جستجوی استاد راهنمای دانشگاهی

Craig Engstromمشاهده پروفایل

دانشیار

• Sports Medicine
• Magnetic Resonance Imaging
• Musculoskeletal System
• +۴ مورد دیگر

Associate Professor Craig Engstrom is an accomplished academic at The University of Queensland's School of Human Movement and Nutrition Sciences within the Faculty of Health, Medicine and Behavioural Sciences. He serves as Program Coordinator for the Postgraduate Masters of Sports Medicine and is an Affiliate of the Centre for Innovation in Pain and Health Research (CIPHeR). With over 100 publications including 47 journal articles, 52 conference papers, and a book chapter, his research spans sports medicine, musculoskeletal imaging, and innovative educational approaches. Bachelor of Human Movement Studies (Education) (Honours), University of Queensland MSc, Queen's University, Canada PhD, University of Queensland Professor Engstrom's research focuses on three primary areas: Sports Medicine with particular emphasis on injury mechanisms in athletes (especially cricket fast bowlers and water polo players), cutting-edge Magnetic Resonance Imaging of the Musculoskeletal System for assessing joint morphology and pathology, and development of innovative web-based approaches for learning and assessment in health professional education. His work on the eCAPS (e-Clinical Assessment of Practical Skills) system demonstrates his commitment to educational innovation. His current research leverages advanced computational methods including deep learning, statistical shape modeling, and 3D segmentation techniques applied to musculoskeletal MRI data. Analysis of his recent publications reveals a strong trend toward applying artificial intelligence and machine learning techniques to medical imaging, particularly in knee and hip joint analysis. His work increasingly focuses on automated segmentation, anomaly detection, and quantitative assessment of joint morphology using 3D MRI. The Osteoarthritis Initiative dataset features prominently in his recent work, indicating a growing focus on osteoarthritis research alongside his longstanding interest in sports injuries. Award for Teaching Excellence (ATE) - Australian Award for University Teaching (AAUT) 2015-2018 Professor Engstrom actively supervises numerous PhD candidates working on projects related to medical image analysis, ACL registry development, and deep learning applications in healthcare. His current research is supported by significant funding including 'Cost effective and portable low-field musculoskeletal MRI for high performance sport' (2025-2027) and 'Quantum-Enabled Low-Field Magnetic Resonance Imaging for High-Performance Sport' (2024-2027). His past funding includes NHMRC Development Grants, ARC Linkage Projects, and industry partnerships with Siemens Healthcare. His work bridges clinical practice, research, and education, with strong collaborations across The University of Queensland, particularly with researchers like Stuart Crozier and Shakes Chandra. His research group appears to focus on developing computational tools for quantitative analysis of musculoskeletal MRI, with applications in sports medicine, orthopedics, and rehabilitation.

Leigh Sperkaمشاهده پروفایل

مدرس

• Outsourcing in Education
• Curriculum and Pedagogy
• Student Perspectives
• +۵ مورد دیگر

Dr. Leigh Sperka is a Lecturer at the School of Human Movement and Nutrition Sciences , The University of Queensland . She graduated with First Class Honours in Bachelor of Health, Sport and Physical Education (2013) and earned her PhD (2018) from UQ. Her research interrogates outsourcing in education , particularly its effects on curriculum, pedagogy, and student agency in Health and Physical Education (HPE) . Her work emphasizes student-centered learning and inclusive pedagogy , advocating for marginalized voices in educational practices. She collaborates on projects like Students-as-partners and explores community health models through affiliations with the Centre for Community Health and Wellbeing and Centre for Sport and Society . Recent publications analyze cost-efficiencies in outsourced HPE , feedback activation in physical education , and disability inclusion in sports pedagogy. Her studies span journal quality indicators , curriculum commercialisation , and critical discourse analysis of external providers in education. Fellow of the Higher Education Academy U21 Health Sciences Teaching Excellence Award (2021) UQ Commendation for Student Learning (2020) Faculty of Health Teaching Excellence Award (2020) As an advisor, she supports PhD research on topics such as community dance wellbeing , student voice in community schools , and feedback culture in HPE . Her scholarship bridges education policy , social justice , and pedagogical innovation .

