جست‌وجوی استاد و پژوهشگر

Lin Tianمشاهده پروفایل

مدرس ارشد

• Multi-scale modeling
• Aerosol transport
• Fluid dynamics
• +۶ مورد دیگر

Dr. Lin Tian is a Senior Lecturer in the School of Engineering at RMIT University, Melbourne, Australia. She holds expertise in multi-scale modeling of fluid dynamics and micro/nano particles, with a focus on aerosol transport, inhalation toxicity, and drug delivery in respiratory systems. Her research integrates computational methods like Lattice Boltzmann simulations to solve multiphase flow problems. Current projects include developing risk assessment platforms for inhaled carbon nanotubes and hydrodynamic modeling for underwater vehicle systems. Her academic journey includes roles as a Research Fellow at RMIT (2015–2018) and Assistant Professor at SUNY Canton, USA (2008–2014). She leads funded projects such as the DECRA-supported multi-scale risk assessment platform and DSTG-funded underwater vehicle hydrodynamics research. Teaching interests span advanced thermo-fluids, mechanics of solids, and computational fluid dynamics. Research highlights include nasal airflow modeling, fiber transport dynamics, and HVAC system optimization for pathogen control. Awards include the DECRA grant (2018), and her work addresses critical areas like occupational exposure, drug delivery efficacy, and sustainable building systems. Dr. Tian is open to supervising Masters and PhD students in computational fluid dynamics and multiphase flow applications.

• Applied Artificial Intelligence (AI)
• Computer Vision
• Disaster Management
• +۴ مورد دیگر

Dr. Umair Iqbal is a Researcher in the School of Engineering at the University of Newcastle, specializing in Applied Artificial Intelligence (AI) with a focus on Computer Vision and Edge-Computing solutions for real-world challenges like disaster management and water resource optimization. He holds a PhD from the University of Wollongong and is an NVIDIA DLI Ambassador, delivering workshops on deep learning and AIoT technologies. His research emphasizes AI-driven video analytics for infrastructure monitoring (e.g., culvert blockage detection), public safety (e.g., violence detection in transit systems), and environmental applications (e.g., flood risk mitigation). His work combines interdisciplinary approaches, leveraging tools like TensorFlow, NVIDIA frameworks, and edge devices (Jetson platforms). Key contributions include developing an AIoT solution for pipeline protection, a real-time edge-computing system for public transport safety, and bibliometric reviews on drones and computer vision in flood management. Dr. Iqbal has secured $114,814 in grants, including a 2023 project on floodborne debris identification and a 2025 initiative for kerbside waste management. He actively supervises graduate students and leads projects like StopBlock (culvert blockage detection) and iMOVE (transport safety analytics). Research interests span AIoT frameworks, generative AI, and geotechnical applications. His publications (23+ journal articles, 3 conference papers, 2 book chapters) address hydraulic infrastructure resilience, machine learning in civil engineering, and disaster response technologies. He collaborates with industry partners like Remondis and fosters innovation through workshops and course coordination at undergraduate/postgraduate levels.

• Computer Vision
• Machine Learning
• Computational Mathematics
• +۳ مورد دیگر

Ryszard Kozera holds the position of Adjunct Associate Professor in the Department of Computer Science and Software Engineering at The University of Western Australia (UWA). He is affiliated with the School of Physics, Maths and Computing. His research focuses on computational mathematics, machine learning, and applied computer vision. Key research interests include spline interpolation, trajectory estimation, neural networks, and their applications in microbiology and hardware optimization. He has contributed to projects like 'Smoothness in geometry and computer vision' funded by ARC Small Grants. Recent work explores machine learning applications in soil microorganism identification and Apple Silicon performance analysis. Collaborations span international conferences such as ICCS and ESM.

