGT Asia. She collaborates with industry partners on projects like Plan2Defend for smart grid security and SDGen for synthetic cybersecurity dataset generation. Her research integrates theoretical machine learning with practical cybersecurity challenges, emphasizing real-world applicability in critical infrastructure and healthcare systems.

• Machine Learning
• Artificial Intelligence
• Data Analytics
• +۹ مورد دیگر

Dr. Mukesh Prasad is an Associate Professor at the School of Computer Science , University of Technology Sydney (UTS). With expertise in Machine Learning , Artificial Intelligence , and Computer Vision , his research addresses applications in healthcare, biomedical science, and smart infrastructure. He holds a Ph.D. in Computer Science from National Chiao Tung University, Taiwan, and an M.S. in Computer and Systems Sciences from Jawaharlal Nehru University, India. Key research areas: Machine Learning, AI, Brain-Computer Interfaces, IoT, and Evolutionary Computation Industry experience: Principal Engineer at TSMC (2016-2017), Postdoctoral Researcher at National Chiao Tung University Dr. Prasad has secured competitive grants for AI applications in disaster response, conversational agents, and medical diagnostics. His work has been published in high-impact venues like IEEE , ACM Transactions , and Springer Nature , with over 200 peer-reviewed papers. He serves on editorial boards for journals including Frontiers in Neurorobotics and ACM Computing Surveys . Scientific Awards: Vice Chancellor Teaching and Learning Citation Award (2019) Alumni Fellowship for Ph.D. (2014) Golden Bamboo NCTU Fellowship (2010) Professional Members: IEEE (2011), ACM (2019)