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Vishnu PareekView profile

Professor

• Multiphase Flow Modelling
• Computational Fluid Dynamics
• Flow Assurance
• +7 more

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.

• Contact Mechanics
• Tribology
• Advanced Manufacturing
• +5 more

Hongtao Zhu is an Associate Professor at the University of Wollongong (UOW), Australia, affiliated with the School of Mechanical, Materials, Mechatronic and Biomedical Engineering. He holds a PhD from Northeastern University (China) and has held academic positions at UOW since 2002, progressing from Research Fellow (2002–2013) to Senior Lecturer/Lecturer (2013–2022) before becoming an Associate Professor in 2022. His research focuses on Contact Mechanics , Tribology , Advanced Manufacturing , and Computational Material Science . Notable contributions include studies on rail-wheel contact systems, high-entropy alloys, and friction reduction in manufacturing processes. He has authored/co-authored over 230 publications with an H-index of 38 (Scopus) and secured $2.2M in industry grants. Zhu leads the 'Wheel and Rail Contact System' research group, collaborating with major railway industries. He has supervised 22 PhD students (6 as principal supervisor) and currently oversees 5 PhD candidates. His professional roles include editorial board memberships for journals like Advances in Materials Science and Engineering and service as an assessor for funding bodies in Australia, Chile, Poland, and the Czech Republic. Key achievements include securing 5 ARC Discovery Projects, 3 ARC Linkage Projects, and industry collaborations with organizations like Sydney Trains and Comsteel. His work addresses critical challenges in rail infrastructure durability, material optimization, and sustainable manufacturing.

Wade SmithView profile

Senior Lecturer

• Dynamics, vibration and vibration control
• Signal processing
• Modelling and simulation
• +7 more

Dr. Wade Smith is a Senior Lecturer within the School of Mechanical and Manufacturing Engineering at the University of New South Wales. He is an active member of the WAVES research group (Wear, Aeroacoustics and Vibration in Engineering Systems) and conducts his research in the Tribology and Machine Condition Monitoring laboratory. His primary research interests include vibration-based diagnostics of rotating machinery, prognostics of rotating machinery, gear wear monitoring and prediction, simulation and modeling of rotating machines for diagnostic applications, and signal processing of machine vibration signatures using cyclostationarity. His work has significant applications in industrial machinery health monitoring and predictive maintenance systems. Dr. Smith's recent publications demonstrate a consistent focus on advanced diagnostic techniques for rotating machinery, with particular emphasis on gear systems and bearings. His research integrates traditional mechanical engineering principles with modern signal processing and machine learning approaches to develop more effective condition monitoring solutions. He actively supervises PhD and Masters students on projects related to gear diagnostics, wear monitoring, and vibration analysis. His current research projects include gear diagnostics in planetary gearboxes using internal sensors, gear wear monitoring and prediction, sliding contact-induced vibration studies, and transmission-error-based gear diagnostics. Dr. Smith's laboratory is equipped with specialized facilities including gearbox test rigs (both planetary and parallel configurations), a rolling element bearing test rig, an engine test rig, friction rig, tribometer, high-quality microscope, and extensive instrumentation for vibration analysis. His research has attracted collaborations with institutions including Queensland University of Technology, SpectraQuest (USA), Weir Minerals, University of Technology Sydney, RWTH Aachen University (Germany), and Safran.

• Medical Imaging
• Molecular Imaging
• Neuroscience
• +3 more

Steven Meikle is a Professor of Medical Imaging Physics and Head of the Imaging Physics Laboratory at the Brain and Mind Centre, University of Sydney. He also serves as Deputy Director (Preclinical) of Sydney Imaging and Deputy Director of the National Imaging Facility's Sydney node. His expertise spans advanced imaging technologies, with a focus on PET/SPECT instrumentation and molecular imaging. He holds a B.App.Sc.(Hons) from the University of Technology Sydney and a PhD from the University of New South Wales. Research focuses include developing novel PET systems like Open-field PET (for freely moving rodents) and Total Body PET, which enhance imaging sensitivity and enable real-time behavioral studies alongside brain function analysis. Collaborations include Tsinghua University (China) and UC Davis (USA). He leads projects on motion correction, quantitative imaging, and AI-driven analysis. Key achievements include over 180 peer-reviewed publications, editorial roles in Physics in Medicine and Biology , and leadership in professional societies. Awards include IEEE Senior Membership and Australian Institute of Physics Fellowship. Current student projects explore Total Body PET applications, motion correction, and radiopharmaceutical evaluation. Teaching roles include medical physics courses in diagnostic radiography and medical physics programs. He advises on imaging ethics, facility implementation, and translational research bridging basic science and clinical applications.

