جست‌وجوی استاد و پژوهشگر

James McDonaldمشاهده پروفایل

مدرس

• Water Treatment
• Membrane Technology
• Trace Organic Contaminants
• +۶ مورد دیگر

Dr. James McDonald serves as a Lecturer and Research Fellow at the University of New South Wales (UNSW) within the School of Civil and Environmental Engineering's Water Research Centre (WRC). His work at the Vallentine Annexe (H22), Room 102, Kensington Campus focuses on advancing water treatment technologies and understanding contaminant behavior in water systems. His position bridges academic instruction with cutting-edge research in environmental engineering. McDonald's research spans multiple critical areas in water science and engineering, with particular emphasis on trace organic contaminants, membrane technologies, and advanced water treatment processes. His work investigates the fate and removal of pharmaceuticals, personal care products, and industrial chemicals in various water treatment systems. He has made significant contributions to understanding disinfection by-product formation, chiral inversion of pharmaceuticals, and the development of novel membrane technologies for improved water purification. His research combines experimental approaches with computational modeling to address complex water quality challenges. Analysis of McDonald's recent publications reveals a strong focus on emerging contaminants, particularly PFAS compounds and chiral pharmaceuticals, and their behavior in water treatment systems. His work demonstrates expertise in both conventional and advanced water treatment technologies, with increasing emphasis on sustainable approaches including green infrastructure and novel membrane processes. The interdisciplinary nature of his research connects environmental engineering, chemistry, microbiology, and materials science to address contemporary water quality challenges. McDonald actively collaborates with researchers across multiple institutions and has contributed to numerous significant studies on water quality and treatment. His work supports the development of more effective water recycling systems and safer drinking water supplies. While specific grant information isn't detailed in the available text, his extensive publication record suggests sustained research funding in water treatment technologies and contaminant fate studies. As part of UNSW's Water Research Centre, McDonald contributes to one of Australia's leading water research facilities. The WRC provides a collaborative environment for addressing complex water challenges through interdisciplinary research that integrates engineering, chemistry, and environmental science perspectives.

Jiyuan Tuمشاهده پروفایل

استاد

• Computational Fluid Dynamics (CFD)
• Multiphase Flows
• Thermal Sciences
• +۴ مورد دیگر

Professor Jiyuan Tu is a Professor in the Department of Mechanical and Automotive Engineering at RMIT University's School of Engineering. He specializes in computational fluid dynamics (CFD), multiphase flows, and their applications in renewable/nuclear energy, biomedical engineering, and built environment systems. His research has led to over 500 peer-reviewed articles, 9 books, and $10M+ in ARC grants. He has supervised over 50 postgraduate students and received prestigious awards such as the RMIT Research Excellence Award (2012) and Fulbright Senior Scholar Award (2008). Research interests include CFD modelling of bioaerosol transport, drug delivery systems, and thermal energy storage. He pioneered numerical models for multiphase flows, contributing to software implementations in industries. Notable works include books on CFD and multiphase flow analysis, and leadership in international conferences like COBEE 2018. He holds honorary professorships at Tsinghua University and is Editor-in-Chief of the Experimental and Computational of Multiphase Flow journal. Industry experience includes roles at ANSTO (1996-2001). Awards span fellowships from JSPS, KOSEF, and Fulbright programs. Grants include ARC Discovery, Linkage, and LIEF projects. His work ranks him among the world’s top researchers in pebble bed reactors and airborne infection studies (SciVal 2016-2025).

• Respiratory Drug Delivery
• Pulmonary Drug Delivery
• Inhalation Aerosols
• +۵ مورد دیگر

Professor Hak-Kim Chan of the Sydney Pharmacy School at the University of Sydney is a world-renowned expert in respiratory drug delivery, particularly pulmonary aerosols and inhalation therapies. With over 480 publications and 17,580+ Google Scholar citations, he has pioneered advancements in powder formulation , in silico modeling , and clinical applications of inhalation technologies. His work includes the development of FDA-approved diagnostics like Aridol™ (inhaled mannitol for asthma) and Bronchitol™ (for cystic fibrosis), and groundbreaking research on inhaled bacteriophage therapy to combat antibiotic-resistant respiratory infections. Education: BPharm (University Medal, 1983), PhD (1988), DSc (2009) from University of Sydney Professional Experience: Postdoc at University of Minnesota (1988–89), Scientist at Genentech Inc. (1992–95) Leadership: Executive Editor of Advanced Drug Delivery Reviews , Fellow of AAPS and RACI His research spans in vitro production methods, computational modeling of inhaler design, and in vivo imaging of aerosol deposition. Current projects focus on nanomedicine , phage therapy , and combating superbugs via inhalation routes. He has secured significant recognition for his work, including NHMRC case studies highlighting public health impacts. Professor Chan has supervised numerous researchers, including PhD student Grace YAU studying pulmonary probiotic delivery . His team's 10 patents (7 as first inventor) reflect practical innovations in dry powder inhalers , antimicrobial formulations , and drug stabilization technologies.

