جستجوی استاد راهنمای دانشگاهی

Tien Tsin Wongمشاهده پروفایل

استاد

• Generative AI
• Computer Graphics
• Computer Vision
• +۴ مورد دیگر

Tien Tsin Wong is a Professor in the Department of Data Science & AI at Monash University, Australia. Previously, he served as a Professor at the Chinese University of Hong Kong (1999–2024) and held a Visiting Assistant Professor position at the Hong Kong University of Science and Technology (1998–1999). His research focuses on Generative AI, Computer Graphics, Computer Vision, and Computational Manga, with significant contributions to GPU techniques, image-based rendering, and multimedia compression. Education: He earned a B.Sc. (1992), MPhil (1994), and PhD (1998) in Computer Science from the Chinese University of Hong Kong. Research Interests: His work bridges computational techniques with artistic applications, particularly in manga and animation. Notable areas include generative models, diffusion-based video synthesis, and physically plausible scene generation. His research aligns with UN Sustainable Development Goals through innovations in education and digital accessibility. Awards : He has received the 2004 Young Researcher Award, 2005 IEEE Transactions on Multimedia Prize Paper Award, and two international invention medals (Geneva 2018, Asia Hong Kong 2019). Editorial Roles : He serves as an Associate Editor for Computer Graphics Forum , IEEE Transactions on Visualization and Computer Graphics , and Computational Visual Media . His editorial work underscores his influence in advancing visualization and graphics research. Labs/Teams : While not explicitly named, his collaborations span global institutions, focusing on computational manga, generative AI, and GPU-optimized techniques. His work often involves interdisciplinary teams addressing challenges in digital media and AI.

Erniel Bayhon Barriosمشاهده پروفایل

استاد

• Computational Statistics
• Nonparametric Methods
• Data Science/Big Data Analytics
• +۵ مورد دیگر

Erniel Bayhon Barrios is a Professor at the Malaysia School of Business, Monash University. Formerly a professor at the University of the Philippines Diliman and a visiting scholar at Karlstad University (Sweden) and the Asian Development Bank Institute (Japan). He holds a PhD in Statistics (1990) from the University of the Philippines Diliman. His research focuses on computational statistics, nonparametric methods, data science, and computational econometrics, with applications in spatiotemporal modeling, time series, and financial markets. He has contributed to high-dimensional data analysis, volatility modeling, and robust statistical techniques. Key projects include the National Mental Health Survey and Well-Being (2019–2021) and building a data-driven organization for the Habib Group (2025–2026). He is an elected member of the International Statistical Institute (2012), associate editor of Communications in Statistical Applications and Methods , and served on the board of the International Association of Statistical Computing (2022–2025). He advises PhD students on topics like stochastic frontier models, data assignment in big data, and high-frequency time series analysis. His work aligns with UN Sustainable Development Goals related to education and economic growth.

• Transportation Engineering
• Urban Planning
• Network Design
• +۵ مورد دیگر

Roles and Affiliations: Professor David Levinson is a Professor of Transport in the School of Civil Engineering at the University of Sydney since 2017. He holds adjunct faculty status at the University of Minnesota's Department of Civil, Environmental, and Geo-Engineering (since 1999–2016). He previously directed the Accessibility Observatory and the Networks, Economics, and Urban Systems (NEXUS) research group. He is a member of The Net Zero Institute and leads the TransportLab research group. Education: PhD 1998 in Urban and Regional Planning from the University of California, Berkeley. Research Interests: Focuses on transportation networks, urban systems, accessibility analysis, and policy. Key themes include spatial equity, transport economics, and the co-evolution of land use and transport infrastructure. His work emphasizes data-driven approaches to urban mobility challenges, including modeling transport networks and evaluating infrastructure impacts on accessibility and equity. Grants and Projects: Recent grants include Design of micro-decisions in automated transport (2022, ARC) and Reducing pedestrian crashes through better intersection design (2021, Australian Road Safety Innovation). Collaborates internationally on projects like the Accessibility Observatory. Labs and Teams: Leads the TransportLab and collaborates with the NEXUS group. Active in interdisciplinary research, including urban analytics and transport policy.

