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Xuan LiangView profile

Lecturer

• Spatial Modelling
• Panel Data
• Nonparametric Statistics
• +3 more

Xuan Liang is a Lecturer in Statistics at the Research School of Finance, Actuarial Studies and Statistics (RSFAS), Australian National University. With a PhD from Peking University and postdoctoral experience at Monash University, his research focuses on spatial statistics, nonparametric modeling, and environmental data analysis. Education: PhD in Statistics (Peking University, 2017), BSc in Statistics (Zhejiang University, 2012) His work addresses methodological challenges in spatial panel data analysis, network modeling, and air pollution quantification. He has developed novel techniques for meteorological confounder adjustment in air quality assessments and contributed to distributed data analysis methods. Recent research trends include: Advancing quasi-score matching for spatial econometric models Improving subbagging algorithms for big data Creating robust distributed data aggregation frameworks Refining spatial autoregressive panel data methodologies Scientific contributions include: ANU Vice-Chancellor’s Citation for Outstanding Contribution to Student Learning (Early Career), 2022 CBE Teaching Commendation for Outstanding Teaching, 2020 Co-development of the ggmatplot R package for matrix visualization Co-inventor of Chinese patent 201811183512.0 for air quality assessment He teaches advanced courses in time series analysis, regression modeling, and mathematical statistics at ANU, while maintaining active research collaborations in econometrics and environmental statistics.

Lindell BromhamView profile

Professor

• Evolutionary Biology
• Cultural Evolution
• Linguistics
• +3 more

Lindell Bromham is a Professor at the Research School of Biology , Australian National University, focusing on evolutionary biology, cultural evolution, and interdisciplinary research. Their work spans genomic mutation rates to global linguistic diversity, with notable projects on language endangerment and Galton’s problem in cross-cultural studies. Broad research themes: evolutionary biology, cultural evolution, macroecology, linguistics Key contributions: interdisciplinary funding disparities, language evolution models, parasite-culture interactions Recent articles emphasize language endangerment risk factors, methodological innovations in cross-cultural analysis, and population size effects on language evolution. Awards include Eureka Prize Finalist (2021) and media recognition in Nature and New Scientist . Supervises students in evolutionary and linguistic research.

• Complex networks
• Industrial electronics
• AI and machine learning
• +4 more

Associate Professor Fengling Han is affiliated with RMIT University's School of Computing Technologies in Melbourne, Australia. He holds the rank of Associate Professor since January 2022. His research focuses on complex networks, industrial electronics, AI/machine learning, and network security. Notable contributions include steganography frameworks for healthcare data, sliding mode control for energy systems, and blockchain applications in surveillance and voting systems. His work spans interdisciplinary areas such as renewable energy integration, battery management systems, and privacy-preserving recommendation systems. He has supervised numerous projects, including AI-driven chatbots, medical imaging watermarking, and peer-to-peer energy trading systems. Han's service roles include conference reviewing and committee memberships in international conferences like IEEE and ISMST. Research Interests: His expertise spans electrical engineering, control systems, and AI applications. Key areas include battery management, cybersecurity, and smart manufacturing. Recent projects emphasize Industry 5.0 technologies, blockchain for data integrity, and deep learning for steganalysis. Teaching and Supervision: Teaches network security, data communication, and IT infrastructure. Current supervision includes AI-powered business modeling, medical imaging tampering detection, and renewable energy sharing systems. Over 14 research projects are documented, reflecting his interdisciplinary impact. Awards and Recognition: While specific awards are not listed, his extensive publications (over 150 outputs) and high citation counts (e.g., 119 citations for the Industry 5.0 survey) highlight his scholarly contributions.

