جست‌وجوی استاد و پژوهشگر

• Artificial Intelligence
• Machine Learning
• Neural Networks
• +۴ مورد دیگر

Prof Kai Qin is a Professor of AI and Data Science at Swinburne University, affiliated with the School of Science, Computing and Emerging Technologies. He holds roles including Director of the Intelligent Data Analytics Lab, Deputy Director of the Swinburne Space Technology and Industry Institute, and Vice President for Education at IEEE Computational Intelligence Society (CIS). Qin earned his B.Eng. from Southeast University (2001) and PhD from Nanyang Technological University (2007). His research focuses on Computational Intelligence (CI), encompassing Neural Networks, Evolutionary Computation, and Fuzzy Systems, with applications in Machine Learning, Remote Sensing, and Pervasive Computing. His work has garnered over 20k Google citations and recognition such as IEEE Fellow (2025). Key achievements include the 2012 IEEE Transactions on Evolutionary Computation Outstanding Paper Award and leadership in conferences like IJCNN 2022. He pioneered the Master of Data Science program at Swinburne (2018–2020) and leads initiatives in federated learning, onboard AI for satellite missions, and AI-driven medical diagnostics. Qin’s professional contributions span editorial roles in journals like Swarm and Evolutionary Computation and leadership in IEEE technical committees. His grants include SmartSat CRC projects for satellite AI and ARC-funded research on gravitational lensing and traffic analytics.

• Network Analysis
• Graph Modeling
• Local Content Development
• +۷ مورد دیگر

Ebenezer Afrifa-Yamoah is a researcher at Edith Cowan University's School of Science with a diverse research portfolio spanning environmental science, public health, statistics, and education. His work demonstrates a strong focus on Ghanaian contexts while addressing global challenges in resource management, health, and education. Dr. Afrifa-Yamoah's research interests include: Network analysis and graph modeling of complex systems Local content development in resource extraction sectors Missing data imputation techniques for environmental monitoring Diabetes research and management in Ghanaian populations Entrepreneurship education and student outcomes Climate change impacts and CO2 emissions analysis His methodological expertise spans path analysis, mediation modeling, Bayesian statistics, and network analysis to address complex problems across multiple sectors. Recent publications (2024-2025) show increasing focus on sustainable resource management in African contexts and application of data science to agricultural, public health, and environmental challenges. His work on Ghana's oil and gas sector revealed the centrality of institutional characteristics in driving local content development, while finding economic growth was the least integrated domain in the ecosystem. His scientific contributions include: Methodological advances in missing data imputation for environmental monitoring Analysis of diabetes management biomarkers in Ghanaian populations Studies on occupational health and safety in Ghanaian mining Research on postharvest technology for agricultural products like jackfruit and persimmon Investigations of educational assessment design during the pandemic Dr. Afrifa-Yamoah maintains active collaborations between Australian and Ghanaian institutions, with his interdisciplinary work demonstrating strong methodological rigor across diverse application areas.

Haohui Luمشاهده پروفایل

پژوهشگر ارشد

• Artificial Intelligence
• Graph Machine Learning
• Health Informatics
• +۱۲ مورد دیگر

