جستجوی استاد راهنمای دانشگاهی

Shoudong Huangمشاهده پروفایل

استاد

• Mobile robot navigation
• Simultaneous localization and mapping (SLAM)
• Underwater robotics
• +۴ مورد دیگر

Shoudong Huang is a Professor at the School of Mechanical and Mechatronic Engineering , University of Technology Sydney, and Deputy Director of the UTS Robotics Institute. His research focuses on mobile robot navigation , SLAM , nonlinear state estimation , and surgical robotics . He has published over 200 papers and is recognized as one of the 100 Most Influential Scholars in Robotics (Aminer, 2018). PhD in Automatic Control, Northeastern University (China) Postdoctoral Research Fellow, University of Hong Kong (1998-2000) Research Fellow, Australian National University (2001-2003) Full-time academic roles at UTS since 2004 His work addresses challenges in robot localization across extreme environments (underwater, underground mining, surgical settings) and develops globally optimal SLAM algorithms with guaranteed performance. He has secured over $4 million AUD in external funding, including ARC Discovery grants and industry partnerships. Recent publications emphasize cross-modal calibration (camera-LiDAR), interval analysis for bounded noise , and template-based deformable surface reconstruction . These span applications in autonomous driving, surgical navigation, and UAV guidance. Chancellor’s Medal for Research Excellence (2020) Supervisor of the Year (2023) Best Paper Award (2016 ICARCV) Huang serves as Associate Editor for IEEE Transactions on Robotics and International Journal of Robotics Research , and has held leadership roles in top robotics conferences like IROS and RSS. His collaborations span MIT, USC, Zhejiang University, and industry partners including PMSW Research Pty Ltd and Multiplex Constructions Pty Ltd.

• 3D Computer Vision
• Machine Learning
• VR and AR
• +۴ مورد دیگر

Professor Hongdong Li is a Tenured Professor at the School of Computing, Australian National University (ANU), within the College of Engineering and Computer Science. His research focuses on 3D Computer Vision, Machine Learning, and their applications in dynamic environments. He has held visiting roles at Carnegie Mellon University and has contributed to significant projects like the Australia Bionic Eyes initiative. Education: PhD (Electrical Engineering). Research Interests : 3D Computer Vision fundamentals and applied AI systems Learning-based 3D perception for plant sciences Robot navigation in unfamiliar environments Awards : Marr Prize Honourable Mention CVPR Best Paper Award Advising & Grants : Supervised 40+ PhD students, with funding from ARC, CSIRO, Microsoft, and firms like OPPO/Tencent. Active in projects such as bushfire detection via video analytics and sign language translation systems. Labs/Teams : Co-founder of the Australian Centre for Robotic Vision (ACRV). Collaborates globally on cross-view localization and autonomous systems.

Ajmal Mianمشاهده پروفایل

استاد

• Artificial Intelligence
• Computer Vision
• Machine Learning
• +۸ مورد دیگر

Ajmal Mian is a Professor of Computer Science at the University of Western Australia (UWA), affiliated with the School of Physics, Maths and Computing. He holds an Australian Research Council Future Fellowship (2022) and leads research in Artificial Intelligence, Computer Vision, and Machine Learning. His work focuses on 3D computer vision, adversarial AI defense, and explainable AI. His research interests include 3D point cloud analysis, face recognition, human action recognition, and remote sensing. He has published over 300 papers and secured major grants from ARC, NHMRC, and DARPA, totaling millions in funding. He has supervised 29 PhD students and mentored 12 postdoctoral researchers. Key projects include 3D diffusion models for scene generation, robust 3D vision systems, and defense against AI deception attacks. He serves as a fellow of IAPR, an ACM Distinguished Speaker, and has editorial roles at IEEE Transactions on Neural Networks and Pattern Recognition. Research Awards: HBF Mid-Career Scientist of the Year, West Australian Early Career Scientist of the Year, IAPR Best Scientific Paper Award. Grants: ARC Discovery Projects, National Intelligence & Security Discovery grants, DARPA grants for AI security. His teaching spans computer vision, machine learning, and programming courses. Collaborations include defense, medical, and agricultural applications.