• Additive Manufacturing
• Micro Metal Forming
• Tribology
• +4 more

Dr Dongbin Wei is an Associate Professor at the School of Mechanical and Mechatronic Engineering , University of Technology Sydney (UTS), with a career spanning academia and industry. He holds a PhD in Materials Processing Engineering from the University of Science and Technology Beijing (2001) and academic appointments from 2005–2012 at the University of Wollongong (Research Fellow to Lecturer) and 2013–2017 at UTS (Senior Lecturer) before his promotion to Associate Professor in 2018. His research lies at the intersection of Mechanical Engineering , Manufacturing Engineering , and Materials Processing , focusing on: Ultrasonic Additive Manufacturing (UAM) Micro Metal Forming and Size Effects Tribology and Lubrication Numerical Simulations of Material Processing Composite Material Fabrication Key contributions include: Development of the Springback Path–Displacement Adjustment (SP-DA) method for stamping accuracy Advancements in femtosecond laser texturing for silicon wettability control Studies on nanolubrication in hot rolling Optimization of micro-deep drawing parameters He has secured competitive grants from the Australian Research Council (ARC) and industry partners like Weir Minerals Australia Ltd , including projects on: Revolutionizing mineral separation via additive manufacturing Super high-speed grinding technologies Mechanics of micro composite drill fabrication As a lead supervisor, he guided the 2022 thesis 'Creation and Validation of 3D Printable Mineral Separation Spiral' . His work bridges theoretical analysis, computational modeling (FEM/FEA), and practical validation in advanced manufacturing systems.

Christopher FerrieView profile

Associate Professor

• Quantum Estimation and Control
• Quantum Machine Learning
• Quantum Information Science
• +5 more

Christopher Ferrie is an Associate Professor at the University of Technology Sydney (UTS), where he is affiliated with the Faculty of Engineering and Information Technology and the Centre for Quantum Software and Information (QSI). His academic career spans quantum information science, machine learning, and scientific education, with a strong emphasis on both theoretical research and public engagement through science communication. Full-time faculty member at UTS Active researcher in quantum information science Director of the Centre for Quantum Software and Information Author of numerous scientific publications and popular science books Dr. Ferrie earned his PhD in Applied Mathematics from the Institute for Quantum Computing and University of Waterloo in Canada in 2012. His doctoral work focused on quantum information and laid the foundation for his subsequent research career in quantum computing and related fields. Dr. Ferrie's research interests span several interconnected domains within quantum information science. His primary focus is on quantum estimation and control, with particular emphasis on applying machine learning techniques to solve statistical problems in quantum information science. He investigates how quantum systems can be characterized, controlled, and optimized for practical applications. His work bridges theoretical quantum physics with practical implementations, exploring how quantum phenomena can be harnessed for computational advantage. Recent research directions include quantum machine learning, quantum neural networks, and quantum optimization algorithms, with applications ranging from quantum state tomography to solving combinatorial optimization problems. Analysis of Dr. Ferrie's recent publications reveals a strong focus on practical quantum computing challenges. His work consistently addresses the intersection of quantum information theory and machine learning, with particular emphasis on making quantum algorithms more efficient, interpretable, and robust against noise. A significant portion of his recent research explores variational quantum algorithms and their optimization, reflecting the current priorities in near-term quantum computing. His publications also demonstrate growing interest in quantum machine learning applications and the development of techniques for quantum error mitigation and characterization. Dr. Ferrie has secured multiple research grants supporting his work in quantum computing and related fields. His funded projects span quantum control, quantum probability, quantum machine learning, and statistical decision theory, reflecting the breadth of his research program. While specific major awards aren't detailed in the available information, his sustained funding and publication record indicate significant recognition within the quantum information science community. Dr. Ferrie is actively involved in research supervision and teaching, with current funding supporting multiple PhD students and postdoctoral researchers. His teaching responsibilities include courses on quantum computing, where he introduces students to the fundamentals of quantum information processing. His research group at the Centre for Quantum Software and Information focuses on developing novel quantum algorithms and exploring the practical implementation challenges of quantum computing. The Centre for Quantum Software and Information at UTS serves as the primary research environment for Dr. Ferrie's work. This center brings together researchers working on various aspects of quantum computing, from hardware development to algorithm design and applications. Dr. Ferrie's team within the center focuses specifically on quantum software development, quantum algorithm design, and the application of machine learning techniques to quantum information problems. The collaborative environment enables interdisciplinary research that bridges theoretical quantum physics with practical computing applications.