Haohui Lu is a Research Fellow at the Molly Wardaguga Research Centre within the Faculty of Health. He is registered to supervise postgraduate research and holds a PhD from The University of Sydney, awarded in March 2024. His work bridges artificial intelligence and public health, with a focus on ethical and explainable AI systems. Research Interests: Artificial Intelligence and Machine Learning Graph Machine Learning and Knowledge Graphs Health Informatics and Clinical Decision Support Bias Mitigation and Fairness in AI Large Language Models for Healthcare Statistical Modeling and Real-World Data Analysis His recent publications demonstrate a strong trend in applying machine learning—particularly graph-based and language models—to public health challenges such as disease progression, policy analysis, and undernutrition. He frequently publishes in high-impact journals like PLoS One , Scientific Reports , and Neurocomputing , and presents at top venues such as EMNLP. His work emphasizes fairness, interpretability, and practical impact for policymakers and communities. Scientific Contributions and Recognition: Active contributor to UN Sustainable Development Goals, particularly in health and well-being. Presented research at EMNLP 2024 on LLMs for disease prediction. Research disseminated through news outlets and widely shared on academic platforms like Mendeley. Advising and Leadership: Registered as a postgraduate research supervisor, indicating a role in mentoring future researchers. Provides technical leadership in AI tool development, research data platforms, and LLM applications, supporting multidisciplinary and international collaborations. Laboratories and Teams: Based at the Molly Wardaguga Research Centre, a hub for health research, where he contributes to projects integrating AI with real-world health data and policy.

• Internet of Things (IoT)
• Sensor Networks
• Automated Learning
• +۶ مورد دیگر

Marimuthu Palaniswami is a Professor in the Department of Electrical and Electronic Engineering at the University of Melbourne, specializing in IoT, sensor networks, and computational intelligence. He leads large-scale research initiatives, including the ARC Research Network ISSNIP and the Centre of Excellence for Networked Decision Systems (CENDS). His work spans infrastructure, smart cities, healthcare, and environmental monitoring, with over 400 publications in top-tier journals and conferences. Education Ph.D., University of Newcastle, Australia Masters (Research), University of Melbourne Masters (Coursework), Indian Institute of Science Bachelor of Engineering (Honours), University of Madras Research Focus His research translates into deployable technologies for societal impact, including smart city projects (e.g., European Union's FP7 Smart Santander), healthcare diagnostics (e.g., CSIRO/Microsoft fetal monitoring tools), and environmental monitoring (Great Barrier Reef initiatives). He emphasizes interdisciplinary collaboration, leveraging IoT and AI for healthcare, transport, and governance. Recent Projects Developing Non-Invasive Diagnostics to Prevent Stillbirth (2019–2025) Hydrogen Storage Using Concrete Composites (2022–2025) Academic Centres of Cyber Security Excellence (2024–2026) Awards & Recognition Fellow of IEEE (2013–2015 Distinguished Lecturer) Knowledge Transfer Excellence Awards (2008, 2008, 2010) Recipient of multiple government and industry grants totaling millions in funding Professional Contributions Co-founder of the European IoT Forum, former co-director of CENDS, and advisor to EU and U.S. projects. Has organized over 10 IEEE conferences and served on NSF and EU panels. Labs & Teams Leads interdisciplinary teams in IoT, healthcare technology, and sensor networks, fostering collaborations with industry partners like Arup and Microsoft Research.

Yuzhe Tianمشاهده پروفایل

مدرس پاره‌وقت

• Anomaly Detection
• Industrial Internet of Things (IIoT)
• Sleep Analysis
• +۵ مورد دیگر

Yuzhe Tian is a Part-Time Lecturer at the School of Computing, Macquarie University, within the Faculty of Science and Engineering. His research focuses on interdisciplinary applications of machine learning, including anomaly detection in industrial IoT systems, sleep stage recognition via graph networks, and brain-computer interface (BCI) technologies. He holds a Doctor of Philosophy and has published extensively in top-tier conferences and journals since 2019. Research interests include real-time BCI systems using geometric learning, optimization of neural networks (e.g., model pruning and quantization), and healthcare applications like sleep analysis through graph attention networks. His work bridges computational methods with practical challenges in biomedical engineering and industrial automation. Yuzhe has collaborated on projects leveraging multi-view learning for anomaly detection and swarm intelligence algorithms (e.g., Krill Herd Algorithm). His publications span topics from lightweight BCI frameworks to cybersecurity in IIoT environments, demonstrating a strong focus on both theoretical advancements and real-world implementation.