• Graph Analytics
• Graph Mining
• Graph Algorithms
• +3 more

Dr. Lijun Chang is an Associate Professor in the School of Computer Science at the University of Sydney. He holds an ARC Future Fellowship (2019–2022) and an ARC DECRA Fellowship (2015–2017). Previously, he was at the University of New South Wales. His research focuses on graph analytics, mining, algorithms, and network science. He teaches courses like INFO5011 (Competitive Programming), COMP5313 (Large Scale Networks), and COMP9120 (Database Management Systems), and coaches the USYD Programming Competition Teams. Education: B.Eng. in Computer Science & Technology from Renmin University of China; PhD from the Chinese University of Hong Kong. Research highlights include scalable graph processing systems (e.g., ScaleG), densest subgraph detection, and graph similarity search. He leads projects funded by ARC grants such as 'Advanced Search of Cohesive Subgraphs in Big Graphs' (2018) and 'Directionality-Aware Cohesive Subgraph Search' (2022). His work emphasizes efficient algorithms for large-scale networks and graph databases. Awards : ARC Future Fellow, ARC DECRA Fellow Students : Yu KONG, Rashmika MATHTHAKA GAMAGE, Mouyi XU Labs/Teams : Focuses on graph algorithms and systems research, contributing to open-source tools and large-scale network analysis.

• Computer Networks
• Network Reliability
• Wireless Mesh Network
• +3 more

Dr. Sie Teng Soh is an Associate Professor at Curtin University's School of Electrical Engineering, Computing and Mathematical Sciences. With qualifications including a PhD from Louisiana State University, he specializes in computer networks, wireless systems, and algorithm design. Research focuses on: Network topology optimization for UAV systems Energy-efficient IoT task scheduling Reliable wireless communication protocols Game-theoretic network management Green computing in software-defined networks Publication trends show advancing work in UAV network optimization, with recent articles addressing max-min rate optimization, energy harvesting in IIoT, and machine learning approaches for coverage prediction. His research consistently addresses practical challenges in wireless network deployment under real-world constraints. Teaching areas include advanced courses in network reliability and traffic engineering. Professional service includes editorial roles for IEEE Transactions on Parallel and Distributed Systems and program committee memberships for major conferences including FAST and EuroSys.

• Differential Geometry
• Lorentzian Geometry
• Conformal Geometry
• +4 more

Associate Professor Thomas Leistner holds a position in Pure Mathematics at the University of Adelaide, within the School of Computer and Mathematical Sciences under the Faculty of Sciences, Engineering and Technology. He earned his PhD from Humboldt-University Berlin, followed by postdoctoral roles at Berlin and Adelaide before becoming an Assistant Professor at the University of Hamburg. His research focuses on differential geometry, particularly Lorentzian and conformal geometries, holonomy groups, and their applications in mathematical physics, general relativity, and string theory. His work explores curved spaces and spacetimes, their geometric properties, symmetries, and connections to differential equations. Notable areas include conformal transformations, holonomy classifications, and geometric structures like Walker metrics and Cahen-Wallach spaces. Recent publications emphasize Lorentzian homogeneous spaces, conformal Killing tensors, and completeness properties of compact manifolds. Leistner is eligible to supervise Masters and PhD students in Pure Mathematics, specializing in geometry and related fields. Though no specific grants or awards are listed, his research maintains an active focus on foundational geometric problems with interdisciplinary relevance.

Max SchleserView profile

Associate Professor

• Immersive Media
• Cinematic VR
• Interactive Filmmaking
• +12 more

Max Schleser is Associate Professor in Film and Television at Swinburne University of Technology, where he also conducts research with the Centre for Transformative Media Technologies (CTMT). He is an Adobe Creative Educator Innovator, Founder of the Mobile Innovation Network & Association (MINA), and Screening Director of the International Mobile Innovation Screening & Festival. His work spans academic, artistic, and community domains, with a focus on innovative screen practices. Max holds a PhD in Creative Arts from the University of Westminster, an MA in Art and Media Practice with Distinction, and a BA Hons. His research centers on immersive media, documentary film, and creative arts 4.0, particularly cinematic VR and interactive filmmaking. He investigates screen production, emerging media, and smartphone filmmaking as tools for community engagement, creative transformation, and transmedia storytelling. His recent publications reveal a strong trend toward integrating Generative AI into documentary and city film genres, exploring the 'AI eye' and computational non-fiction. He also investigates novel immersive production methods in VR, HyFlex pedagogies in film education, and collaborative mobile storytelling through workshops. His work consistently bridges creative practice with theoretical inquiry. Best Cinematography, International Cell Phone Cinema - Cannes (2025) Best Mobile Film, Nitiin International Film Festival (2024) Dean's Award for Teaching and Research, Swinburne University (2023) International Visiting Research Scholar, Auckland University of Technology (2024) Early Career Award - Research, Massey University (2012) Max leads multiple research grants focused on health storytelling, intergenerational connection, AI in screen industries, and sustainability education. He supervises creative projects and has conducted digital storytelling workshops for cultural institutes and government bodies worldwide. As founder of MINA and director of major festivals, he leads international networks in mobile media innovation. He also serves on the editorial board of the Media Practice and Education journal and has co-edited several key volumes on mobile media and storytelling.