Kavan ModiView profile

Professor

• Quantum Information Theory
• Quantum Mechanics
• Quantum Physics
• +6 more

Kavan Modi is a Professor at the School of Physics and Astronomy, Monash University. His research focuses on quantum information theory applied to dynamics, metrology, computation, thermodynamics, and relativity. He leads the Monash Quantum Information Science (MonQIS) group and serves as Director of the Centre for Quantum Technology at Transport for NSW (2022–2024). Education: B.Sc. Engineering Physics (Embry-Riddle Aeronautical University, 2001), M.A. Physics (University of Texas at Austin, 2004), Ph.D. Physics (University of Texas at Austin, 2008). Postdoctoral positions included the Centre for Quantum Technologies (Singapore, 2008–2011) and Clarendon Lab, Oxford (2011–2013). Joined Monash in 2014. Research interests center on quantum dynamics, non-Markovian processes, and their applications in quantum computing and information science. Projects include developing error correction codes, quantum algorithms for network analysis, and mitigating correlated noise in quantum systems. He has authored over 111 publications, with recent work emphasizing non-Markovian characterization, quantum process tomography, and topology-based quantum algorithms. Awards and grants include leadership in multiple Australian Research Council projects. Advising/Grants: Primary Chief Investigator in projects like 'Quantum Software Platform' (2023–2026) and 'Mitigating Correlated Noise in Quantum Machines' (2020–2021). Supervises graduate students and collaborates globally on quantum information science. Labs/Teams: MonQIS group focuses on foundational and applied quantum research, integrating theory and experimental collaborations.

Raphaël PhanView profile

Professor

• Security
• Cryptography
• Malicious AI
• +5 more

Prof Raphaël Phan is a Professor and Deputy Head of the School of IT at Monash University Malaysia. His expertise spans security, cryptography, malicious AI, emotion recognition, motion analysis, and generative AI. He has published over 220 papers and led significant projects including privacy-preserving data mining funded by UK MoD and Malaysian government grants exceeding RM4 million. He co-designed the BLAKE hash function (SHA-3 finalist) and has an h-index of 50. Education: PhD in Cryptography (Multimedia University, 2005), MEngSci (2001), BEng (Hons) Computer Engineering (1999). Research focuses on adversarial AI, brain networks, and secure systems. Current projects include Æmbience: emotion-aware virtual assistants using motion magnification. Supervised 15 PhD graduates and 19 current students. Professional affiliations: Chartered Engineer (IET, UK), HEA Fellow, Board of Engineers Malaysia. Recent work emphasizes causal bias detection in micro-expressions, brain tumor detection via advanced YOLOv8, and generative adversarial networks for medical imaging. His work bridges cybersecurity with neuroscience applications.

• Project Management
• Construction Engineering
• Sustainability in Construction
• +7 more

Dr. Michel Chaaya is a Senior Lecturer in Civil Engineering at the University of Sydney, with expertise in project management, construction engineering, and sustainability. He holds a PhD from the University of Sydney and is a Fellow of the Institution of Engineers Australia and the College of Leadership and Management. His research focuses on innovative project management methodologies, sustainable construction practices, and BIM implementation. Education: BE, ME(Res), PhD in Project Management and IT from the University of Sydney. Research Interests: Enhancing project success through risk management, modular construction, and BIM adoption. He emphasizes communication, sustainability, and community wellbeing in construction projects. Recent projects include studies on net-zero steel production, BIM in SMEs, and NCC 2022 energy requirements. Awards: ARCHIBUS Excellence Awards (2017-2013), Best Residential Development Awards (2009-2010), and Australian Postgraduate Award (1997). Teaching: Courses include Project Planning, Professional Practice in Engineering Management, and Global Project Management. He has supervised over 250 theses since 2003. Industry Roles: Director of Business Development for multiple organizations, specializing in construction, IT systems, and real estate. He advises on complex project delivery and stakeholder management.