Hrishi Patelمشاهده پروفایل

پژوهشگر

• Machine Learning
• Learning Analytics
• Data Science Teaching
• +۳ مورد دیگر

Hrishi Patel is a PhD researcher at The University of Queensland (UQ) , affiliated with CIRES (Centre for Innovation in Resource Efficiency and Sustainability). Holding a Master of Data Science from UQ, his work spans machine learning, learning analytics, and data science pedagogy. Education : Master of Data Science, University of Queensland His research focuses on developing outpatient risk prediction models to combat pediatric sepsis, in collaboration with Queensland Health . Supervised by Dr. Sen Wang, Dr. Adam Irwin (Queensland Health), and Professor Shazia Sadiq, his work aims to improve critical care for children through interdisciplinary partnerships. Notable achievements include recognition for outstanding tutoring skills at UQ Business School. He actively collaborates with academic and government teams to bridge data science with healthcare applications.

Michael Mahoneyمشاهده پروفایل

استاد

• Randomized Numerical Linear Algebra
• Machine Learning
• High Dimensional Statistics
• +۳ مورد دیگر

Michael Mahoney is Professor of Statistics at the University of California, Berkeley, with additional affiliations at the International Computer Science Institute (ICSI) where he is Vice President and Director of the Big Data Group, the Lawrence Berkeley National Laboratory (LBNL) where he leads the Machine Learning and Analytics Group, and the EECS department's RISELab. He is also an Amazon Scholar. Education: While specific degrees are not listed in the provided text, his extensive record of teaching, research leadership, and publications indicates doctoral-level training in Statistics and Computer Science. Research Interests: Mahoney's work centers on the applied mathematics of data , spanning algorithmic and statistical foundations of big data, randomized numerical linear algebra (RandNLA), high-dimensional statistics, machine learning, and scientific machine learning. He develops theory, scalable implementations, and real-world applications in internet analytics, social networks, genetics, astronomy, and climate science. Recent software contributions include the RandBLAS and RandLAPACK libraries (standardizing RandNLA routines), Landscaper for visualizing deep-learning loss landscapes, and packages such as FreeAlg , DetKit , Imate , and LeaderBot . Awards & Honors: NeurIPS 2020 Best Paper Award (co-authored work on column subset selection) Director, NSF TRIPODS UC Berkeley FODA Institute Key contributor to the BALLISTIC project for next-generation BLAS/LAPACK Grants & Leadership: Principal Investigator, NSF TRIPODS FODA Institute (Foundations of Data Analysis) Group Lead, Machine Learning and Analytics, LBNL Vice President & Director, Big Data Group, ICSI Advising & Mentoring: Mahoney has an extensive network of current and former PhD students, postdocs, and visiting researchers, including placements at MIT, Stanford, Waterloo, Stevens, Tsinghua, and Georgia Tech. Current advisees include Shengaho Yang, Zhichao Wang, Hyunsuk Kim, and Pu Ren, among many others. Labs & Teams: He directs research efforts across UC Berkeley Statistics, ICSI’s Big Data Group, LBNL’s Machine Learning and Analytics Group, and the RISELab (formerly AMPLab), fostering cross-disciplinary collaboration between statistics, computer science, and domain sciences.

Francis Huiمشاهده پروفایل

دانشیار

• Ecological Statistics
• Longitudinal and Spatio-Temporal Data Analysis
• Mixed Effects Models
• +۵ مورد دیگر

Francis Hui is an Associate Professor in Statistics at the Research School of Finance, Actuarial Studies and Statistics (RSFAS), The Australian National University (ANU), specializing in ecological statistics, longitudinal data analysis, and statistical computing. His work bridges methodological advancements with practical applications in environmental science, actuarial studies, and multivariate data analysis. Education: PhD in Statistics from the University of New South Wales (2015) BSc/BA (First Class Honours, University Medal) from the University of New South Wales (2012) Research Interests: Approximate likelihood methods for estimation and inference Ecological statistics and joint species distribution modeling Dimension reduction and model selection for complex data Variational approximations and semiparametric regression Spatio-temporal and multivariate longitudinal data analysis Publication Trends: Francis's recent work focuses on robust statistical methods for ecological modeling, including generalized linear mixed models, composite likelihood techniques, and dimension reduction. His research also explores fair actuarial models for retirement outcomes and computational efficiency in handling large-scale environmental datasets. Projects: Current projects include developing modern statistical methods for clustering community ecology data, enhancing predictive accuracy in spatio-temporal models, and creating scalable tools for multivariate abundance data analysis. Software: He has contributed to R packages such as assam , COQUE , HPGEE , and gllvm , which implement advanced statistical techniques for ecological and longitudinal data.

