جست‌وجوی استاد و پژوهشگر

James Mountمشاهده پروفایل

مدرس پاره‌وقت

• Robotics Engineering
• Autonomous Vehicles
• Computer Vision
• +۳ مورد دیگر

Dr. James Mount is a robotics engineer, STEM communicator, and sessional academic at Queensland University of Technology (QUT). He specializes in designing robotic systems for real-world applications and promoting technology education through engaging demonstrations. His research focuses on autonomous vehicles, visual localization, and robotics engineering. Mount holds an Honours Degree in Mechatronics Engineering and is awaiting his PhD Final Thesis Examination reviews from the Australian Centre for Robotic Vision. Education: Bachelor of Mechatronics Engineering (Honours) PhD Candidate in Robotics (pending final thesis review) Research interests include autonomous vehicle infrastructure readiness, vision-based robotics, and STEM education outreach. Notable projects include assessing Australian road infrastructure for autonomous vehicles and developing the Carlie mini-autonomous vehicle platform. He has presented at international conferences such as ICRA and IEEE Robotics and Automation Letters. Mount has delivered interactive STEM presentations to large audiences, including guest appearances on children’s TV shows like Brain Buzz and Juiced TV. His work bridges technical innovation with public engagement, emphasizing ethical AI considerations through interactive demonstrations like the Robotronica Trolley Problem exhibit. Labs/Teams: Core contributor to QUT’s Centre for Robotics and the Australian Centre for Robotic Vision. Lead engineer on the Carlie project and BBot Kickstarter initiative.

Beverley Gorryمشاهده پروفایل

پژوهشگر

• Robotics
• Computer Vision
• SLAM
• +۳ مورد دیگر

Beverley Gorry is a PhD Researcher at the QUT Centre for Robotics, affiliated with Queensland University of Technology. She holds a Bachelor of Engineering (Mechatronics) with First Class Honours and a Bachelor of Information Technology (Computer Science), both from QUT. Her research focuses on Simultaneous Localization and Mapping (SLAM) in dynamic environments, conducted under the supervision of Professor Michael Milford, Dr. Tobias Fischer, and Dr. Alejandro Fontan Villacampa. Before her PhD, Beverley worked as a software engineer at Shell Energy for two years and completed an honours research project on Visual SLAM (VSLAM) in collaboration with QCR and QUT Motorsport. Her technical expertise spans robotics perception, autonomous systems, and mechatronics. Beverley’s work contributes to advancing navigation and localization technologies in complex, changing environments. While no specific awards are noted for her, her research aligns with the Centre’s commitment to robotics innovation and interdisciplinary collaboration.

• Autonomous Vehicles
• Sensor Fusion
• Simultaneous Localization and Mapping (SLAM)
• +۳ مورد دیگر

Stephany Berrio Perez is a Research Fellow at the Australian Centre for Robotics, University of Sydney. Her research focuses on perception and mapping for autonomous vehicles, with expertise in sensor fusion, SLAM, and V2X cooperative perception. She holds a PhD from the University of Sydney (2021) and a Master's from Universidad del Valle (Colombia). Her work addresses challenges in real-time data alignment, bandwidth-efficient V2X communication, and domain adaptation for autonomous systems. Research Interests: Stephany's research spans autonomous vehicle perception, multi-sensor fusion (LiDAR, cameras), and cooperative V2X systems. She has developed frameworks for robust map maintenance, edge case testing, and safety protocols for autonomous navigation. Her international collaborations include projects with France's LS2N laboratory and Cornell University's Co-Sense initiative. Key Research Themes: 3D object detection and domain adaptation Latency-resilient V2X data fusion Human-robot interaction in urban environments Autonomous vehicle safety validation Student Supervision: Stephany advises research students on topics including human-machine interfaces, 3D occupancy prediction, and rural autonomous navigation. Lab Affiliation: Australian Centre for Robotics (ACFR), where her team focuses on real-world deployment of perception systems in complex urban scenarios.