James SalamyView profile

Lecturer

• Photonics
• Optical Engineering
• Laser Technology
• +3 more

James Salamy is a part-time Lecturer in the Department of Electrical and Computer Systems Engineering at the College of Engineering, Monash University. He actively contributes to both research and teaching initiatives, with a focus on photonics and engineering education. His work bridges technical innovation with pedagogical advancements, ensuring holistic development of educational strategies. Current Affiliation: Monash University, College of Engineering Academic Role: Lecturer Status: Part-time faculty member His research spans two primary domains: photonics and engineering education . In photonics, he investigates laser stabilization techniques using microring resonators for dense wavelength-division multiplexing (WDM) systems, aiming to improve optical network scalability and reliability. In education, he explores factors influencing student success, including mental health literacy, onboarding experiences, and the role of failure in academic growth. Additionally, he has contributed to cloud-based machine learning infrastructure optimization. Recent research trends emphasize interdisciplinary collaboration, particularly between Monash University and the University of Warwick, focusing on scalable automated feedback systems for cross-campus education. His technical work on self-injection locking mechanisms has implications for temperature-stable laser systems, while his educational studies address equity and pedagogical innovation. While no scientific awards are documented in the provided material, his 2024-2025 projects include research on Research Cross Campus Peer Instructions and scalable feedback systems, demonstrating his commitment to enhancing engagement in information engineering education.

• Automated reasoning
• Formal methods
• Autonomous systems
• +5 more

Zhe Hou is a Senior Lecturer at the School of Information and Communication Technology , Griffith University, Australia. His academic journey includes a PhD in automated reasoning for separation logic from the Australian National University (2015) and prior research roles at Nanyang Technological University, Singapore (2015-2017). He joined Griffith University in 2017 and became permanent faculty in late 2019. Research Interests : Formal methods for software verification Automated reasoning with logical frameworks Blockchain technology and security Quantum computing verification Integration of LLMs with rigorous reasoning Sports analytics via model checking Recent Publications demonstrate expertise in neural-symbolic reasoning, blockchain security, quantum SAT solvers, and runtime verification frameworks. His work combines formal logic with machine learning for applications in cybersecurity and AI trustworthiness. Scientific Awards : ACM SIGSOFT Distinguished Paper Award (2025) Supervision Roles : Principal/Associate Supervisor for 6+ doctoral projects in blockchain security, AI verification, and network security. Professional Activities : Editor for Springer-Nature and Formal Aspects of Computing special issues, conference chair for ICFEM, ICECCS, and ISACE symposia.

• Learning Theory
• Neural Networks
• Deep Learning
• +6 more

Professor Dingxuan Zhou is a distinguished academic serving as Professor and Head of School of Mathematics and Statistics at The University of Sydney, joining the institution on August 29, 2022. He is also a member of The Net Zero Institute and has held significant editorial positions, including editor-in-chief of the journal "Analysis and Application" of "Mathematical Foundations of Computing" and serving on the editorial boards of over ten international journals. Educational Background: BSc in Mathematics from Zhejiang University, China (1988) PhD in Mathematics from Zhejiang University, China (1991) Professor Zhou's research spans learning theory, neural networks, wavelet analysis, and approximation theory, with his current focus on the theory of deep learning. His work aligns with the Faculty of Science Research Strengths in Complex Systems, Precision and Digital Health, Data and Decisions, and National Security. His research demonstrates a consistent progression from foundational mathematical theory to cutting-edge applications in machine learning and artificial intelligence, with particular emphasis on understanding the theoretical underpinnings of neural networks and deep learning systems. His extensive publication record reveals a strong trend toward distributed learning frameworks, approximation theory for neural networks, and the mathematical foundations of deep learning. Recent work focuses on federated learning, transformers, physics-informed neural networks, and the theoretical analysis of over-parameterized networks, reflecting the evolving landscape of machine learning research with increasing emphasis on theoretical guarantees and practical applications. Scientific Awards: Humboldt Research Fellowship (1993) Fund for Distinguished Young Scholars from the National Science Foundation of China (2005) Highly-cited Researcher by Thomson Reuters/Clarivate Analytics (2014-17) World's Top 2% Scientist by Stanford University (2021, 2022, 2023) Professor Zhou has demonstrated exceptional leadership in research and mentorship, having conducted over 40 research grants as Principal Investigator, supervised more than 20 PhD students, and co-organized over 20 international conferences. His collaborative approach is evident in his extensive co-authorship network across multiple institutions globally. He has also served in significant administrative roles including Head of Department of Mathematics (2006-12), Associate Dean of School of Data Science (2018-22), and Director of the Liu Bie Ju Centre for Mathematical Sciences (2019-22) at City University of Hong Kong.