Vishnu Pareekمشاهده پروفایل

استاد

• Multiphase Flow Modelling
• Computational Fluid Dynamics
• Flow Assurance
• +۷ مورد دیگر

Professor Vishnu Pareek is the John Curtin Distinguished Professor at Curtin University, leading the Western Australian School of Mines (WASM) within the Faculty of Science and Engineering. He has held academic roles including Dean of Engineering, Head of School, and various professorships since 2002. His research focuses on multiphase flow modeling, computational fluid dynamics, and reactor engineering, with applications in energy and chemical processes. He holds a BE (Hons) from MNIT, MTech from IIT Delhi, and a PhD from UNSW. Key research interests include LNG process modeling, erosion modeling, and granular flow dynamics. He has authored over 200 peer-reviewed publications, with recent work emphasizing structured packing design, biomass gasification, and additive manufacturing for process intensification. Notable projects include CFD-ANN hybrid models for fluidized beds and experimental studies on 3D-printed structured packings. His expertise spans industrial collaborations in LNG safety, fluid catalytic cracking, and biofuel production. Teaching areas include chemical engineering fundamentals and process systems engineering. He advises on energy policy and leads research teams in multiphase flow and reactor design.

• Geomechanics
• Granular Materials
• Particulate Systems
• +۶ مورد دیگر

Professor Itai Einav is a renowned academic in civil engineering and geomechanics at The University of Sydney. He serves as Director of SciGEM (Science of Granular and Multiphase Energy Materials) and holds an honorary professorship at University College London. His research focuses on granular materials, particulate systems, and geomechanics, with particular emphasis on breakage mechanics and applications in mining, heat transfer, and fault dynamics. He advises PhD students on topics like robotic navigation inspired by earthworms and soil mechanics. Einav's work bridges fundamental physics and engineering applications, leveraging advanced imaging techniques (e.g., X-ray tomography) and computational models. He is affiliated with The Net Zero Institute and collaborates globally on projects like granular flow dynamics and porous media behavior. Notable contributions include the 2007 development of breakage mechanics theory and innovations in granular rheology and fault modeling.

• Geotechnical Engineering
• Geomechanics
• Numerical Modelling
• +۳ مورد دیگر

Dr. Xuzhen He is a Senior Lecturer at the School of Civil and Environmental Engineering, University of Technology Sydney (UTS). He holds a BSc from Tsinghua University and a PhD from the University of Cambridge, where he received the John Winbolt Prize (2015). His research focuses on geotechnics, geomechanics, and numerical methods, with an emphasis on AI integration. Notable contributions include studies on soil erosion, particle segregation, and tunnel engineering. He leads projects funded by ARC, including DECRA (2021) and a Discovery grant (2023). His work bridges experimental and computational approaches, addressing challenges in geotechnical infrastructure and environmental stability. Education: Bachelor of Science, Tsinghua University, China PhD in Civil Engineering, University of Cambridge, UK Research Interests: AI-driven geotechnical analysis (slope stability, tunnelling) Multiscale geomechanical modelling (hypoplasticity, multiphase systems) Numerical methods (DEM, SPH, material point method) Awards: ARC DECRA (2021) John Winbolt Prize (2015) Grants: "Modernise geotechnical investigation and analysis with machine learning" (ARC DP230100678) "Multiscale modelling of fluid–particle transport in porous media" (ARC DE220100763) Labs/Teams: Member of UTS Transport Research Centre (TRC) Associate member of Centre for Advanced Modelling and Geospatial lnformation Systems (CAMGIS)

• Materials Science
• Geotechnical Engineering
• X-ray Computed Tomography
• +۵ مورد دیگر

Dr Yulai Zhang is a researcher in the Department of Materials Physics at the Australian National University . His work focuses on advanced imaging techniques for material and geological analysis. Expertise: X-ray micro-computed tomography (μCT), pore-scale and multiscale modeling, coal seam and ore characterization Collaborations: International partnerships in coal bed methane, mineral liberation, and rock failure analysis His research applies 4D/X-ray tomography to study dynamic processes in copper ores, shale, and coal, including fragmentation, diffusion, and fracture networks. Recent publications highlight innovations in super-resolution imaging , feature extraction methods , and in-situ studies of mineral behavior under stress. Dr Zhang actively supervises students and contributes to ore beneficiation, CO2 geo-sequestration, and unconventional reservoir characterization. Collaborative projects involve institutions in Australia and Indonesia, with a focus on digital rock physics and microstructural evolution .