Scott Sissonمشاهده پروفایل

استاد

• Computational Statistics
• Bayesian Statistics
• Big Data
• +۴ مورد دیگر

Professor Scott Sisson is Director of the UNSW Data Science Hub (uDASH) and Professor of Statistics and Data Science at the University of New South Wales, School of Mathematics and Statistics. His research focuses on computational statistics, particularly solving 'intractable' statistical problems through Bayesian methods, big data techniques, simulation algorithms, and extreme value theory with environmental applications. Education includes a PhD in Statistics from Bristol University (2002), MSc in Environmental Statistics from Lancaster University (1997), and BSc in Mathematics and Statistics from Lancaster University (1996). Research interests span: Bayesian statistics and uncertainty quantification Big data analytics and scalable algorithms Machine learning integration with statistical methods Extreme value modeling for climate/environment Computational techniques for intractable problems Recent publications (2022-2025) demonstrate strong emphasis on Bayesian computation, spatiotemporal modeling, and interdisciplinary applications in materials science, oncology, quantum computing, transportation policy, and ecology. Methodological innovations include likelihood-free inference, modular Bayesian analyses, and symbolic data modeling. Awards and honors: 2020 Service Award (Statistical Society of Australia) 2017 ARC Future Fellowship 2015 G. N. Alexander Medal (Engineers Australia) 2011 Moran Medal (Australian Academy of Science) 2010 J.G. Russell Award (Australian Academy of Science) 2010 Queen Elizabeth II Research Fellowship As Director of uDASH, he leads data science initiatives across UNSW. He maintains sustained ARC funding and supervises students in computational statistics, Bayesian methods, extreme value theory, and machine learning. Professional service includes editorial roles for Statistics and Computing and past presidency of Statistical Society of Australia.

• Power Electronics
• Renewable Energy Systems
• Electrical Machine Drives
• +۳ مورد دیگر

Dr. Xinan Zhang is an Associate Professor in the School of Engineering at The University of Western Australia (UWA), specializing in Electrical, Electronic, and Computer Engineering. He holds a BEng from Fudan University (2008) and a PhD from Nanyang Technological University (2014). Before joining UWA in 2019, he held roles as a Lecturer and Research Fellow in Singapore and Australia. His research focuses on power electronics, electrical machine drives, and renewable energy, with over 60 top-tier publications. He is the Portfolio Lead for Industry Engagement in UWA's School of Engineering and co-leads the Power and Clean Energy (PACE) research group. Education: BEng in Electrical Engineering, Fudan University (2004–2008) PhD in Electrical Engineering, Nanyang Technological University (2010–2014) Research Interests: Dr. Zhang’s work spans power electronics, renewable energy systems, energy storage, and smart grid technologies. He emphasizes practical applications, such as battery management systems for vanadium redox flow batteries and adaptive control strategies for microgrids. His contributions address challenges in energy efficiency, grid stability, and sustainable power solutions. Articles & Trends: Recent publications focus on advanced control algorithms for inverters, battery modeling, and renewable energy integration. His work combines data-driven methods with traditional control theory to enhance system efficiency and reliability. Notable areas include DC microgrid control, vanadium redox flow battery optimization, and model predictive control for power electronics. Awards: Listed in Stanford University’s Top 2% Scientists (2020–2022) Grants & Collaborations: He leads or co-leads projects funded by the Australian government and industry partners, including the GenX Betavoltaic Battery Pilot Manufacturing Process and Mine Electrification . These projects aim to advance clean energy technologies and industrial applications. Labs & Teams: As co-lead of the PACE group, he fosters interdisciplinary collaboration to tackle global energy challenges, aligning with UN Sustainable Development Goals for affordable and clean energy (SDG 7).

Ly Fie Sugiantoمشاهده پروفایل

دانشیار

• Accounting Information Systems
• Data Analytics
• Artificial Intelligence
• +۸ مورد دیگر