• Network Science
• Cybersecurity
• Data Analysis
• +۳ مورد دیگر

Matthew Roughan is a Professor and Interim Director of the Teletraffic Research Centre at the University of Adelaide. His research focuses on network science, cybersecurity, data analysis, and statistical modeling. He is affiliated with the University's academic community and contributes to both theoretical and applied research in these domains. His work spans diverse areas including satellite maneuver analysis, causal network discovery, entropy rate estimation, and protocol reverse-engineering. Recent projects highlight advancements in machine learning applications for space surveillance, robust causal network development, and optimization algorithms for multi-modal systems. Roughan's articles reflect a commitment to interdisciplinary approaches, blending mathematics, computer science, and domain-specific knowledge. Notably, his contributions to entropy theory, cybersecurity tool development, and space technology analysis underscore his impact in both academic and practical contexts. His work in Teletraffic Research Centre involves managing interdisciplinary projects and fostering innovation in network and data analysis methodologies.

Niko Suenderhaufمشاهده پروفایل

استاد

• Robotic Vision
• Computer Vision
• Machine Learning
• +۷ مورد دیگر

Professor Niko Suenderhauf is a leading researcher in robotic vision and AI at Queensland University of Technology (QUT). He focuses on enabling robots to understand and interact with their environment through advancements in SLAM, scene understanding, and semantic mapping. His work bridges robotics, computer vision, and machine learning, aiming to create reliable, relatable, and sustainable robotic systems that collaborate with humans to address societal challenges like climate change and aging populations. His key achievements include pioneering graph-based SLAM methods integrated into the ROS ecosystem, developing object-based semantic SLAM systems, and demonstrating the efficacy of deep learning for place recognition. Collaborations with institutions like DeepMind and Amazon reflect his interdisciplinary approach. Awards include recognition for groundbreaking SLAM research and highly cited publications. Research interests emphasize open-world navigation, privacy-preserving vision, and embodied AI, with a focus on practical applications in construction, environmental monitoring, and domestic robotics. His work on uncertainty quantification and robust perception ensures systems operate safely in unpredictable environments. Professor Suenderhauf actively engages in education, mentoring students across undergraduate and postgraduate levels, and emphasizes the societal impact of technology through collaborations with neuroscience, law, and design experts.

Bai-qian Daiمشاهده پروفایل

دانشیار مدعو

• Clean Coal Technology
• Ash Slagging
• Energy Evaluation
• +۵ مورد دیگر

Dr. Bai-qian Dai is an Adjunct Associate Professor in Chemical & Biological Engineering at Monash University's Suzhou Research Institute. His research focuses on clean coal technology, ash slagging mechanisms, energy process design, and fly ash utilization. He holds a PhD from Wuhan University of Technology (2006) and has conducted postdoctoral studies on fly ash vitrification. His work contributes to UN Sustainable Development Goals related to affordable clean energy and climate action. He leads projects on carbon waste valorization and catalyst development, including roles in the ARC Research Hub for Value-Added Processing of Underutilised Carbon Waste. Notable awards include the Suzhou Industrial Park Young International Discipline Leaders (2021) and the Suzhou Industry Park Excellent Teaching Award (2022). His publications span over 60 peer-reviewed articles, emphasizing sustainable energy solutions, computational modeling, and catalytic innovation. Education: Bachelor of Engineering, Wuhan University of Technology (2006) Master of Engineering, University of Shanghai for Science and Technology (2009) PhD in Clean Coal Technology (2013) Research interests include: Clean energy systems High-temperature catalysis CFD modeling for industrial processes Waste-to-value technologies Recent projects involve: Development of Pt-based electrocatalysts CO₂ capture in FCC units Smart cokemaking via AI-driven modeling Awards: National honour (2015) Suzhou Young Leader award (2021) Teaching Excellence (2022) Collaborations span institutions like Southeast University (China), QUT, and industry partners such as Enzide Technologies and Carbon Technologies Australia.