Naveen Kumar Elumalaiمشاهده پروفایل

مدرس ارشد

• Renewable Energy
• Materials Science
• Photovoltaics
• +۳ مورد دیگر

Naveen Kumar Elumalai serves as a Senior Lecturer in Mechanical Engineering within the Faculty of Science and Technology at Charles Darwin University. His academic profile demonstrates strong institutional integration through his affiliation with the Energy and Resources Institute and membership in the Higher Degree by Research (HDR) Cohort, where he is registered to supervise postgraduate research. His extensive collaboration network spans Charles Darwin University, University of New South Wales, Curtin University of Technology, and international institutions including Sunway University and University Malaysia Pahang. Dr. Elumalai's research focuses on cutting-edge renewable energy technologies, with particular expertise in photovoltaic systems, advanced materials for energy applications, and sustainable engineering solutions. His work bridges theoretical materials science with practical engineering implementations, especially in solar energy conversion and storage systems. The research portfolio demonstrates consistent growth from 2011 through 2025, with significant output in perovskite solar cells, energy harvesting technologies, and nanomaterial applications for renewable energy systems. Analysis of his 85 research outputs reveals a strategic evolution from fundamental materials research toward applied sustainable engineering solutions. Recent publications (2023-2025) show increased focus on commercialization pathways, system integration, and environmental impact assessment of renewable energy technologies. His collaborative work with over 50 researchers demonstrates strong interdisciplinary connections between mechanical engineering, materials science, and environmental sustainability. With an h-index of 35 and 4,483 citations, his scholarly impact is substantial within the renewable energy research community. His research trajectory shows consistent publication activity with 5 projects in 2022 alone, indicating sustained research momentum and competitive grant acquisition. Dr. Elumalai actively supervises postgraduate research through Charles Darwin University's HDR program and maintains extensive collaborative networks with major Australian research institutions. His laboratory work appears centered within the Energy and Resources Institute, focusing on experimental validation of novel materials and energy conversion systems. Current research directions suggest growing emphasis on practical implementation challenges of renewable energy technologies and their integration into existing infrastructure.

Jan Zikaمشاهده پروفایل

دانشیار

• Oceanography
• Climate Science
• Fluid Dynamics
• +۵ مورد دیگر

Jan Zika is an Associate Professor in the School of Mathematics & Statistics at the University of New South Wales, Sydney. His research focuses on the role of ocean dynamics in climate change, particularly water mass transformation, heat transport, and the interaction between ocean circulation and the global water cycle. PhD from CSIRO (Hobart) Prior affiliations: Université Joseph Fourier, University of Southampton, Imperial College London Key research areas include: Ocean mixing and its impact on climate sensitivity Water mass transformation as a diagnostic for ocean circulation Heat engine efficiency in warming climates Biogeochemical tracer transport Recent publications emphasize: Advancements in water mass transformation frameworks Machine learning applications for salinity mapping Constraints on ocean carbon uptake mechanisms Historical trends in freshwater transport Scientific awards: 2018 EGU Outstanding Early Career Researcher Award 2020 Australian Academy of Science Anton Hales Medal Grants include NERC Postdoctoral and Senior Fellowships. He leads interdisciplinary teams studying Southern Ocean dynamics and has contributed to ocean modeling benchmarks in CMIP5/CMIP6 frameworks.

