جست‌وجوی استاد و پژوهشگر

• Data Mining
• Data Science
• Data Analytics
• +۴ مورد دیگر

Professor Geoff Webb is a world-leading data scientist at Monash University , serving as Director of the Monash University Centre for Data Science within the Faculty of Information Technology . His research focuses on leveraging data science to enable evidence-based decision making and derive actionable insights through artificial intelligence, machine learning, and big data analytics. Core expertise in data mining , bioinformatics , and computational biology Developed Magnum Opus software and contributed to the Weka machine learning workbench Recipient of the Eureka Prize for Excellence in Data Science and leadership roles in major data mining conferences Research Interests Professor Webb's work spans artificial intelligence , machine learning , and data analytics , with a focus on black-box user modelling , interactive data analytics , and statistically-sound pattern discovery . His recent publications highlight advancements in Bayesian networks , time series analysis , and genomic data interpretation , demonstrating his interdisciplinary impact. Scientific Contributions AI in healthcare : EHR-ML framework for clinical records analysis Biological applications : KcatNet for enzyme prediction, PFresGO for protein function Data mining innovations : OPUS search algorithm, proximity forest techniques As a technical adviser to data science company Froomle , he bridges academic research with real-world applications. His work has been recognized through numerous research awards and leadership as Editor-in-Chief of Data Mining and Knowledge Discovery for a decade.

• Geo-environmental remediation
• PFAS contamination and remediation
• Geochemical kinetics and modeling
• +۵ مورد دیگر

Professor Brett Turner from the School of Civil and Environmental Engineering at University of Technology Sydney is a leading expert in geo-environmental remediation , PFAS contamination , and geochemical kinetics with over 20 years of experience. He heads a state-of-the-art emerging contaminant analysis laboratory equipped with LC/MSMS, FTIR, and Simultaneous Thermal Analysis (STA) instruments. PhD Environmental Engineering (University of Newcastle, 1999-2003) B.Sc (Hons 1) (University of Newcastle, 1994-1999) His research focuses on PFAS remediation using natural materials like hemp protein, fluoride removal via calcite permeable barriers, and soil stabilization in coastal environments. Recent publications examine pollen bioaccumulation of PFAS, salinity effects on clay plasticity, and metal ion impacts on defluoridation. Current funding includes an ARC Discovery Indigenous grant (2024-2027) for protein-enhanced landfill clay liners to manage PFAS transport. Awards include the 2015 Discovery Indigenous Research Fellowship and the 2017 Newcastle University Chancellor’s Award for Innovation Excellence. He teaches 49116 Contaminated Site Remediation and Waste Management , emphasizing practical skills in GIS, contaminant transport modeling, and remediation strategies under ASTM standards. His laboratory investigates emerging contaminants through advanced analytical techniques and field testing (CPTu, zeta-potential).

• Machine Learning
• Big Data Analytics
• Computational Intelligence
• +۴ مورد دیگر

Vahid Behbood is a Lecturer at the School of Computer Science within the Faculty of Engineering and Information Technology at the University of Technology Sydney (UTS). He belongs to the Decision Systems and e-Service Intelligence Research Laboratory in the Centre for Quantum Computation and Intelligent Systems. Ph.D. in Software Engineering from UTS (2014) His research spans machine learning , big data analytics , computational intelligence , and transfer learning , with a focus on addressing data shortage challenges through fuzzy systems and domain adaptation. His work includes: Advancing fuzzy regression domain adaptation for cross-domain prediction Developing granular computing techniques for financial failure prediction Exploring SQL query error patterns in computer science education Designing IoT architectural frameworks via systematic reviews Publications demonstrate expertise in neural networks , fuzzy logic , Bayesian methods , and financial data analytics . He applies these techniques to banking ecosystems, real estate valuation, and educational data mining.

• Artificial Intelligence
• Machine Learning
• Federated Learning
• +۸ مورد دیگر

Guodong Long is an Associate Professor at the University of Technology Sydney (UTS) in the Faculty of Engineering and Information Technology. He joined UTS in 2010 and earned his PhD there in 2014. His research focuses on federated learning, trustworthy AI, and pre-trained foundation models with applications in healthcare, IoT, and social media. PhD in Artificial Intelligence, University of Technology Sydney (2014) Leading the Foundation Model and Federated Learning research group (https://www.fmfl.group/) His work addresses challenges in frequency transformation for time series, privacy-preserving healthcare analytics, and spatio-temporal traffic forecasting. He has published extensively at top AI conferences like AAAI, ICLR, and NeurIPS, with significant citation impact (4,682 citations in 2022). Collaborations with industry partners have secured over $4M in external funding. Recent publications emphasize federated foundation models (ICLR'25), privacy-preserving recommendation systems (WWW'25), and adaptive time series analysis. His research integrates domain knowledge and graph learning for multivariate time series imputation and traffic prediction. Dr. Long actively contributes to academic leadership as General Co-Chair for WebConf 2025, Program Co-Chair for AI conferences, and reviewer for top venues. He supervises PhD and master's students and welcomes research visitors for extended collaborations.