Hongfu SunView profile

Senior Lecturer

• Biomedical Engineering
• Deep Learning
• Magnetic Resonance Imaging (MRI)
• +3 more

Hongfu Sun is a Senior Lecturer at the School of Engineering, University of Newcastle. His research focuses on innovating MRI mechanisms for clinical and research applications, particularly in Quantitative Susceptibility Mapping (QSM). He is internationally recognized as a pioneer in QSM and integrates MR physics, signal processing, and AI for medical imaging advancements. Sun holds a Ph.D. in Biomedical Engineering from the University of Alberta, Canada. Professional Experience: Senior Lecturer at University of Newcastle (current) ARC DECRA Research Fellow at University of Queensland (2021–2023) Postdoctoral Researcher at University of Calgary (2015–2019) Research Interests: Focuses on MRI innovation, including QSM, deep learning for medical imaging, and AI-driven reconstruction techniques. His work addresses challenges like sub-millimeter resolution and artifact reduction in MRI. Recent projects involve generative AI models for MRI analysis and accelerated quantitative imaging methods. Grants and Funding: AU$1.69M in grants, including a 2021 ARC DECRA for microscopic MRI techniques 2024 NHMRC grant for Parkinson’s disease MRI diagnostics Teaching: Course coordinator for Medical Imaging and Signal Processing at University of Newcastle Focus on biomedical imaging, computational methods, and signal analysis Labs/Teams: Leads research in MRI innovation, collaborating on QSM, deep learning applications, and translational imaging techniques. Active in interdisciplinary projects combining physics, AI, and clinical medicine.

Judy HartView profile

Associate Professor

• Renewable Energy Materials
• Photocatalysis
• Solar Cells
• +5 more

Associate Professor Judy Hart is a materials scientist at the School of Materials Science & Engineering, UNSW Sydney , specializing in the development of semiconducting materials for renewable energy applications. Her work integrates computational (DFT) and experimental approaches to understand composition-property relationships in systems like solid solutions , heterostructures , and doped materials for photocatalysis and solar cells . She leads projects funded by ARC Discovery and Linkage grants , including work on photo-electro-catalysis systems and stabilizing ceramic materials . Education: PhD in Materials Engineering (Monash University, 2007), BEng (Materials) (Monash, 2002) Professional Experience: Senior Lecturer (UNSW, 2017–), Lecturer (UNSW, 2013–2017), University of Bristol (2007–2012) Research Interests Her research focuses on designing materials for renewable energy , particularly photoelectrochemical water splitting and organic oxidation reactions . Key areas include Density Functional Theory (DFT) , defect engineering , band gap tuning , and nanostructured materials . She investigates ferroelectric polarization effects , metal oxide heterostructures , and stability of battery components , with applications in hydrogen production , CO2 conversion , and advanced battery materials . Scientific Awards Ramsay Memorial Fellowship (University of Bristol, 2007–2009) Teaching Contributions She is co-author of the 1st Australian & New Zealand edition of "Materials Science and Engineering: An Introduction" , and teaches courses on computational materials science , corrosion-resistant surfaces , mechanical behavior of metals , and materials design .

Ben MatherView profile

Research Fellow

• Enigmatic Volcanoes
• Groundwater Flow
• Critical Minerals and Green Energy
• +3 more

Ben Mather is a Research Fellow in the School of Geosciences at The University of Sydney, specializing in geodynamic modeling and Earth system processes. He leads the EarthByte Group's efforts to integrate numerical models with geophysical data, focusing on volcanic systems, groundwater dynamics, and critical mineral exploration. His work bridges geoscience and climate change mitigation strategies, influencing national and international policy discussions. Education: PhD in Earth Science, The University of Melbourne (2016) Bachelor of Science (Hons), Monash University (2011) Diploma of Film and Television, Monash University (2010) Research Interests: Enigmatic volcanic activity patterns and their tectonic drivers Groundwater flow pathways under climate extremes Carbon sequestration via tectonic processes Development of open-source geodynamic tools like Stripy and PyCurious Notable Projects: Project Volcanoes Downunder: Investigating volcanic chains in the Tasman Sea Groundwater modeling for southeastern Australia's aquifers Thermal structure studies in Ireland and Australia using Bayesian inversion His computational frameworks, built on PETSc and Python, enable large-scale simulations of Earth's thermal and hydrological systems. Mather actively engages in public science communication through media interviews and educational workshops.