• Belt Conveyor Systems
• Bulk Solids Handling
• Energy Efficiency in Material Transportation
• +5 more

Professor Craig Wheeler is a distinguished academic in the School of Engineering at the University of Newcastle, specializing in Mechanical Engineering with a focus on bulk solids handling and belt conveyor technology. As Associate Director of the Centre for Bulk Solids and Particulate Technologies and Deputy Chairman for the Australian Society for Bulk Solid Handling, he has established the university as a global leader in fundamental and applied research within this field. Wheeler's research interests primarily center on reducing the energy intensity and environmental impact of ore and mineral transportation globally. His work develops novel theoretical approaches to model and optimize belt conveyor and bulk handling systems, with significant contributions in energy-efficient transportation, dust emission control, and innovative conveying technologies like the Rail Conveyor system. His research bridges fundamental computational techniques with practical industrial applications, addressing real-world challenges in bulk material handling. His extensive publication record demonstrates trends toward increasingly sophisticated modeling techniques, combining continuum mechanics, discrete element methods, and computational fluid dynamics to solve complex problems in bulk material flow and energy consumption. Recent work shows particular emphasis on large-diameter idler rollers for energy savings, rail-running conveyor systems, and advanced dust control methodologies. 2023 Engineers Australia - Australian Society for Bulk Solids Handling 2017 Significant Contributions to Engineers Australia's Warman Design and Build Competition (Weir Minerals) 2017 Australian Council of Engineering Deans National Award for Engineering Education Excellence 2016 Innovative Technology Award (Australian Bulk Handling) 2010 Rising Star Award (Newcastle Innovation, The University of Newcastle) 2009 Pro-Vice Chancellor's Award for Research Excellence 2006 Best Research and Development Project (Australian Bulk Handling Review) 2000 A.W. Roberts Award (Australian Society for Bulk Solids Handling) Professor Wheeler has successfully led numerous Linkage Projects with major companies including Rio Tinto, Veyance Technologies, and Laing O'Rourke, securing significant cash and in-kind contributions for research projects. His industrial consulting experience, built on a 10-year engineering career with BHP, provides valuable insights that bridge fundamental research with practical applications. He actively supervises research students and contributes to professional development courses both within Australia and internationally. As a key member of the Centre for Bulk Solids and Particulate Technologies in association with TUNRA Bulk Solids, Wheeler leads research teams focused on developing eco-friendly conveying solutions. His work has resulted in new licensed technologies, internationally recognized testing methods, design guidelines, and Australian Standards that have transformed industry practices worldwide.

Mingyan LiView profile

Research Fellow

• Imaging
• Sensing
• Biomedical Engineering
• +3 more

Dr. Mingyan Li is an Adjunct Research Fellow at The University of Queensland's School of Electrical Engineering and Computer Science. Their research focuses on advanced imaging and sensing technologies with applications in biomedical engineering, particularly in MRI system development, RF coil design, and medical signal processing. They hold a PhD from The University of Queensland (2015). Research interests include high-field MRI systems, rotating RF coil technologies, MRI-Linac integration, and electrical properties tomography (EPT). Key contributions include innovations in MRI-Linac distortion correction, RF shielding for SAR reduction, and deep learning approaches for cardiac arrhythmia classification. Publications span MRI hardware optimization, image reconstruction algorithms, and biomedical signal analysis. Collaborations include work on metamaterial-inspired RF shielding and multi-modal antenna systems for body MRI.