Dr. Ly Fie Sugianto is an Associate Professor in the Department of Accounting at Monash Business School, Monash University. Her research focuses on the integration of data analytics, artificial intelligence, and machine learning in accounting and business systems, with applications in the energy sector and organizational behavior. Monash Business School, Monash University Department of Accounting Specialization: Accounting Information Systems, Data Analytics, AI Her research interests span Accounting Information Systems , Agent-Based Simulation , Decision Support Systems , and Technology Adoption . She applies computational methods to study competitive dynamics in deregulated electricity markets and the impact of digital tools on employee well-being and organizational resilience. The recent publications reflect a strong trend in using AI and simulation to analyze complex socio-technical systems, particularly in energy markets and leadership dynamics. Keywords across her work include agent-based modeling , data analytics , servant leadership , and enterprise social media , indicating interdisciplinary research at the intersection of information systems, management, and public policy. Her scientific awards include competitive grants from the ARC (SPIRT/Linkage) , the Australia Indonesia Governance Research Partnership (AIGRP) , and the Sumitomo Foundation . ARC Grant: Dispatch Optimisation in the Australian National Electricity Market Sumitomo Foundation: Technology Use and Employee Well-Being AIGRP: Governance and MSME Resilience during Pandemic Dr. Sugianto has advised research projects and collaborated with industry partners such as Western Power , Ecogen Energy , and AEMO . She is currently accepting PhD students and leads externally funded research initiatives. Her work contributes to UN Sustainable Development Goals related to industry innovation and responsible consumption. She is affiliated with research teams focusing on intelligent decision support systems and digital transformation in business , with active collaborations in Australia and Indonesia.

• Structural Dynamics
• Earthquake Engineering
• Blast and Impact Engineering
• +۳ مورد دیگر

Professor Hong Hao is a John Curtin Distinguished Professor at Curtin University, affiliated with the School of Civil and Mechanical Engineering and the Curtin Research Centre for Infrastructural Monitoring & Protection. His expertise spans Structural Dynamics, Earthquake Engineering, Blast and Impact Engineering, and Structural Health Monitoring. He holds prestigious roles like Fellow of ATSE, ISEAM, and ASCE, and has led organizations such as the International Association of Protective Structures and the Australian Earthquake Engineering Society. Education: BE (Tianjin University, 1982), MSc (UC Berkeley, 1985), PhD (UC Berkeley, 1989). Awards include the Tan Chin Tuan Fellowship and multiple Ko Medals. He has authored over 200 journal articles, with recent work focusing on blast-resistant materials, seismic fragility, and AI-driven structural health monitoring. His research emphasizes resilient infrastructure, including metaconcrete structures, corrosion-resistant materials, and sensor-based damage detection. Ongoing projects involve smart tunnel safety under BLEVE explosions and modular building systems.

• Computer Vision
• Deep Learning
• Reinforcement Learning
• +۷ مورد دیگر

Dr. Chandranath Adak is an Assistant Professor at the Department of Computer Science and Engineering, Indian Institute of Technology Patna (IIT Patna), and concurrently serves as a Visiting Fellow at the School of Computer Science, University of Technology Sydney (UTS), Australia. He holds a Ph.D. in Analytics from UTS (2019) and previously served as an Assistant Professor at Indian Institute of Information Technology Lucknow (IIITL) and the Centre for Data Science at JIS Institute of Advanced Studies, Kolkata. Education: Ph.D. (Analytics), University of Technology Sydney (2019) M.Tech., Computer Science and Engineering, University of Kalyani (2014) B.Tech., Computer Science and Engineering, West Bengal University of Technology (2012) Research Interests: His work spans Computer Vision, Deep Learning, Reinforcement Learning, Document Image Analysis, and AI-driven solutions for healthcare, forensics, and industrial automation. He has pioneered methods in biomarker detection using electrochemical sensors combined with ML models, handwriting analysis for educational and forensic applications, and anomaly detection in industrial systems. His research bridges theoretical advances with real-world applications, such as medical diagnostics and quality control systems. Publications: His recent work includes innovations in biosensor-based medical diagnostics, handwriting evaluation systems, and transformer networks for historical document analysis. These contributions reflect a focus on interdisciplinary applications of AI across healthcare, cultural heritage preservation, and industrial automation. Awards: Start-up Research Grant, SERB, India (2022) Dr. Kalam Doctoral Scholarship, UTS (2018) IEEE CIS Graduate Student Research Grant (2017) Senior Member, IEEE (2024) Teaching & Supervision: Taught courses at UTS including 'Introduction to Data Analytics' and supervised research in machine learning and computer vision. His mentorship emphasizes hands-on experience with AI tools and real-world problem-solving. Labs & Teams: Engaged in collaborative projects at UTS's CIBCI Centre and Griffith University's IIIS, focusing on computational intelligence and sensor-driven AI systems.