Richard TaylorView profile

Associate Professor

• Superconductivity
• HTS Materials
• Power Engineering
• +2 more

Richard Taylor is Associate Professor in QUT's Faculty of Engineering, specializing in applied superconductivity and power engineering. His research focuses on high-temperature superconducting (HTS) materials characterization, MgB2 wire technology, and energy-efficient cryogenic systems. Experimental work includes developing testing facilities for HTS machine performance under dynamic electromagnetic conditions. Publications demonstrate consistent focus on superconducting materials optimization for industrial applications.

SangHyung AhnView profile

Lecturer

• Construction Process Modelling
• Building Information Modelling (BIM)
• Agent-Based Simulation
• +3 more

SangHyung Ahn is a Lecturer at the School of Civil Engineering , University of Queensland (UQ), since 2017. He joined UQ as a postdoctoral research fellow in 2015 after earning his PhD in Civil Engineering (Construction Engineering and Management) from Purdue University, USA. Prior to his academic career, he worked as an assistant manager at Hyundai Engineering and Construction Co., Ltd. (2003-2007) and holds an MBA in international business from Hanyang University and a B.Sc in Civil Engineering from Korea University. Research Focus: Construction process modelling with virtual reality, decision support systems for construction, automation of data-driven simulation modelling, sensor-based operations analysis, and integration of Building Information Modelling (BIM). Teaching: Coordinates undergraduate courses Introduction to Project Management (CIVL3510) and Construction Engineering Management (CIVL4522) . Research Trends: His recent publications highlight interdisciplinary work in transportation engineering, structural design, and AI-driven simulation tools. Key themes include application of machine learning to car-following models, drone-based vehicle identification, and optimization of public transport systems using agent-based simulations. Supervision: Available for supervision, with completed supervision of PhD and Master’s theses on topics such as BIM-LCA integration, pedestrian trajectory analysis, and AI-driven driving behavior models.

Gavin SchwarzView profile

Professor

• Organizational Change
• Organizational Failure and Inertia
• Virtual Teams
• +2 more

Gavin Schwarz is a Professor and Head of School at the School of Management and Governance within the UNSW Business School . He specializes in organizational change , organizational failure and inertia , and the dynamics of virtual teams , with a focus on how organizations fail during change processes and how to develop applied strategies for change management . His work spans diverse sectors including healthcare, technology, and education, with publications in leading journals such as Academy of Management Learning and Education and Administrative Science Quarterly . Education : PhD in Management (University of Queensland), MPhil (Hons) in Management (University of Auckland), BA in Management and English (University of Auckland). Grants : 2020 Brock University grant for "University communication in times of COVID-19" , 2020 UNSW Medicine grant for "Translation and change: Embedding effective change management in health" , and earlier Australian Research Council and Gordon J. Samuels Fellowship awards. His research explores the development of knowledge in organizational theories , with an emphasis on collective responses to change , HR management during crises , and technology strategy . He has contributed to understanding organizational communication , team innovation , and structural inertia . His 15 most recent publications cover topics from AI’s role in organizational change to pandemic-driven research adaptation, with keywords spanning management science , behavioral economics , and digital transformation . Scientific Awards 2021-2023 : Outstanding Reviewer Awards (Academy of Management Review) 2017-2020 : Best Reviewer Awards (Journal of Organizational Behavior) 2019, 2013, 2011 : Best Paper Finalist/Awardee (Academy of Management divisions) 2007, 2006 : Editorial Board Excellence (Academy of Management Journal) and Gordon J. Samuels Fellowship As an active supervisor in organizational change and HR development , his work supports healthcare innovation and digital transformation. He serves as Editor-in-Chief for the Journal of Applied Behavioral Science and is on the editorial boards of Academy of Management Review , Journal of Management , and Journal of Organizational Behavior . Contact: g.schwarz@unsw.edu.au