Zahidul Islamمشاهده پروفایل

استاد

• Data Mining
• Machine Learning
• Cyber Security
• +۴ مورد دیگر

Zahidul Islam is a Professor of Computer Science and Associate Dean (Research) at Charles Sturt University's Faculty of Business, Justice and Behavioural Sciences (FOBJBS). With a PhD in Computer Science from the University of Newcastle and over 150 peer-reviewed publications, his research focuses on data mining, cybersecurity, and AI applications across health, agriculture, and social systems. BSc in Civil Engineering (RUET, Bangladesh) Graduate Diploma in Information Science (UNSW, Australia) PhD in Computer Science (University of Newcastle, Australia) His research spans advanced domains like privacy-preserving data mining , federated learning , and cross-domain security threats . Recent work addresses AI applications in agri-food supply chains, malware detection across platforms, and collaborative learning frameworks for medical data. Key scientific achievements include: Cyber Security Researcher of the Year 2021 (AISA) Innovation Award 2017 (NSW Agency for Clinical Innovation) 2024 Excellence Award (CSU) Stanford University - Elsevier World Top 2% Scientists (2024) $11M+ in competitive research funding

• Probabilistic Methods
• Complex Networks
• Network Simulation
• +۲ مورد دیگر

Max Glonek is a Lecturer in Statistics at the School of Computer and Mathematical Sciences, University of Adelaide. His research focuses on developing probabilistic methods for analyzing complex networks, with applied work in political voting behavior and network polarization. Glonek's research interests center on probabilistic modeling, network simulation, and computational statistics. Key applications include measuring political partisanship through voting network analysis and developing algorithms for graph-based machine learning. His publications demonstrate consistent focus on network analysis techniques (2018-2019) and foundational work in directional time series modeling (2015-2016). Research integrates computer science, statistics, and domain-specific applications in political science and geophysics.

Jinling Wangمشاهده پروفایل

دانشیار

• Geospatial Mapping
• Global Navigation Satellite Systems (GNSS)
• Inertial Navigation Systems (INS)
• +۵ مورد دیگر

Associate Professor Jinling Wang is an academic at the University of New South Wales (UNSW), affiliated with the School of Civil Engineering and the Department of Civil Engineering. Her research focuses on geospatial mapping and navigation, including Global Navigation Satellite Systems (GNSS), Inertial Navigation Systems (INS), and multi-sensor integration for applications in autonomous vehicles and high-definition mapping. She has supervised 22 PhD and 6 Master’s students, seven of whom won prestigious international awards during their studies. Her awards include being named on the World’s Top 2% Scientists List from 2020 to 2024 and recognized as Australia’s Research Leader in 'Radar, Positioning, and Navigation' multiple times. She leads projects such as the UNSW Engineering Goldstar Award 2024 Research Grant and the UNSW-Tsinghua Collaborative Research Seed Project. Dr. Wang’s research trends emphasize ionospheric analysis, robust positioning algorithms, and sensor fusion for autonomous systems. Her articles span disciplines like atmospheric science, data science, and geomatics engineering, addressing challenges in GNSS reliability and real-time navigation integrity. Her advisory and grants section highlights her role in advancing autonomous vehicle technologies and HD mapping. She is part of research teams utilizing advanced facilities at UNSW’s Mark Wainwright Analytical Centre and collaborates on global navigation challenges.

Raymond Wongمشاهده پروفایل

دانشیار

• Database Systems
• Data Science/Analytics
• Information Extraction
• +۵ مورد دیگر