• Hydrogen Fuel Cells
• Electrochemistry
• Renewable Energy
• +۴ مورد دیگر

Dr. Quentin Meyer is a Lecturer at the School of Chemistry, University of New South Wales (UNSW), specializing in advanced electrochemical systems and fuel cell technologies. His research focuses on optimizing catalysts, membrane electrode assemblies, and electrochemical diagnostics for hydrogen fuel cells. Recent publications highlight his work on platinum-free catalysts , oxygen reduction reaction mechanisms , and electrochemical impedance spectroscopy . Collaborations span institutions in Australia and international partners in Europe and Asia. Key research areas: Hydrogen fuel cell engineering Electrochemical stability and degradation Catalyst layer optimization Operando and in situ diagnostics

Alice Towlerمشاهده پروفایل

مدرس پاره‌وقت

• Forensic Psychology
• Face Identification
• Cognitive Science
• +۳ مورد دیگر

Dr. Alice Towler is a Casual Academic at the School of Psychology within the University of New South Wales (UNSW). Her research focuses on improving face identification accuracy and efficiency in forensic settings, including the study of super-recognition abilities, development of evidence-based facial comparison methods, and optimization of human-software collaboration in identity verification. Her primary research interests include: Forensic face identification techniques and error reduction Predictors of superior face recognition ability (super-recognition) Development of evidence-based training protocols for facial image comparison Integration of human expertise with facial recognition software systems Identity fraud detection in critical applications like passport verification Dr. Towler collaborates extensively with the UNSW Forensic Psychology Lab under Dr. David White and Prof. Richard Kemp. She maintains an active partnership with the Australian Passport Office to enhance identity fraud detection capabilities. As a member of the Evidence-Based Forensics Initiative, she advocates for scientifically validated practices in forensic sciences through interdisciplinary collaboration between legal professionals, forensic scientists, and cognitive researchers. Her recent publications demonstrate consistent focus on face recognition mechanisms, perceptual expertise development, and forensic applications. Research themes include optimization of training protocols, individual differences in recognition abilities, validation of forensic methodologies, and real-world implementation challenges.

Lily Qiaoمشاهده پروفایل

مدرس ارشد

• Space Systems Engineering
• Autonomous Spacecraft Navigation
• GNSS and Celestial Navigation
• +۵ مورد دیگر

Lily Qiao is a Senior Lecturer in Space Systems Engineering at the School of Engineering and Technology , University of New South Wales (UNSW) Canberra . She holds a PhD in Guidance, Navigation, and Control (GNC) from Nanjing University of Aeronautics and Astronautics (2011) and has been affiliated with UNSW since 2011, progressing from Research Associate to her current role. Her teaching includes postgraduate courses on Space Systems Design (ZEIT8008) and Global Navigation Satellite Systems (GNSS) (ZEIT8009), and undergraduate Radar Technology and Application (ZEIT4227). PhD in Guidance, Navigation, and Control (NUAA, 2011) Study exchange at UNSW Sydney Certificate of Completion with Merit (Graduate Teaching Training Program, UNSW Canberra, 2015) Foundations of University Learning and Teaching (FULT) Program (2019) National Mental Health First Aid Certificate (2019–2021) UNSW Course Design Institute (2023) Her research focuses on space systems engineering and the application of AI/CI-based methods to aerospace challenges, including autonomous spacecraft navigation (GNSS, celestial navigation), spacecraft attitude determination and control , Kalman filter technology , and cybersecurity for space systems . She has published over 50 peer-reviewed works and supervised six PhD students. Her recent publications highlight trends in cybersecurity for space systems, GNSS reflectometry for Earth observation, and modular design analysis for CubeSats. Notable awards include the UNSW Career Advancement Fund for Female Academics (2018–2020) and IEEE Senior Membership (2024) . Associate Editor, IEEE Transactions on Aerospace and Electronic Systems Chair, IEEE Australian Capital Territory (ACT) Section (2024) Founder and Chair, AESS Chapter in IEEE ACT Section (2022–2024) President, Women in Space Chapter (WiSC) , National Space Society of Australia Executive Committee, IEEE ACT Section (2020–2023) UNSW Canberra Representative, Women in Research Network (since 2021) She offers $35,000 AUD scholarships for PhD candidates with H1/High Distinction in undergraduate or Master’s programs. Current research supervision topics include AI Test and Evaluation , Real-Time Cybersecurity Platforms , and Dynamic Modular System Architectures .