• Fluid Dynamics
• Particle-Laden Flows
• Machine Learning
• +۴ مورد دیگر

Dr Xinchen Zhang is a Grant-Funded Researcher (A) at the University of Adelaide's Department of Mechanical Engineering within the School of Electrical and Mechanical Engineering. His research focuses on integrating machine learning with computational fluid dynamics (CFD) to enhance predictive capabilities for multiphase flow solutions, particularly in sustainable energy applications like decarbonization technologies. He holds a PhD (2022) with a Dean's Commendation for Doctoral Thesis Excellence, emphasizing fluid and particle dynamics in particle-laden flows. His work addresses challenges in net-zero industrial processes such as limestone calcination and hydrogen production via methane pyrolysis, leveraging advanced CFD and ML-augmented methodologies. Key research areas include turbulence modeling, particle dispersion in jets, and flow regime analysis in horizontal particle-laden pipe systems. He is eligible to supervise Masters and PhD students as a co-supervisor. Dr Zhang's publications span 2018–2024, with recent trends focusing on physics-informed machine learning for turbulence modeling and multiphase flow optimization. His contributions advance computational efficiency and accuracy in predicting complex fluid-particle interactions.

Bikram Banerjeeمشاهده پروفایل

مدرس

• Remote Sensing
• Geospatial Science
• Environmental Monitoring
• +۳ مورد دیگر

Dr. Bikram Banerjee is a Lecturer in Remote Sensing and Geospatial Science at the University of Southern Queensland, affiliated with the School of Surveying and Built Environment. He holds a PhD from UNSW, MTech from IIRS NRSA, and BTech from West Bengal University of Technology. His research focuses on geospatial technologies, machine learning applications in environmental monitoring, and precision agriculture. He is associated with the Centre for Agricultural Engineering, Centre for Crop Health, and Centre for Sustainable Agricultural Systems. Key research interests include UAV-based remote sensing for mine spoil characterization, hyperspectral imaging for crop phenotyping, and integrating IoT/ML for agricultural solutions. His work bridges environmental science, geotechnical engineering, and agricultural technology. Recent publications explore coal spoil analysis, mine safety automation, and vegetation health monitoring using advanced sensor technologies. Dr. Banerjee has supervised doctoral research on mobile laser scanning for underground mines, UAV-LiDAR applications, and proximal sensing for crop phenotyping. His research outputs have garnered over 2,327 views and 1,160 downloads, reflecting significant impact in geospatial and agricultural domains. He actively contributes to interdisciplinary projects addressing environmental sustainability and resource management challenges.

Hongsheng Huمشاهده پروفایل

مدرس

• Data Privacy
• Machine Unlearning
• Trustworthy Machine Learning
• +۵ مورد دیگر

Dr. Hongsheng Hu is currently a Lecturer in the School of Information and Physical Sciences at the University of Newcastle, Australia, specializing in the Data Science and Statistics focus area. Prior to this position, he served as a Postdoc Research Fellow at CSIRO's Data61 from October 2022 to August 2024. His academic journey includes a Doctor of Philosophy in Computer Systems Engineering from the University of Auckland in New Zealand, establishing his foundation in advanced computing systems. Dr. Hu's research centers on enhancing the trustworthiness of machine learning systems, with particular emphasis on identifying critical privacy vulnerabilities within machine learning models and developing robust defensive strategies. His work spans several key domains including adversarial machine learning (30% focus), statistical data science (30% focus), and data and information privacy (40% focus). He investigates membership inference attacks, machine unlearning techniques, and privacy-preserving mechanisms in federated learning environments. His research addresses fundamental challenges in AI security, exploring how machine learning models can be compromised through sophisticated privacy attacks and developing methods to mitigate these vulnerabilities while maintaining model utility. Analysis of Dr. Hu's publication record reveals a strong research trajectory focused on machine learning security and privacy. His work consistently addresses vulnerabilities in machine learning systems, particularly examining membership inference attacks, machine unlearning mechanisms, and privacy-preserving techniques in federated learning. The research spans top-tier venues including IEEE Security & Privacy, USENIX Security, NDSS, NeurIPS, IJCAI, AAAI, and WWW, demonstrating both technical depth and recognition by the research community. His publications show an evolving focus from foundational privacy attacks to developing more sophisticated unlearning techniques and robust defense mechanisms, with increasing citation counts indicating growing impact in the field. Active Program Committee member for USENIX Security, NDSS, ICLR, IJCAI, WWW, ICDM, ECML, and PKDD Invited reviewer for IEEE Transactions on Information Forensics and Security (TIFS), IEEE Transactions on Dependable and Secure Computing (TDSC), IEEE Transactions on Pattern Analysis and Machine Intelligence (IPAMI), and ACM Computing Surveys (CSUR) Dr. Hu currently serves as Course Coordinator for STAT6020 and STAT2020 Predictive Analytics at the University of Newcastle. As an academic supervisor, he co-supervises one PhD student working on 'Identifying and Mitigating Vulnerability in Recommender Systems' at Macquarie University. His research collaborations span multiple countries, with significant publication counts in Australia (18), China (12), New Zealand (10), and the United States (7), reflecting an active international research network focused on AI security challenges.