Associate Professor Raymond Wong is a faculty member in the School of Computer Science & Engineering at UNSW Sydney, where he also serves as Director of Online Education. He holds a PhD from Hong Kong University of Science & Technology, and has extensive expertise in database systems, data science, information extraction, and cloud computing. With over 220 conference papers, 56 journal articles, and two US patents, his research spans data mining, machine learning, and big data analytics. He has received 8 best paper awards and 4 industry/professional recognitions. His advisory roles include stints at Data61/CSIRO and Stats Central, and he has co-founded technology startups. Key projects include the Singapore NTU SPIRIT Smart Nation Research Centre's Intelligent Case Retrieval System (ICRS) and collaborations across Asia-Pacific regions. His research interests focus on database optimization, data analytics, NLP, and cloud infrastructure. He has supervised 25 PhD students and 65 honors students, six of whom earned University Medals. Notable grants include projects on ovarian cancer detection ($135k), cardiovascular disease analysis ($100k), and social media analytics ($250k). Awards include the 2016 ARC UNSW Supervisor Award and multiple IEEE best paper honors. His work bridges academia and industry, addressing challenges in healthcare, cybersecurity, and data-driven innovation.

• Operating systems
• Distributed systems
• Computer Science
• +۳ مورد دیگر

Professor Willy Zwaenepoel is a distinguished academic and Dean of the Faculty of Engineering at the University of Sydney. He holds Fellowships from ACM, IEEE, and ATSE. His research focuses on distributed systems, operating systems, and experimental computer science. Previously, he spent two decades at Rice University and nine years as Dean of EPFL's School of Computer and Communication Sciences before joining Sydney in 2018. Education: BS/MS, Ghent University (1979) MS/PhD, Stanford University (1980/1984) Research Interests: His work emphasizes distributed systems and operating systems, with contributions to key-value stores, transactional systems, and large-scale graph processing. Recent projects include optimizing geo-replicated systems and improving datacenter scheduling efficiency. Articles Trends: Recent publications address OS scheduling (Nest), distributed graph mining (Tesseract), and transactional systems' performance limits. He explores hardware-software co-design for multicore systems and energy-efficient data centers. Awards: Fellow of ACM (2021) Fellow of IEEE (2020) Fellow of ATSE (2019) Grants & Advising: Active grants include adaptive key-value store research (2021) and large-graph processing systems (2018). He advises PhD students and postdocs on distributed systems and storage challenges. Labs/Teams: Leads the Sydney systems research group focusing on scalable distributed systems and cloud infrastructure.

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.

Erick Alfons Lisanganمشاهده پروفایل

مدرس

• Intelligent Transportation Systems
• Artificial Intelligence
• Deep Learning
• +۵ مورد دیگر

Erick Alfons Lisangan is a Computer Science lecturer at Atma Jaya University of Makassar and a Doctor of Philosophy student at the University of Western Australia's School of Physics, Mathematics and Computing. His research integrates artificial intelligence with transportation infrastructure to address urban mobility challenges. Education Master of Computer Science, Gadjah Mada University (2013-2015) Bachelor of Computer Science, Atma Jaya University of Makassar (2006-2010) Research Interests His core focus lies in Intelligent Transportation Systems , analyzing graph traffic networks and spatiotemporal data collection. He develops algorithms for real-time traffic condition inference using embedded systems and AI-driven simulation. Specialized work includes deep learning architectures (LSTM/BiLSTM) for passenger flow prediction, fuzzy logic systems for dynamic route optimization, and wireless sensor networks for smart city applications like flood detection. His research bridges theoretical AI with practical urban infrastructure solutions. Research Output Trends Publications (2018-2025) reveal a trajectory from foundational smart city sensor networks toward advanced AI-driven transportation modeling. Recent work emphasizes hybrid deep learning for Transjakarta bus system prediction, while earlier contributions established fuzzy ant colony systems for traffic-responsive routing and image-based fake news detection frameworks. Advising and Grants Lisangan serves as a casual tutor for Data Structures & Algorithms and Computational Thinking with Python at UWA during his PhD studies. No formal advisees or research grant details are documented in the provided materials. Labs and Teams His doctoral research operates under supervision of Professor Rachel Cardell-Oliver and Professor Amitava Datta at UWA, contributing to intelligent transportation systems research groups with cross-institutional collaborations in Indonesia and Australia.