• Network Security
• Cryptographic Protocols
• Industrial Control System Security
• +۷ مورد دیگر

Professor Ernest Foo is a distinguished academic at Griffith University's School of Information and Communication Technology, specializing in cybersecurity with a focus on industrial control systems and cryptographic protocols. With over 15 years of experience in computer networking, he has established himself as a leading expert in SCADA security and smart grid cybersecurity. His research has significant practical applications in critical infrastructure protection, and he has developed hands-on security training programs that have trained professionals from major Australian utilities and government agencies. Professor Foo's educational background includes: Bachelor of Engineering with Honours in Electronic and Computer Engineering from University of Queensland Doctor of Philosophy from Queensland University of Technology Professor Foo's research interests center around secure cryptographic protocols with specific applications in industrial control system security and cyber physical systems. His work spans SCADA security, smart grid protection, wireless sensor network security, and post-quantum cryptography. He has made significant contributions to understanding vulnerabilities in industrial protocols like Modbus and DNP3, and has pioneered the application of process mining and data mining techniques for attack detection in critical infrastructure systems. His research bridges theoretical security concepts with practical implementations in real-world industrial environments. Professor Foo's recent publications demonstrate a clear trajectory toward increasingly sophisticated security frameworks for critical infrastructure. His work shows a progression from foundational SCADA security research to advanced applications of artificial intelligence, machine learning, and formal methods in cybersecurity. A notable trend is the integration of zero trust principles with industrial control systems, alongside growing emphasis on post-quantum cryptographic solutions. His publications span high-impact journals and conferences in cybersecurity, with increasing focus on anomaly detection in cyber-physical systems and the application of graph-based machine learning techniques to network security challenges. Professor Foo's notable scientific achievements include: Best paper award at the 2nd International Cyber Resilience Conference for "Gap analysis of Intrusion Detection in Smart Grids" Professor Foo has secured significant research funding including an ARC Linkage grant with Powerlink Queensland focused on cyber security for electricity sub-stations. He currently leads multiple research projects including Westpac Micro-Credentials in Financial Crime Investigation, Digital Banking Micro-credentials with ANZ, and research on quantum-safe cryptography. As a dedicated educator, he serves as Program Director for multiple cybersecurity programs including the Master of Cyber Security, and has supervised numerous doctoral and masters students. His Cyber Security: Industrial Control System course, conducted annually from 2013-2018, featured innovative hands-on training with real-world participants from major Australian utilities. Professor Foo has been instrumental in establishing the SCADA security research laboratory with multiple vendor system miniatures running industrial PLCs. His work bridges theoretical security concepts with practical implementations, making significant contributions to the security of industrial control systems and critical infrastructure worldwide.

• Structural Dynamics
• Earthquake Engineering
• Blast and Impact Engineering
• +۳ مورد دیگر

Professor Hong Hao is a John Curtin Distinguished Professor at Curtin University, affiliated with the School of Civil and Mechanical Engineering and the Curtin Research Centre for Infrastructural Monitoring & Protection. His expertise spans Structural Dynamics, Earthquake Engineering, Blast and Impact Engineering, and Structural Health Monitoring. He holds prestigious roles like Fellow of ATSE, ISEAM, and ASCE, and has led organizations such as the International Association of Protective Structures and the Australian Earthquake Engineering Society. Education: BE (Tianjin University, 1982), MSc (UC Berkeley, 1985), PhD (UC Berkeley, 1989). Awards include the Tan Chin Tuan Fellowship and multiple Ko Medals. He has authored over 200 journal articles, with recent work focusing on blast-resistant materials, seismic fragility, and AI-driven structural health monitoring. His research emphasizes resilient infrastructure, including metaconcrete structures, corrosion-resistant materials, and sensor-based damage detection. Ongoing projects involve smart tunnel safety under BLEVE explosions and modular building systems.