Kenan Degirmenciمشاهده پروفایل

مدرس

• Information Systems
• Behavioral Economics
• Technology Adoption
• +۷ مورد دیگر

Dr. Kenan Degirmenci is a Lecturer in Information Systems at Queensland University of Technology's School of Information Systems, Faculty of Science. He serves as Course Coordinator for the Master of Information Technology (Online) and Bachelor of Information Technology programs, and as Deputy Academic Lead in Learning & Teaching. His research focuses on digital innovation, behavioral economics, and sustainable technology adoption, particularly examining how AI, blockchain, and IoT transform organizational decision-making for sustainability. Dr. Degirmenci earned his Doctor of Economics (Dr. rer. pol.) from Leibniz University Hannover in Germany. His academic journey includes postdoctoral research and industry collaborations with organizations including Volkswagen during his doctoral studies. His research spans four interconnected areas: (1) business analysis and decision modeling for organizational transformation, (2) quantitative and qualitative methods for evaluating decision-making processes, (3) digital solutions for sustainable behavior change, and (4) psychometric analysis for information security and privacy decisions. He integrates behavioral economics with information systems to develop user-centered technologies that address sustainability challenges in energy markets, smart cities, and environmental management. Analysis of his recent publications reveals a strong focus on sustainable technology adoption, particularly electric vehicles and smart city applications. His work bridges technical innovation with human behavior, examining how cognitive computing, gamification, and IoT can drive eco-efficient practices. Key themes include urban resilience, AI governance, energy informatics, and privacy concerns in digital transformation. Vice Chancellor's Award for Excellence (Information Systems Learning & Teaching Team, 2021) Australian Computer Society Reaccreditation Excellence (Team, 2021) Colleague of the Year, School of Information Systems (2021) Secretary of SIG Green, Association for Information Systems (2024) Early Career Editorial Board, Journal of Strategic Information Systems (2022) Dr. Degirmenci actively supervises PhD students researching sustainable energy systems, privacy-preserving tools, and electric mobility. His current research is supported by Queensland Government funding for local climate research, with previous projects funded by ARC Discovery Grants, COALAR, and the German Federal Ministry. As Head of the Behavioural Energy Analytics & Modelling (BEAM) research group and Deputy Program Lead at the Centre for Behavioural Economics, Society and Technology, he leads interdisciplinary collaborations addressing urban sustainability challenges. He is a core member of QUT's Smart City Lab and Urban AI Hub, contributing to initiatives like the Australia-Brazil Smart City Research and Practice Forum and IoT-based environmental monitoring projects for urban heat mitigation. His work connects academic research with practical applications for sustainable development.

• Algorithms
• Data Structures
• Graph Theory
• +۴ مورد دیگر

Andrew Gozzard is a Lecturer in the Department of Computer Science and Software Engineering at the University of Western Australia's School of Physics, Maths and Computing. He holds a PhD from UWA (2023), a BSc in Physics, and a BSc (Honors) in Computer Science. Education : PhD in Computer Science (2023), BSc in Physics, BSc (Honors) in Computer Science His research focuses on algorithms, data structures, graph theory, and functional programming. He has contributed to probabilistic reasoning in multi-agent systems and developed algorithms for geometric intersection graphs. His work intersects computer science with mathematical logic. Key publications include studies on aleatoric dynamic epistemic logic and efficient graph algorithms. He previously worked as a programmer with UWA's Gravitational Wave Data Analysis team and completed five software engineering internships at Google. His teaching philosophy emphasizes collaborative learning and co-founding the UWA Programming Competition Society.