Huaming Chenمشاهده پروفایل

مدرس ارشد

• Software Engineering
• AI
• Cybersecurity
• +۳ مورد دیگر

Huaming Chen is a Senior Lecturer at the School of Electrical and Computer Engineering, The University of Sydney (equivalent to Associate Professor in the US system). He specializes in trustworthy AI, software security, and computational biology. His research focuses on integrating AI techniques into software engineering and scientific domains to enhance system reliability and ethical compliance. Currently, he advises PhD and master's students on projects involving AI in intelligent building systems, blockchain-driven governance frameworks, and open-source AI security. He holds roles as Area Chair for ACM MM, PC member for venues like ACM CCS and IJCAI, and Guest Editor for Computers & Security . His service extends to IEEE/ACM memberships and discipline expertise in Software Engineering and Electrical & Computer Systems. Awards include the 2020 IEEE CIS Student Grant and 2017 travel fellowships for bioinformatics and services computing conferences. Research interests span adversarial machine learning, AI ethics, computational biology applications, and secure AI system design. Key contributions include work on federated learning security, adversarial attacks on vision transformers, and privacy-aware STI/HIV risk prediction models. He actively promotes responsible AI through workshops and interdisciplinary collaborations.

• Biomechanics
• Computational Biomechanics
• Finite Element Analysis
• +۷ مورد دیگر

Karol Miller is a Winthrop Professor of Applied Mechanics at The University of Western Australia and a Visiting Professor of Radiology at Harvard Medical School. He holds a MSc in aerospace engineering, a PhD in robotics, and a DSc in biomechanics. His research focuses on computational biomechanics, particularly in the biomechanics of the brain and vascular systems, aiming to advance personalized medicine through technology integration. Key roles: Editor of Modelling in Biology and Medicine , Associate Editor of International Journal for Numerical Methods in Biomedical Engineering , and former Associate Editor of Annals of Biomedical Engineering . Education: MSc (Warsaw), PhD (Warsaw), DSc (Warsaw) Research interests include patient-specific engineering, finite element analysis, and biomechanical modeling of brain-skull interfaces and abdominal aortic aneurysms. His lab, the Intelligent Systems for Medicine Laboratory (ISML), develops technologies to improve clinical outcomes. Notable projects include real-time brain shift correction in neurosurgery and biomechanics-meets-phenomics studies for AAA disease progression. Awards: Humboldt Research Award, NVIDIA GPU Computing Champion, UWA Student Guild Choice Award, and multiple teaching recognitions. Grants: Over 50 projects funded by ARC, NHMRC, NIH, and international agencies. Active projects include real-time neural network-based brain shift correction and computational biophysics integration in 3D Slicer. Labs/Teams: ISML, collaborations with Harvard Medical School, and leadership in computational biomechanics software development (e.g., SlicerCBM framework).

Danny Priceمشاهده پروفایل

مدرس ارشد

• low-frequency radio astronomy
• instrumentation
• digital signal processing
• +۵ مورد دیگر

Dr. Danny Price is an Adjunct Senior Lecturer at Curtin University, affiliated with the Curtin Research Institute and Curtin Institute of Radio Astronomy (CIRA). He holds a position as SKA-Low Operations Scientist and is part of the Faculty of Science and Engineering within the School of Electrical Engineering, Computing and Mathematical Sciences (EECMS). His research focuses on low-frequency radio astronomy, instrumentation, digital signal processing, 21-cm cosmology, fast radio bursts (FRBs), pulsars, and technosignatures related to SETI. Price has contributed to developing novel file formats like SDHDF and optimizing beamforming techniques for large-scale radio telescopes. His work spans Breakthrough Listen projects targeting nearby stars, galaxies, and the Galactic Center, leveraging advanced machine learning and GPU computing for signal analysis. He has also been involved in infrastructure development for the Square Kilometer Array (SKA) and collaborates globally on radio transient monitoring systems. His teaching contributions span multiple disciplines including the Centre for Aboriginal Studies, Business and Law, Health Sciences, Humanities, and Science & Engineering. Affiliated across Curtin's campuses in Australia, Dubai, Malaysia, Mauritius, and Singapore, Price emphasizes interdisciplinary approaches to radio astronomy challenges. His publications highlight advancements in search pipelines for FRBs and technosignatures, hardware management systems, and the application of cutting-edge computational methods in radio instrumentation. While no formal awards are listed, his research has produced impactful work in cosmic dawn studies and pulsar timing methodologies.