Tilak Chandratillekeمشاهده پروفایل

استاد

• Computational Fluid Dynamics
• Heat and Mass Transfer
• Thermal Design
• +۳ مورد دیگر

Professor Tilak Chandratilleke is a faculty member in the School of Civil and Mechanical Engineering at Curtin University, part of the Faculty of Science and Engineering. He holds a PhD from the University of Cambridge and has extensive post-nominals including MIEAust, CPEng, and MASME. His research focuses on advanced thermal engineering, computational fluid dynamics (CFD), and heat transfer optimization. Key areas of interest include thermal energy storage systems, fluid flow in curved ducts, and thermal design for industrial applications. He also serves in the Office of the Provost, contributing to academic governance. Research Interests: - Computational Fluid Dynamics (CFD) modeling of complex thermal systems. - Heat and mass transfer in energy storage and manufacturing processes. - Design and analysis of heat exchangers and thermal recuperators. - Fluid dynamics in curved geometries and secondary vortex structures. - Applications in renewable energy systems and advanced manufacturing. Selected Publications (2022–2010): - Investigated high-temperature thermal energy storage using CaCO₃/Al₂O₃ reactors (2022). - Developed numerical models for metal hydride thermal storage systems (2021). - Analyzed boiling heat transfer in curved ducts and laser-assisted machining thermal effects (2020–2019). - Advanced CFD methodologies for convective boiling and turbulent flow modeling (2018–2016). - Pioneered studies on Dean vortices and microfluidic heat enhancement (2011–2010). Teaching: - Thermodynamics and Heat Transfer. - Fluid Mechanics and Engineering Applications. Labs/Teams: - Involved in Curtin’s thermal energy and advanced manufacturing research groups. - Collaborates with industry partners on renewable energy and thermal system optimization projects.

• Geomaterials Physics
• Soft Matter
• Structured Materials
• +۱۳ مورد دیگر

Dr. Nicolas Francois is an Associate Professor in the Department of Materials Physics at Australian National University (ANU), specializing in experimental geomaterials physics, soft matter, and fluid hydrodynamics. He leads the X-ray Tomography and Applications Research Group, combining curiosity-driven and applied research in out-of-equilibrium systems. ARC Industry Fellow (2024-2030): Improving Australian iron ore comminution for green steel production ARC DECRA Fellow (2016-2018): Biofilms in two-dimensional turbulent flows His research spans fundamental questions in: Fragmentation of solid materials Autonomous devices powered by chaotic flows Hydrodynamic waves Stochastic thermodynamics Granular matter Polymer rheology and applied areas in: Comminution of geomaterials Mechanics of fractured rocks Wave-energy conversion Environmental fluid mechanics Publications reveal a trajectory focused on X-ray tomography applications, granular dynamics, and turbulence-driven systems. He utilizes advanced imaging techniques to study material failure mechanisms and fluid-structure interactions, contributing to fields ranging from green steel production to biofilm dynamics. Current student projects and grants emphasize sustainable resource processing and fundamental fluid physics.