Jochen Trumpfمشاهده پروفایل

استاد

• Control Theory
• Observer Theory
• Optimization on Manifolds
• +۳ مورد دیگر

Prof Jochen Trumpf is a Professor in the School of Engineering at the Australian National University (ANU). He holds an ORCID identifier and has an h-index of 22 with over 2,500 citations. His research focuses on control theory, observer theory, optimization on manifolds, and applications in robotics, computer vision, and wireless communication. He completed his PhD in Mathematics at the University of Würzburg (2002) and held postdoctoral positions at Ben-Gurion University of the Negev and the University of Notre Dame prior to joining ANU in 2003. His research interests emphasize geometric approaches to nonlinear systems, including equivariant filter design, attitude estimation, and SLAM (Simultaneous Localization and Mapping). He has led or co-investigated multiple projects, including the National Facility for Electricity Grid Security and Resilience Research (2023–2025) and studies on distributed collaborative localization and control. He has collaborated widely, with notable contributions to sensor fusion, inertial navigation, and observer-based control strategies. Prof Trumpf’s work integrates mathematical rigor with practical engineering challenges, with over 90 publications in peer-reviewed journals and conferences. His projects often involve cross-disciplinary teams addressing issues in autonomous systems, navigation, and sensor technology. Despite no explicit awards listed, his citation metrics and project leadership reflect significant academic impact. He supervises research students and has been involved in doctoral training programs, such as the Defence Staff PhD Agreement with Joyce Mau (2018–2022). His research extends to applications in robotics, environmental sensing, and smart grid systems, reflecting a balance between theoretical innovation and real-world problem-solving.

Robert Mahonyمشاهده پروفایل

استاد

• Robotics
• Control Systems
• Computer Vision
• +۷ مورد دیگر

Robert Mahony is a Professor in the School of Engineering at the Australian National University (ANU) and an IEEE Fellow. His research focuses on nonlinear systems theory, geometric optimization, and robotics applications, with particular emphasis on control systems, vision-based navigation, and autonomous systems. He holds a PhD in Systems Engineering (1995) and a BSc in Applied Mathematics and Geology (1989), both from ANU. His work spans robot vision, mobile robotics, Lyapunov methods, and nonlinear control, with contributions to inertial navigation, event cameras, and equivariant filtering. He teaches courses such as ENGN4627/ENGN6627 Robotics and leads the Control, Information, Intelligence, Communication, Automation, Decision, and Autonomy Laboratory (CII-CADA Lab) at ANU. Key research themes include observer design for nonlinear systems, sensor fusion, and real-time control algorithms for autonomous vehicles. His publications address challenges in SLAM (Simultaneous Localization and Mapping), multi-object tracking, and robust state estimation under sensor limitations. Awards: IEEE Fellow Labs/Teams: CII-CADA Lab (ANU) Grants/Advising: Course convener for robotics modules; extensive collaborations on fire detection technology and aerospace systems.

Haimei Helen Zhaoمشاهده پروفایل

پژوهشگر ارشد

• Multimodal machine learning
• Generative AI
• Digital health
• +۳ مورد دیگر

Dr. Haimei Helen Zhao is a Research Fellow and ICT Director at the School of Biomedical Engineering, University of Sydney. She holds a PhD from the Sydney AI Centre (2024) and an M.Eng. from Tsinghua University (2020). Her research focuses on AI-driven biomedical technologies, including generative AI, digital health diagnostics, and translational research. Current projects include SmartClot-AI, a blood coagulation testing platform, and generative AI models for stroke prediction. Education: PhD in Computer Science (University of Sydney, 2024), M.Eng. in Computer Science (Tsinghua University, 2020). Research interests span multimodal machine learning, biosensing, and low-cost diagnostic systems. Awards include the 2024 PERIscope Commercialisation Award and 2024 Faculty of Engineering Career Advancement Award. Leadership roles: ICT Director of School of Biomedical Engineering, interim Snow Manager of Ju-Snow Lab, and former DEI Committee Chair. Active in grant development and industry partnerships.