Hasindu Malshan Gamaarachchiمشاهده پروفایل

مدرس

• Bioinformatics
• Genomics Data Processing
• Embedded Systems
• +۳ مورد دیگر

Hasindu Malshan Gamaarachchi is a Lecturer in the School of Computer Science and Engineering at UNSW Sydney since 2023, and a visiting scientist at the Garvan Institute of Medical Research's Genomic Technologies Group. He completed his PhD in Computer Science and Engineering at UNSW Sydney (2020) and holds a BSc (Hons.) in Computer Engineering from the University of Peradeniya (2015). His research focuses on bioinformatics software and hardware systems for real-time nanopore sequencing analysis, embedded computing, and GPGPU/FPGA acceleration. Education: PhD in Computer Science and Engineering, UNSW Sydney (2020) BSc Engineering (Hons.) in Computer Engineering, University of Peradeniya (2015) Research Interests: Design and optimization of domain-specific systems for genomics, including nanopore sequencing analysis, algorithmic efficiency, and hardware-software co-design. Key applications include real-time data processing and embedded systems for bioinformatics. Grants & Awards: ARC Discovery Early Career Researcher Award (DECRA) 2023 (Sole CI) ARC Discovery Projects 2023 (CI with Sri Parameswaran) 2021: Outstanding PhD Thesis Award (ABACBS), Dean's Award (UNSW) 2020: Third place at ACM SRC Awards span academic excellence (e.g., IET-YPS 2016) to bioinformatics innovation. Collaborates with industry and institutions like Garvan for genomic technologies.

Simon Angusمشاهده پروفایل

استاد

• Computational and Complexity Science
• Numerical Simulation
• Machine Learning
• +۱۰ مورد دیگر

Simon Angus is a Professor at the Department of Economics, Monash Business School, Monash University. He leads the Monash IP Observatory as Director and co-founded SoDa Labs, focusing on computational and complexity science applications across social, biological, and physical sciences. His work bridges artificial intelligence, machine learning, and policy analysis through innovative methodologies. Director, Monash IP Observatory Co-founder, SoDa Labs Lead developer of Astro (LLM-powered app) and Forumbrain AI His research spans complexity economics , agent-based modeling , evolutionary game theory , and narrative extraction . Recent projects explore democratic resilience, disadvantage narratives in Australia, and AI applications in social science. He has pioneered GPU-based genetic algorithms for radiotherapy optimization and LLM integration for policy analysis . Scientific Awards: Australian Awards for University Teaching (AAUT) - 2017 ABDC Award for Innovation and Excellence in Research - 2022 Vice Chancellor's Award for Teaching Excellence - 2016 Angus collaborates with institutions like the United Nations OHCHR, US FCC, and media outlets (Washington Post, Reuters) through the Monash IP Observatory. He teaches "Harnessing Big Data for Business and Society" and supervises research in algorithmic and computational projects with an economics lens.

• Network Science
• Evolutionary Dynamics
• Complex Systems
• +۵ مورد دیگر

Dr. Wei Zhang is a Researcher at the Complex Systems and Data Science group within The University of Sydney. Her work focuses on network science, evolutionary dynamics, and complex systems, with an emphasis on modeling social systems and analyzing meso-scale structures in social networks. She combines theoretical approaches, data-driven modeling, and web-based experimental methods to study emergent collective phenomena. Dr. Zhang holds a PhD in Network Science from ETH Zurich. Her research spans interdisciplinary topics including data science applications to complex systems and the detection of structural patterns in social networks. Recent projects include exploring domain adaptation techniques in machine learning and developing methods for 3D object detection and model compression. Her publications reflect expertise in computer vision, machine learning, and environmental science. Key themes include domain adaptation networks, 3D reconstruction, and LiDAR-based forestry analysis. While no specific awards are mentioned, her work demonstrates significant contributions to theoretical and applied data science. Dr. Zhang collaborates on projects involving interdisciplinary teams, though specific grants or lab affiliations are not detailed in the provided text. Her current research continues to bridge complex systems theory with modern data-driven methodologies.