• Computer Vision
• Deep Learning
• Reinforcement Learning
• +۷ مورد دیگر

Dr. Chandranath Adak is an Assistant Professor at the Department of Computer Science and Engineering, Indian Institute of Technology Patna (IIT Patna), and concurrently serves as a Visiting Fellow at the School of Computer Science, University of Technology Sydney (UTS), Australia. He holds a Ph.D. in Analytics from UTS (2019) and previously served as an Assistant Professor at Indian Institute of Information Technology Lucknow (IIITL) and the Centre for Data Science at JIS Institute of Advanced Studies, Kolkata. Education: Ph.D. (Analytics), University of Technology Sydney (2019) M.Tech., Computer Science and Engineering, University of Kalyani (2014) B.Tech., Computer Science and Engineering, West Bengal University of Technology (2012) Research Interests: His work spans Computer Vision, Deep Learning, Reinforcement Learning, Document Image Analysis, and AI-driven solutions for healthcare, forensics, and industrial automation. He has pioneered methods in biomarker detection using electrochemical sensors combined with ML models, handwriting analysis for educational and forensic applications, and anomaly detection in industrial systems. His research bridges theoretical advances with real-world applications, such as medical diagnostics and quality control systems. Publications: His recent work includes innovations in biosensor-based medical diagnostics, handwriting evaluation systems, and transformer networks for historical document analysis. These contributions reflect a focus on interdisciplinary applications of AI across healthcare, cultural heritage preservation, and industrial automation. Awards: Start-up Research Grant, SERB, India (2022) Dr. Kalam Doctoral Scholarship, UTS (2018) IEEE CIS Graduate Student Research Grant (2017) Senior Member, IEEE (2024) Teaching & Supervision: Taught courses at UTS including 'Introduction to Data Analytics' and supervised research in machine learning and computer vision. His mentorship emphasizes hands-on experience with AI tools and real-world problem-solving. Labs & Teams: Engaged in collaborative projects at UTS's CIBCI Centre and Griffith University's IIIS, focusing on computational intelligence and sensor-driven AI systems.

Sarath Kodagodaمشاهده پروفایل

استاد

• Robotics
• Mechatronics
• Sensor Fusion
• +۶ مورد دیگر

Professor Sarath Kodagoda is a leading academic and researcher in robotics and mechatronics at the University of Technology Sydney (UTS), where he serves as Acting Director of the UTS Robotics Institute. He specializes in sensor fusion, data processing, and machine learning, with a focus on robotic solutions for infrastructure inspection and assistive technologies. His work includes developing the Robotic Remote Lab teaching facilities and founding the iPipes lab for wastewater infrastructure research. Affiliations: UTS Robotics Institute, Faculty of Engineering & IT Leadership Roles: President of the Australian Robotics & Automation Association, Ambassador for NSW Smart Sensing Network His research interests span robotics, sensor networks, and infrastructure robotics, with notable contributions to pipeline inspection and tactile sensing. He has published over 170 papers, attracted $6M+ in grants, and supervised 12 PhD students now working at Amazon, Google, and ABB. Awards include the UTS Medal for Teaching & Research Integration and multiple national/international innovation awards. Recent work emphasizes robotic systems for wastewater infrastructure, assistive robotics for vision-impaired individuals, and advanced sensor technologies. His articles highlight innovations in tactile sensing skins, 3D object detection (e.g., CaLiJD, LMIINet), and pipeline defect detection (PIPE-CovNet+). Grants: Contracts with Amplitel, NBN Co Ltd, and ARC Linkage Projects Labs/Teams: UTS Robotics Institute, iPipes Lab

Dylan Campbellمشاهده پروفایل

مدرس

• Computer Vision
• Optimization
• Machine Learning
• +۳ مورد دیگر

Dylan Campbell is a Lecturer in Computing at the Australian National University (ANU), affiliated with the ANU College of Systems & Society. His research focuses on computer vision, optimization, and robotics, particularly in 3D vision and deep learning applications. He has held prior roles as a Research Fellow at the University of Oxford’s Visual Geometry Group and ANU’s Australian Centre for Robotic Vision. Campbell holds a PhD from ANU (2018) and a BE in Mechatronic Engineering from UNSW (2012). Research interests include geometric sensor alignment, neural radiance fields, and differentiable optimization layers. He actively supervises students (7 PhD/DPhil, 3 MEng, 9 honours) and teaches advanced courses in computer vision and robotics. Notable awards include the Marr Prize Honourable Mention (2017) and the IEEE Australia Council Postgraduate Student Paper Competition (2018). He has organized workshops at ECCV and CVPR, served as a reviewer for top conferences like CVPR/ICCV/ECCV, and contributed to datasets like SEED4D and RefRef. His work emphasizes efficient training of neural networks and leveraging symmetries in data for long-range connections.