• Computer Vision
• Machine Learning
• Human Attention Modeling
• +۲ مورد دیگر

Minh Hoai Nguyen is a Professor at the School of Computer and Mathematical Sciences and the Australian Institute for Machine Learning (AIML) at the University of Adelaide. Previously, he was a tenured Associate Professor at Stony Brook University (2014-2024) and worked at VinAI Research in Vietnam. He holds a Bachelor of Software Engineering from the University of New South Wales (2006) and a PhD in Robotics from Carnegie Mellon University (2012). His research focuses on computer vision and machine learning, with specialization in human attention modeling, gaze prediction, action recognition, and object counting. Recent work explores applications in educational technology, driver monitoring, and multimodal interaction systems using advanced deep learning approaches. Publication analysis reveals strong emphasis on attention mechanisms across domains, with recent works exploring: (1) Gaze and scanpath prediction using transformer architectures, (2) Few-shot learning for action counting and personalized modeling, (3) Multimodal analysis of cognitive states, and (4) Efficient scene-adaptive vision systems. His research consistently bridges theoretical machine learning with practical applications in human behavior analysis. Scientific Awards: Best Student Paper Award at IEEE CVPR (2012)

• Intelligent Systems
• Deep Learning
• Graph Neural Networks
• +۵ مورد دیگر

Professor J. Joshua Thomas is a distinguished academic at University of Wollongong Malaysia, specializing in intelligent systems and advanced computational techniques. He holds a PhD in Intelligent Systems Techniques from Universiti Sains Malaysia (2015) and a Master's degree in Computer Science from Madurai Kamaraj University, India (1999). Professor Thomas has demonstrated strong leadership in academic administration, having served as Head and Deputy Head of the Department of Computing between 2012 and 2017. His research focuses on intelligent systems and interdisciplinary computational algorithms, with recent work emphasizing Deep Learning, Graph Convolutional Neural Networks (GCNN), Graph Recurrent Neural Networks (GRNN), Hyper-Graph Attention Networks, and Quantum Machine Learning. Professor Thomas's projects span diverse applications including end-to-end steering learning systems, algorithm design in drug discovery, and advanced data analytics for electricity consumption and carbon price forecasting. Professor Thomas maintains an impressive publication record with over 50 peer-reviewed publications and 12 books with publishers including Wiley, Elsevier, and IGI Global. His research is supported by multiple active grants at institutional, national, and international levels, including projects on cancer survivor well-being and AI bias in smart cities. Best Paper Award at SCI 2025 Oracle Cloud Infrastructure 2024 Generative AI Certified Professional 2022 Global Staff Awards (UOWGE) - Excellence In Research Winner V-MIIEX2021: COVIDNet - GOLD AWARD Fundamental Research Grant Scheme (FRGS) recipient (2019) As an active researcher and educator, Professor Thomas serves as Principal Investigator on multiple grants, supervises PhD and Master's students, and regularly delivers keynote addresses at international conferences. His collaborative spirit is evident through partnerships with institutions in India, USA, China, and Germany, fostering knowledge exchange and innovation across disciplines.

Sim Kwan Huaمشاهده پروفایل

مدرس

• Time Series Analysis
• Time Series Pattern Recognition
• Data Mining
• +۲ مورد دیگر

Sim Kwan Hua serves as Head of Department for Computer Science and Software Engineering and Lecturer at Swinburne University of Technology Sarawak Campus's Faculty of Engineering, Computing and Science. Joining the institution in 2007 after 6 years in ICT academia, he became Computer Science Program Coordinator in 2011 and has established himself as a key researcher in data-intensive computational fields. His academic credentials include: Bachelor of Information Technology in Computer Science (Honours), Universiti Malaysia Sabah Master of Science in Information Technology, Universiti Malaysia Sarawak Dr. Sim's research program centers on advanced analytical methodologies: Time Series Analysis for dynamic system modeling Time Series Pattern Recognition in complex datasets Data Mining techniques for knowledge discovery Statistical frameworks for data interpretation Predictive Analytic systems for real-world applications His research impact is demonstrated through significant grant funding: Smart Waste Collection System with Intelligent Data Optimization (SDEC TRG 2021, MYR 121,800.00, 2022-2024) Scalable Pattern Recognition in Financial Time Series Data (FRGS 2020, MYR 58,390.00) Prospective graduate students are encouraged to contact Dr. Sim via email to explore research opportunities in his data analytics specialization areas, where he actively mentors the next generation of computational scientists.