• structural optimisation
• computational mechanics
• high performance computing
• +۷ مورد دیگر

Associate Professor Vivien Challis is a leading academic in Applied and Computational Mathematics at Queensland University of Technology (QUT), affiliated with the Faculty of Science and the School of Mathematical Sciences . She earned her PhD in Mathematics from The University of Queensland in 2009 and has since built an international reputation in structural optimisation, computational mechanics, and high-performance computing. Her research focuses on piezoelectric metamaterials , topology optimization , and GPU-accelerated computational methods , with recent work advancing robotics applications and biomedical prosthetics. She leads the 2022 Australian Research Council Discovery Project DP220102759 on optimising piezoelectric materials for robotics and contributed to ARC projects DP240102104 and DP170104307. 2025: GridapTopOpt.jl (Julia toolbox for level set optimization) 2025: Wave power absorption by floating plates (piezoelectric energy harvesting) 2025: FF-PINNTO framework (neural network-based optimization) In 2024, she received the JH Michell Medal from ANZIAM, recognizing her contributions to industrial and applied mathematics. Her teaching includes first-year calculus (MXB105) and second-year computational mathematics (MXB226), and she directed QUT's Mathematics Summer School (2022-2024).

• Applied Mathematics
• Numerical and Computational Mathematics
• Biomedical Engineering
• +۲ مورد دیگر

Dr. Megan Farquhar is a Lecturer at the School of Mathematical Sciences, Faculty of Science, Queensland University of Technology (QUT). She holds a PhD in Cardiac Modelling with Fractional Calculus from QUT (2018). Her research focuses on applied mathematics, numerical methods, and computational modeling in biomedical contexts, particularly in cardiac physiology and tissue engineering. She specializes in GPU-accelerated algorithms, fractional calculus applications, and modeling cardiac fibrosis/ischaemia using advanced computational techniques. Education: PhD in Applied Mathematics (QUT, 2018) Her research interests span computational cardiology, biomedical engineering, and numerical analysis. She has developed models for cardiac tissue dynamics and diffusion processes using graph theory and fractional differential equations. Her work integrates high-performance computing to address complex biomedical challenges. Publications highlight contributions to cardiac fibrosis modeling, diffusional kurtosis mapping, and GPU-based numerical methods. Her research bridges mathematical theory with practical biomedical applications, advancing computational tools for medical diagnostics and treatment planning.

• Computer Vision
• Stereo Vision
• Image Processing
• +۵ مورد دیگر

Dr. Jasmine Banks is a Lecturer in the School of Electrical Engineering & Robotics at Queensland University of Technology (QUT). She holds a PhD in Engineering from QUT and a dual Bachelor's degree in Electronics and Information Technology. Her academic roles include serving as First Year Coordinator for Engineering since 2016, focusing on student welfare, academic advising, and curriculum management. Research Interests: Her work spans FPGA-based signal processing, computer vision, biomedical image analysis, and embedded systems. Key areas include stereo vision algorithms, white blood cell segmentation, and 3D face tracking using stereo cameras. She emphasizes practical applications of hardware-software co-design and FPGA acceleration. Teaching: Over 20 years of university teaching experience, including courses in Electronics, Signal Processing, and FPGA programming. Notable roles include Unit Coordinator for ENB240 Introduction to Electronics and EGH449 Advanced Electronics. She pioneered blended learning approaches and developed FPGA tutorials used globally. Publications: Over 80 peer-reviewed papers in areas like FPGA-based solvers, medical image segmentation, and biometric recognition. Recent work focuses on microstructural analysis of drying processes and deep learning for cell classification. Professional Service: IEEE Queensland Section Treasurer (2009–2010) and QUT Student Branch Counselor. Active member of Engineers Australia and the Australasian Association for Engineering Education (AAEE).