Ehsan Abbasnejadمشاهده پروفایل

دانشیار

• Machine Learning
• Computer Vision
• Natural Language Processing
• +۳ مورد دیگر

Dr. Ehsan Abbasnejad is an Associate Professor at Monash University's Department of Data Science and Artificial Intelligence, and holds adjunct positions at the Australian Institute for Machine Learning (AIML, University of Adelaide) and the Centre for Augmented Reasoning (CAR). He specializes in foundational AI, focusing on vision-language tasks, adversarial machine learning, and reinforcement learning. His work bridges theory with real-world applications in agriculture, energy, healthcare, and sports. Education: PhD in Computer Science from Australian National University (ANU). Research Interests: Machine Learning Theory and Adversarial Defenses Neural Network Robustness and Generalization Multimodal Learning (Vision-Language) Continual and Transfer Learning Applications in Energy, Healthcare, and Robotics Awards: Finalist for Australian AI Academic/Researcher of the Year (2024) Multidisciplinary competition wins (e.g., OzMineral Explorer Challenge) Advising & Grants: Australian Research Council (ARC) Discovery Project on Reinforcement Learning CSIRO's Next Generation Graduate Fund Accepting PhD students in foundational AI and applications Labs & Teams: Director of Foundational Machine Learning & Reasoning at Monash, leading global teams in AI competitions and industry collaborations (Microsoft Research, NEC Labs America).

• Medical Microwave Imaging
• Microwave Engineering
• Millimeter-wave Engineering
• +۳ مورد دیگر

Professor Amin Abbosh is a faculty member at the School of Electrical Engineering and Computer Science, University of Queensland. His research focuses on Medical Microwave Imaging and Millimeter-wave Engineering, with contributions to advanced imaging systems, antenna design, and communication technologies. He leads projects in electromagnetic medical sensing, including portable brain scanners and wearable diagnostic systems. His work integrates applied electromagnetics with AI-driven algorithms, addressing challenges in stroke detection, liver health monitoring, and deep vein thrombosis diagnosis. With over 16 patents and collaborations across biomedical and engineering domains, his research bridges clinical needs with cutting-edge electromagnetic techniques. Key projects include the development of low-cost healthcare monitoring systems and reconfigurable antennas for satellite communications. Research interests span medical imaging systems, antenna array design, and signal processing for healthcare applications. His team innovates in areas like phased arrays, dielectric property analysis, and non-invasive diagnostics. Recent advancements include synthetic microwave focusing techniques and self-supervised deep learning models for clutter removal in imaging. Publications highlight contributions in IEEE journals and conferences, emphasizing clinical applications and device prototyping. Collaborations with institutions like the University of Queensland’s medical faculty and industry partners ensure practical implementation of his research.

• Fracture Mechanics
• Materials Science
• Solid Mechanics
• +۵ مورد دیگر

Professor Chunsheng Lu is a faculty member at Curtin University's School of Civil and Mechanical Engineering within the Faculty of Science and Engineering. He currently holds the position of Professor and serves as Editor-in-Chief of Mechanical Engineering Advances . His research focuses on fracture mechanics, multi-scale modeling, energy materials, nonlinear dynamics, and natural disaster risk analysis. Lu is actively involved in HDR (Masters/PhD) supervision, offering projects on advanced materials modeling and simulations. His research interests include mechanics of energy materials, multi-scale modeling, and fracture statistics. He has contributed to over 200 publications, with recent work emphasizing piezoelectric semiconductors, nanomaterials, and energy storage systems. Lu's teaching spans materials engineering, solid mechanics, and numerical methods.

Jiwon Kimمشاهده پروفایل

دانشیار

• Transport Engineering
• Artificial Intelligence
• Machine Learning
• +۶ مورد دیگر

Associate Professor Jiwon Kim is a leading researcher in Transport Engineering at the University of Queensland's School of Civil Engineering. She serves as Director of Higher Degree by Research and was a DECRA Fellow from 2019-2022. Holding degrees from Korea University and Northwestern University, she specializes in AI/ML applications for transportation systems. PhD, Northwestern University BS & MS, Korea University Her research focuses on Artificial Intelligence and Machine Learning applications in transportation, including: Deep learning for traffic management Reinforcement learning in mixed traffic environments Multi-agent systems for urban mobility optimization Spatiotemporal trajectory analysis Recent publications demonstrate expertise in: Eco-driving strategies Traffic incident prediction Queue length estimation Crash risk modeling Scientific recognition includes: ARC DECRA Fellowship (2019-2022) She supervises doctoral students in: Transportation data analytics Autonomous vehicle systems Intelligent traffic management Current projects explore real-time traffic monitoring, synthetic mobility data generation, and connected vehicle technologies.