• Early Childhood Education
• Art Education
• Play-Based Learning
• +۵ مورد دیگر

Felicity McArdle is a Professor in the Department of Early Childhood Education within the Faculty of Education at Queensland University of Technology. With a distinguished career spanning over two decades, her work centers on reimagining early childhood pedagogy through critical lenses of social justice, art education, and play-based learning. Her research has significantly influenced Australian early childhood policy and practice. McArdle's research interests demonstrate a consistent commitment to transformative education. She critically examines how art and play function as vehicles for social justice, challenging neoliberal discourses in early childhood education. Her work on refugee children's artistic expression and anti-bias curriculum development reveals her dedication to equity-oriented pedagogy. Through collaborative drawing methodologies and visual research approaches, she has pioneered innovative ways of understanding children's perspectives. Her publication trends reveal a sophisticated evolution from foundational work on visual arts in education (2001-2010) toward increasingly critical examinations of power, policy, and social justice (2011-2021). The consistent citation impact of her work—particularly her 2002 paper 'Early Childhood Art Education: A Palimpsest' with over 13,000 citations—demonstrates her field-defining contributions to reconceptualizing art education as critical praxis. McArdle has held significant editorial roles including co-editing the influential 2013 volume 'Young Children, Pedagogy and the Arts' and serving on the editorial board of Australian Journal of Early Childhood. Her collaborations with leading scholars like Susan Grieshaber, Jennifer Sumsion, and Linda Knight have produced seminal work on intentional teaching and play-based learning. Her research methodology expertise centers on visual and arts-based approaches, particularly collaborative drawing techniques that engage children as active research participants. This methodological innovation reflects her commitment to ethical research practices that honor children's agency while generating meaningful insights about early childhood experiences.

Neil Selwynمشاهده پروفایل

استاد

• Digital technology and society
• AI in education
• Digital sociology of education
• +۵ مورد دیگر

Neil Selwyn is a Professor in the Faculty of Education at Monash University with over 30 years of research experience focusing on the integration of digital technology into educational settings. His work centers on the 'real-life' constraints and problems faced when implementing technology-based education, with expertise in AI, automation, data-driven education, and digital degrowth. His primary research interests span digital technology and society, the use of digital technologies in educational settings, digital sociology of education, and environmental sustainability issues associated with digital technology use. Professor Selwyn is recognized internationally for his critical approach to educational technology and has led numerous nationally-funded projects examining educational data, learning analytics, and AI technologies. His recent publications reflect a growing focus on the environmental implications of AI in education, digital degrowth, and the practical challenges teachers face when implementing AI tools in classrooms. This work demonstrates a shift toward examining the sustainability and ethical dimensions of educational technology. Fellow of the Academy of Social Sciences in Australia (2024) Professor Selwyn serves as a media commentator for major outlets including the NYT, WSJ, CNN, Guardian, BBC, and ABC, and hosts the 'Education Technology Society' podcast. He maintains significant international collaborations, currently holding visiting positions at Lund University (2025), University of Oxford (2023-2027), and previously at the University of Gothenburg (2016-2017).

• Natural Language Processing
• Knowledge Graphs
• Machine Learning
• +۵ مورد دیگر

Dr. Sirui Li is a Lecturer at Murdoch University's School of Information Technology within the College of Science, Technology, Engineering and Mathematics. Her research focuses on Artificial Intelligence, Natural Language Processing (NLP), Machine Learning, Knowledge Graphs, Data Analysis, Temporal Data, and Multi-modal Models, with applications in medicine, agriculture, and mining. She collaborates with industry partners like BHP and has published in journals such as Food Chemistry and Knowledge and Information Systems , as well as conferences like ICSME and IJCNN. Education: Bachelor of Advanced Computing (Honours) in Computer Science at Australian National University Master of Computing (Specialising in AI) at ANU Ph.D. in Information Technology (AI) at Murdoch University Research interests include interdisciplinary applications of AI, such as clinical coding privacy solutions, disease spread modeling, and drug repurposing for pandemics. Her work emphasizes practical industry integration, demonstrated through awards like the 2024 EMNLP Best Demo Award and the 2023 Iron Ore Circuit Hackathon innovation prize. Professional roles include IEEE Western Australia Section committee membership, conference chair positions, and peer review for top journals. She actively mentors students pursuing Honours, Master's, or PhD projects in her areas of expertise.