Tyson Govan Phillipsمشاهده پروفایل

مدرس ارشد

• Autonomous Systems
• Robotics
• Mining Automation
• +۴ مورد دیگر

Dr. Tyson Phillips serves as Senior Lecturer and Director of Teaching and Learning at The University of Queensland's School of Mechanical and Mining Engineering within the Faculty of Engineering, Architecture and Information Technology. He is an active Affiliate of the Future Autonomous Systems and Technologies research group, focusing on translating robotics innovations into practical mining applications. His academic leadership includes curriculum development for engineering programs and direct industry engagement with major mining equipment manufacturers. He earned his Doctor of Philosophy (PhD) from The University of Queensland in 2016, with thesis research centered on LiDAR-based perception systems for autonomous excavators. His doctoral work established foundational methods for object pose verification in mining contexts. Phillips' research specializes in robotics perception for extreme mining environments, developing LiDAR-centric solutions for autonomous equipment operation amid dust, fog, and unstructured terrain. Key contributions include evidential reasoning frameworks for uncertainty management, real-time pose estimation algorithms, and sensor fusion techniques for excavators and bulldozers. His work bridges theoretical computer vision with industrial deployment, targeting operational safety and efficiency in mineral extraction. Publication analysis reveals consistent focus on mining robotics since 2012, with recent works (2021-2024) emphasizing minimal-sensor configurations, probabilistic terrain mapping, and vibration-assisted gripper technology. His 14 scholarly outputs demonstrate evolution from sensor evaluation (2012-2015) toward integrated autonomy systems (2018-2024), predominantly in Journal of Field Robotics and Sensors . He actively supervises graduate researchers as Principal Advisor for a PhD on multimodal perception mapping and Associate Advisor for two PhD projects involving spreader systems and physics-informed neural networks. Completed supervision includes a 2024 PhD on bulldozer terrain mapping and a 2021 Master's on shovel/hopper interaction strategies. Research funding spans 14 projects from 2012-2026, including current Australian Coal Association Research Program support (2025-2026) and major Caterpillar Inc. collaborations for ERS self-protection and articulated truck automation. Phillips operates within The University of Queensland's Future Autonomous Systems and Technologies group, which develops field-deployable autonomy solutions for mining partners. This team conducts real-world testing of perception systems using Caterpillar and FMG operational sites as validation environments.

• Granular Mechanics
• Mechanical Engineering
• Neutron Diffraction
• +۴ مورد دیگر

Associate Professor Chris Wensrich is a faculty member in the School of Engineering at the University of Newcastle, specializing in Mechanical Engineering. He holds a PhD, Bachelor of Mathematics, and Bachelor of Engineering from the same university. His research focuses on granular mechanics, neutron diffraction, and strain tomography, with pioneering work in Bragg-edge transmission strain tomography and granular dynamics modeling. Wensrich has held visiting appointments at Clare Hall College, Cambridge University, and the Isaac Newton Institute for Mathematical Sciences in the UK. He currently serves as President of the Australian Neutron Beam User Group (ANBUG) and is a member of the ACNS Program Advisory Team at ANSTO. His expertise spans applied mechanics, computational modeling (DEM), and experimental techniques involving neutron diffraction. His research interests include granular material behavior, stress distribution measurement, and validation of computational models using neutron imaging. Notable contributions include studies on silo quaking dynamics, force chain analysis in granular assemblies, and residual stress characterization in additive manufacturing. Wensrich has supervised 11 PhD students and secured over $5.4M in grants, including ARC Discovery Projects and industry-linked initiatives. Publications span granular mechanics, strain tomography, and material characterization, with over 60 journal articles and 38 conference papers. His work bridges theoretical, computational, and experimental methods to advance understanding of particulate systems and engineering materials.

• Artificial Intelligence (AI)
• Machine Learning Algorithms
• AI Ethics
• +۴ مورد دیگر

Daswin De Silva is a Full Professor of AI and Analytics at La Trobe University, Australia, and Deputy Director of the Centre for Data Analytics and Cognition (CDAC). He also holds an Adjunct Professor position at Lulea University of Technology, Sweden. His expertise spans AI ethics, algorithm development, and applications in healthcare, energy, and education. He leads major initiatives like the La Trobe Energy AI Platform for net-zero emissions and the OptusU AI Micro-credentials program. Education: PhD in AI (Monash University, 2011). Awards include the Australian Awards for University Teaching (2021), Vice-Chancellor’s Teaching Award (2019), and Mid-Career Research Excellence Award (2018). Editor of five journals including IEEE Transactions on Industrial Informatics and Springer Discover AI. Research focuses on generative AI, ethical AI systems, and vector symbolic architectures. Recent work includes AI applications in healthcare diagnostics, energy efficiency, and smart cities. He has secured AU$14M in research funding and supervised 15 PhD completions with 10 current students. Leadership roles include Deputy Chair of La Trobe’s Research & Graduate Studies Committee and chairmanship of IEEE committees on Responsible AI and Web/Information Systems. Keynote speaker at global conferences like IEEE HSI, INDIN, and ETFA. Media engagements include ABC News, Forbes, and The Conversation.