• Audio Signal Processing
• Spatial Audio Technology
• Medical Engineering
• +۳ مورد دیگر

Craig Jin is an Associate Professor at the University of Sydney, leading the CARlab (Computing and Audio Research Laboratory) and Spatial Audio Research initiatives within the School of Electrical and Computer Engineering. He holds a BS from Stanford University, an MS from Caltech, and a PhD from the University of Sydney. His work focuses on immersive audio technologies, biomedical signal processing, and assistive technologies for sensory augmentation. Research interests include spatial audio reproduction, binaural processing, acoustic sensing for accessibility, and machine learning applications in signal processing. Key contributions span HRTF interpolation, noise reduction algorithms, and acoustic touch systems for the visually impaired. Recent projects include real-time MRI analysis of vocal tract dynamics and sparse recovery techniques for sound field reconstruction. His publications span over 150 peer-reviewed articles in journals like IEEE Transactions on Audio, Speech, and Language Processing, and conferences such as ICASSP. He advises four current PhD/Master’s students on projects like predictive gesture tracking, voice disorder classification, and magnetic resonance imaging techniques.

Matthew Dunbabinمشاهده پروفایل

استاد

• Environmental Robotics
• Vision-Based Perception
• Marine Robotics
• +۴ مورد دیگر

Matthew Dunbabin is a Professor at Queensland University of Technology (QUT) and Chief Investigator at the Australian Centre for Robotic Vision (ACRV). His expertise spans environmental robotics, with a focus on vision-based autonomous systems for marine conservation, water quality monitoring, and greenhouse gas management. He holds a PhD from QUT and a BEng (Aerospace) from RMIT. Dunbabin has led projects at CSIRO and QUT, developing robots like COTSBot and RangerBot to combat marine pests and promote reef restoration. His work has earned national and international awards, including the 2019 Australian Water Association Award and 2016 Google Impact Challenge. Education: PhD in Engineering, Queensland University of Technology (Queensland, Australia) BEng (Hons) in Aerospace Engineering, Royal Melbourne Institute of Technology (Melbourne, Australia) Research Interests: Environmental robotics and autonomous systems Vision-based perception and classification Marine habitat restoration and pest control Greenhouse gas monitoring via autonomous vehicles Cooperative robotics and sensor networks Awards & Recognition: 2019 Australian Water Association Award (SAMMI Project) 2019 Good Design Award - Sustainability (RangerBot) 2016 Google Impact Challenge People’s Choice Award (RangerBot AUV) 2010 Australian ICT Industry Association National iAward (iSnet) 2006 Queensland Engineering Excellence Innovation Award (Starbug Project) Grants & Projects: ARC Centre of Excellence for Robotic Vision (ACRV): Leading robotic vision research (2014–present) Revolutionising Protection Against Air Pollution: Air quality monitoring networks (2015–present) Establishing Advanced Networks for Air Quality Sensing: Sensor development for urban environments (2017–present) Labs & Collaborations: ACRV at QUT Institute for Future Environments (QUT) CSIRO Autonomous Systems Laboratory (2001–2013)

Ningning Houمشاهده پروفایل

مدرس

• Low Power Wide Area Networks (LPWANs)
• Internet-of-Things
• Wireless Sensing
• +۳ مورد دیگر

Ningning Hou is a Lecturer in Computing (IoT/Networking) at Macquarie University's School of Computing. They are affiliated with the Future Communications Research Centre and Data Horizons Research Centre. Previously, they served as a Postdoctoral Fellow at The Hong Kong Polytechnic University (PolyU) and earned their PhD in Computer Science from PolyU in 2021, followed by a B.Eng. in Telecommunication Engineering from Beijing University of Posts and Telecommunications (2017). Research Interests: Low Power Wide Area Networks (LPWANs) Internet-of-Things (IoT) Wireless Sensing and Networking Wireless Security Recent research focuses on enhancing LoRa communication through innovations like full-duplex gateways (FDLoRa) and scalable logical channels (XGate), addressing challenges in network scalability, signal interference, and security threats. Their work spans topics such as data aggregation efficiency, antenna diversity, and covert channel mitigation. Advising: Actively seeking self-motivated PhD students and researchers for projects in LPWANs, edge computing, and wireless security. Labs/Teams: Involved with Future Communications Research Centre and Data Horizons Research Centre.