John Fletcherمشاهده پروفایل

استاد

• Electrical Engineering
• Power Engineering
• Renewable Energy
• +۷ مورد دیگر

Professor John Fletcher is a world-leading academic in electrical power engineering at the University of New South Wales' School of Electrical Engineering and Telecommunications. He serves as Director of the Digital Grid Futures Institute (appointed May 2022), UNSW's Director of the NSW Government's Electrification and Energy Systems Network, and leads the TRaCE-funded $2M Technology Translation Squad. His research portfolio totals $15 million with $4.7 million awarded since 2022. Fletcher's research focuses on electrical power engineering , with highly-cited works in HVDC and wind generation integration, multi-phase drive systems, and novel electrical machines. His expertise spans renewable energy integration, smart grids, power electronics, and electrical machines. He has made significant contributions to wind turbine technology, photovoltaic integration, and power semiconductors for energy applications. His publication record shows consistent high-impact output with 156 journal articles, demonstrating strength in renewable energy integration, power system stability, and advanced power electronics. His work shows increasing emphasis on practical implementation challenges of renewable energy systems and grid modernization. Fletcher has completed 56 PhD students throughout his career (12 in 2022-2025), making him the top professor in his school for PhD completions during that period. He has secured substantial funding including ARC Discovery Projects, DSTG Next Generation Technology Fund, CRC, CSIRO, ARENA, and CRC-P grants totaling $4.7 million. As an educator, he developed UNSW's first microcredential in Hydrogen engineering and manages a full teaching load with classes exceeding 380 students, receiving outstanding student feedback (myExperience scores 5.5 to 5.63). He also manages two VIP projects (Sunswift and Atomcraft). Fletcher actively engages with industry and government, representing UNSW's stake in electrification to high-level officials including Penny Sharpe, Grant King, Chris Bowen, Mary O'Kane, Paul Fletcher, and Victor Dominello, as well as presenting to the Board of the Climate Change Authority and delegations from AEMO, Pearcey Foundation, and CEFC.

• Traffic congestion management
• Transport systems
• Sustainable mobility solutions
• +۷ مورد دیگر

Dr. Mohsen Ramezani is an Associate Professor in Transport Engineering at the School of Civil Engineering, University of Sydney, where he has been working since 2016. He holds an ARC DECRA Fellowship (2021-2023) and is a member of The Net Zero Institute. Prior to joining the University of Sydney, he served as a Lecturer at the Institute of Transport Studies at Monash University, Melbourne, Australia (2015-2016). Dr. Ramezani received his B.Sc. and M.Sc. degrees in electrical engineering (control systems) from the University of Tehran, Iran, in 2008 and 2010, respectively, and his Ph.D. degree in Transportation from École Polytechnique Fédérale de Lausanne (EPFL), Switzerland in 2014. His research focuses on mitigating urban traffic congestion through advanced modeling and control techniques. Dr. Ramezani develops multi-scale and multimodal traffic control schemes to address complex transport networks. His quantitative approach combines physical modeling of traffic flow dynamics, big data analytics, and advanced mathematical tools to create efficient, sustainable, and equitable mobility solutions. He emphasizes the practical implementation of theoretical concepts, with a particular interest in the dynamic behavior of human decision-making within transport systems. Dr. Ramezani's recent publications reveal a strong trajectory toward e-hailing markets, autonomous vehicle systems, and intelligent traffic management. His work demonstrates increasing integration of machine learning techniques with traditional traffic engineering approaches, while maintaining focus on real-world applicability. The research spans theoretical development and practical implementation, with emphasis on real-time control systems and optimization algorithms for complex urban networks. Key Achievements: Australian Research Council (ARC) DECRA Fellow (2021-2023) Best Ph.D. Dissertation Award by the IEEE Intelligent Transportation Systems Society (2015) Dr. Ramezani currently supervises multiple PhD students working on cutting-edge transport research topics including predictive analytics for urban corridor control, shared electric micromobility systems, and autonomous vehicle control. His research portfolio includes significant ARC-funded projects on congestion control in complex networks and design of micro-decisions in automated transport, demonstrating his leadership in advancing sustainable urban mobility solutions.

Chi-Ho Ngمشاهده پروفایل

پژوهشگر ارشد

• Additive Manufacturing
• Materials Engineering
• Titanium Alloys
• +۳ مورد دیگر

Dr Chi-Ho Ng is a Research Fellow at the School of Mechanical and Mining Engineering , The University of Queensland . His research focuses on advanced materials processing, particularly in additive manufacturing and surface treatments of titanium alloys. Bachelor (Honours) in Production Technology, Hong Kong Polytechnic University Masters in Manufacturing Technology, Cranfield University PhD in Materials Engineering, The University of Queensland His research interests include: Additive manufacturing techniques (wire arc, laser powder bed fusion) Hot isostatic pressing for defect elimination and microstructure control Laser-based surface modification (nitriding, alloying) Biomedical applications of titanium alloys and coatings Use of machine learning for porosity analysis in 3D-printed materials Recent publications highlight his work on β-fleck defect characterization, cryogenic coolant applications, and machine learning-assisted porosity analysis in titanium alloys. He actively supervises projects related to piezoelectric materials for biomedical applications and has received funding from the Australian Nuclear Science and Technology Organisation for process optimization research.