Sirojan TharmakulasingamView profile

Lecturer

• Machine Learning
• Artificial Intelligence
• Data Science
• +6 more

Dr. Sirojan Tharmakulasingam serves as a Lecturer and Research and Development Coordinator at the Signals, Information & Machine Intelligence lab within the Faculty of Engineering at the University of New South Wales (UNSW) Sydney. His work bridges theoretical machine learning with practical applications in edge computing and high-performance systems. His research spans multiple cutting-edge domains including machine learning, artificial intelligence, data science, edge computing, and high-performance computing. Dr. Tharmakulasingam specializes in developing next-generation inference models by integrating machine learning, signal processing, mathematical modeling, and computing across diverse data types including images, video, audio, and quantum molecular data. His work has significant implications for scientific computing, telecommunications, and healthcare applications. Analysis of his publication trends reveals a strong focus on practical AI implementations, with increasing emphasis on edge computing solutions, quantum applications, and energy-efficient models. His recent work demonstrates progression from foundational machine learning techniques toward specialized applications in scientific computing and real-time systems. Dr. Tharmakulasingam holds a Doctor of Philosophy from UNSW Sydney and a Bachelor of Science of Engineering from the University of Moratuwa in Sri Lanka. His academic journey reflects a strong foundation in both theoretical and applied engineering principles. As Research and Development Coordinator for the Signals, Information & Machine Intelligence lab, he oversees critical research infrastructure and collaborations. His work location in Room 447 of the EE&T Building (G17) places him at the heart of UNSW's engineering research ecosystem, with access to the Mark Wainwright Analytical Centre's extensive facilities.

Tom KompasView profile

Professor

• Bioeconomic Modelling
• Natural Resource Economics
• Environmental Economics
• +3 more

Tom Kompas is an Honorary Professor of Environmental Economics and Biosecurity at the University of Melbourne, affiliated with the Crawford School of Public Policy. He holds leadership roles, including Editor-in-Chief of Asia and the Pacific Policy Studies and former Director of the Crawford School (2009–2015). His research focuses on bioeconomic modelling, climate change impacts, and natural resource economics, with contributions to global biosecurity policy and fisheries management. He has advised the Australian Department of Agriculture’s Eminent Scientists Group since 2010. His research integrates large-scale economic models to address complex environmental challenges, such as optimal biosecurity surveillance and climate policy. Key publications include analyses of foot-and-mouth disease management, fisheries sustainability, and the economic implications of climate change. He has received prestigious awards, including the CSIRO Medal for Research Achievement (2009) and the Eureka Prize (2011). Educations: Not explicitly listed, but extensive academic career implies advanced degrees in economics or related fields. His work bridges theory and practice, addressing real-world issues like water resource management, invasive species control, and sustainable development. He has served on international advisory boards and contributed to policy frameworks for marine reserves and fisheries governance. Grants & Advising: His research has been widely funded, though specific grants are not detailed here. Advised numerous collaborative projects with government and academic institutions. Tom’s teaching excellence is recognized through multiple awards, reflecting his commitment to student learning and pedagogical innovation.

• Data Management
• Social Media Analysis
• User Behavior Analysis
• +4 more

Dr. Ying Zhou is a Lecturer in the School of Computer Science at The University of Sydney. She holds a BSc and MEng from Nanjing University (1997) and a PhD from the School of Computing at the National University of Singapore (2003). Her research focuses on human-centred data management, including large-scale data storage, user behavior analysis, and improving image query systems. Current projects include mining socially tagged images and accountability mechanisms for multitenant cloud platforms. Teaching responsibilities include courses on Advanced Data Models (COMP5338), e-Commerce Technology (COMP5347), and Cloud Computing (COMP5349). She collaborates with industry partners like Amazon and IBM, and advises four research students. She is a member of the Sydney Southeast Asia Centre. Her publication record spans 20 years, with recent work emphasizing machine learning applications, big data systems, and cloud security. Research trends include optimizing distributed computing frameworks (Hadoop/Spark/Flink), adversarial machine learning, and trustworthy database systems. Earlier work focused on social network analysis, web communities, and blogosphere dynamics. Current research projects address both technical challenges (e.g., efficient image search, cloud platform accountability) and applied solutions for leveraging social media data. Her lab activities involve interdisciplinary collaboration between computer science and information systems domains.