• Applied Statistics
• Big Data
• Economic Models and Forecasting
• +۲ مورد دیگر

Prof Terry O'Neill serves as Executive Dean at the Bond Business School , Bond University, while also holding the title of Professor and Director at the Centre for Data Analytics. His research spans applied statistics , big data analytics , and financial modeling , with over 80 publications (20% in A* journals). Grants: Lead investigator on five ARC Discovery/Linkage grants totaling $2.99M, including studies on retirement savings, climate change modeling, and financial crisis resilience. Research Trends: Focus on financial literacy , retirement economics , and myelopoiesis , bridging finance with computational biology through collaborations with his spouse Helen O'Neill. Academic Leadership: Established Bond University's University Centre in Actuarial and Financial Big Data Analytics to elevate institutional research prominence in data science.

Wenhua Zhaoمشاهده پروفایل

دانشیار

• Offshore Hydrodynamics
• Renewable Energy Technologies
• Wave-Structure Interactions
• +۱۲ مورد دیگر

Associate Professor Wenhua Zhao is a globally recognized expert in offshore hydrodynamics and renewable energy technologies at The University of Queensland , School of Civil Engineering. With over 110 publications and 30 million AUD in secured research funding, his work bridges theoretical and practical advancements in marine engineering. Research focuses on Clean Energy , Artificial Intelligence , and Climate Change , specifically floating wind energy, floating solar, offshore aquaculture, and green hydrogen production. His 15 most recent articles (2024-2025) emphasize wave-structure interactions, gap resonance dynamics, and AI-driven wave prediction, published in top journals like Journal of Fluid Mechanics and Ocean Engineering . Scientific awards include the prestigious ARC Future Fellowship (2024-2028) and DECRA Fellowship (2019-2022) , recognizing his contributions to academia and industry. He teaches the 'Design of Offshore Energy Systems' course , training hundreds of students in coastal and ocean engineering, and serves as Deputy Editor for Ocean Engineering and Associate Editor for ASME's Journal of OMAE . Available for research supervision, Zhao actively collaborates with editorial boards of Applied Ocean Research and other Q1 journals.

Lasantha Meegahapolaمشاهده پروفایل

دانشیار

• Power System Stability
• Renewable Energy Integration
• Microgrid Modelling and Control
• +۴ مورد دیگر

Associate Professor Lasantha Meegahapola is a Deputy Head of Department (Teaching & Learning) at RMIT University's School of Engineering in Melbourne, Australia. He holds an IEEE Senior Membership and serves as an Associate Editor for several prestigious journals, including IEEE Transactions on Power Systems and IET Renewable Power Generation. His research focuses on Power System Stability with Renewable Integration, Microgrid Control, and Smart Grid Technologies, addressing challenges like voltage stability, inverter-based grid dynamics, and renewable energy penetration. He has supervised 16 PhD students to completion and published over 200 articles. Key contributions include identifying stability issues in microgrids and advancing grid-forming inverter control strategies. His work aligns with UN Sustainable Development Goals 7 (Clean Energy), 9 (Infrastructure), and 13 (Climate Action). He is actively involved in IEEE committees, including the PSDP Task Force on Microgrid Stability. Teaching roles include Programme Manager for the Bachelor of Electrical Engineering (HK) and Subject Coordinator for Power System Analysis and Control courses. Collaborations span industry and international research institutions, emphasizing real-world applications of his research in power systems and renewable energy integration.

Jiwon Kimمشاهده پروفایل

دانشیار

• Transport Engineering
• Artificial Intelligence
• Machine Learning
• +۶ مورد دیگر

Associate Professor Jiwon Kim is a leading researcher in Transport Engineering at the University of Queensland's School of Civil Engineering. She serves as Director of Higher Degree by Research and was a DECRA Fellow from 2019-2022. Holding degrees from Korea University and Northwestern University, she specializes in AI/ML applications for transportation systems. PhD, Northwestern University BS & MS, Korea University Her research focuses on Artificial Intelligence and Machine Learning applications in transportation, including: Deep learning for traffic management Reinforcement learning in mixed traffic environments Multi-agent systems for urban mobility optimization Spatiotemporal trajectory analysis Recent publications demonstrate expertise in: Eco-driving strategies Traffic incident prediction Queue length estimation Crash risk modeling Scientific recognition includes: ARC DECRA Fellowship (2019-2022) She supervises doctoral students in: Transportation data analytics Autonomous vehicle systems Intelligent traffic management Current projects explore real-time traffic monitoring, synthetic mobility data generation, and connected vehicle technologies.