John Taylorمشاهده پروفایل

استاد

• Climate Modeling
• Machine Learning
• Image Analysis
• +۳ مورد دیگر

Prof. John Taylor is a prominent academic at The Australian National University (ANU), affiliated with the School of Computing. His research focuses on interdisciplinary areas spanning climate science, machine learning, and high-performance computing. He has made significant contributions to atmospheric modeling, GPU-accelerated algorithms, and data-driven environmental predictions. Research Interests Climate Modeling and Regional Climate Simulations Machine Learning Applications in Meteorology Deep Learning for Image Analysis and Semantic Segmentation High-Performance Computing and GPU Optimization Statistical Downscaling of Climate Variables Cloud-Based Scientific Workflows Collaborations Prof. Taylor collaborates extensively with institutions globally, including work on projects like the Earth Virtualization Engines (EVE) and the PAUNet precipitation prediction framework. His research often bridges computational methods with real-world environmental challenges, such as heat extremes and hydrological modeling. Key Achievements Over 5000 citations and an h-index of 50 Developed innovative frameworks for climate data assimilation and GPU-accelerated algorithms Leader in applying machine learning to environmental monitoring and prediction Labs/Teams He leads research groups at ANU focused on computational climate science and advanced imaging techniques. His team has developed platforms like the Cloud-Based Image Analysis Toolbox and the DCM software for 3D materials modeling.

• Gravitational Wave Astronomy
• Binary Star Evolution
• Neutron Star Research
• +۳ مورد دیگر

Dr. Simon Stevenson is an OzGrav Senior Research Fellow at Swinburne University of Technology's School of Science, Computing and Emerging Technologies. His research focuses on gravitational wave formation channels, binary star evolution, and neutron star mass function constraints through both dynamical and isolated formation models. Key research areas: Gravitational Wave Detection, Binary Star Evolution, Compact Object Formation Professional roles: Committee Member at Astronomy Australia, Chair of OzGrav Population Modelling Program His work has resulted in significant discoveries regarding neutron star birth masses and exceptional gravitational wave events like GW190521. He employs advanced computational techniques (COMPAS, METISSE) and Bayesian inference frameworks to study binary black hole populations across cosmic history. Scientific Recognition: Special Breakthrough Prize in Fundamental Physics (2016) Vice-Chancellor's Research Excellence award (2020) FSET ECR Award (2020) As an active PhD supervisor, he's guided projects on pulsar modeling, globular cluster dynamics, and gravitational wave source characterization. His recent publications in Nature Astronomy and Physical Review X have significantly advanced understanding of compact binary populations.

Edwin Petersمشاهده پروفایل

مدرس

• Signal Processing
• Satellite Communications
• Radar Systems
• +۴ مورد دیگر

Dr. Edwin Peters is a Lecturer in Electrical Engineering at UNSW Canberra, Australia. He specializes in signal processing, satellite communications, radar systems, and embedded technologies (FPGA, GPU, edge computing), with applications in space domain awareness, spacecraft tracking, and near-Earth asteroid characterization via radar. His teaching includes courses like ZEIT3223: Embedded Systems and ZEIT8219: Satellite Communications . Research Focus: Radio frequency sensing for space domain awareness, bi-static/passive radar, and algorithm development for GPU/FPGA platforms. Supervision: Advises PhD candidates in applied signal processing, machine learning, and radar applications for space monitoring. Engagement: Leads outreach programs Young Women in Engineering (YoWIE) and Young Space Explorers to promote STEM participation.