• Computer Vision
• Robotics
• Remote Sensing
• +۳ مورد دیگر

Boying Li is a Research Fellow in the Department of Data Science & AI at Monash University. Their research focuses on advancing computer vision, robotics, and remote sensing technologies. Key contributions include developing SLAM algorithms using semantic planar text features, self-supervised depth estimation systems, and SAR datasets for ship interpretation. Research interests span neuro-symbolic AI frameworks, autonomous navigation, and sensor data fusion. Their work contributes to the UN Sustainable Development Goals through applications in maritime surveillance and autonomous systems. Collaborations involve structural regularities in indoor environments and satellite imagery analysis. Notable outputs include the OpenSARShip dataset (2017–2018) and recent advancements in Hier-SLAM++ (2025). Awards and grants are not explicitly listed in available texts. No lab affiliations or future works are detailed.

Peyman Moghadamمشاهده پروفایل

استاد مدعو

• Embodied AI
• Robotics
• Computer Vision
• +۵ مورد دیگر

Peyman Moghadam is a Principal Research Scientist at CSIRO Data61 and an Adjunct Professor at Queensland University of Technology (QUT). He leads the Embodied AI Research Cluster at CSIRO, focusing on robotics and machine learning intersections. His roles include former Group Leader of Robotic Perception and Acting Leader of the Spatiotemporal AI portfolio within CSIRO's MLAI Future Science Platform. Education: PhD in Robotics from Nanyang Technological University (2012). Professional experiences include Visiting Professorships at ETH Zurich (2022) and University of Bonn (2019), alongside leadership in multidisciplinary projects. Research interests span self-supervised learning, embodied AI, 3D perception, and agricultural robotics. Awards include CSIRO's Julius Career Award, Collaboration Medal, and national/state iAwards for innovation in robotics. He has held adjunct roles at QUT and the University of Queensland. Current roles emphasize AI-driven solutions for scientific challenges, such as Great Barrier Reef conservation and autonomous systems in agriculture. Key projects include the DARPA Subterranean Challenge (2nd place), Hovermap LiDAR technology, and collaborations with industry partners like Emesent and Georgia Tech. His work bridges foundational research with real-world applications in mining, agriculture, and environmental monitoring.

Tobias Fischerمشاهده پروفایل

مدرس ارشد

• Robotics
• Computer Vision
• Computational Cognition
• +۴ مورد دیگر

Dr. Tobias Fischer is a Senior Lecturer at Queensland University of Technology (QUT) in the School of Electrical Engineering & Robotics, Faculty of Engineering. He is an ARC DECRA Fellow and leads research in robotics, computer vision, and computational cognition. His work focuses on enabling robots to interact with humans through perception systems inspired by animal visual systems. He holds a PhD from Imperial College London and has held postdoctoral roles at Imperial College's Personal Robotics Lab. Education: PhD (Imperial College London, 2019), MSc (University of Edinburgh, 2014), BSc (Ilmenau University of Technology, Germany, 2013). Scholarships include the German National Academic Foundation and DECRA Fellowship. Research interests span bio-inspired neural networks, event-based vision, spiking neural networks, and neuromorphic computing. He has led projects funded by Intel, Amazon, Samsung, EU, and Australian Research Council, including $462,000 DECRA grant for adaptive robot positioning. Key awards include the 2023 IEEE Outstanding Paper Award and Queen Mary UK Best PhD in Robotics. Teaching includes units like EGB339 Introduction to Robotics and EGB439 Advanced Robotics. Supervision involves PhD students in place recognition and underwater imagery. His lab, QUT Centre for Robotics, focuses on long-term localization and bio-inspired autonomy.

• Machine Learning
• Computer Vision
• Deep Learning
• +۴ مورد دیگر

Dr. Maryam Haghighat is a Lecturer at QUT's School of Electrical Engineering & Robotics, specializing in Machine Learning, Computer Vision, and Robotics. She holds a PhD from UNSW's School of Electrical Engineering and Telecommunications, where her thesis earned the 2020 UNSW Award for Outstanding Doctoral Thesis. Her postdoctoral research at the University of Oxford's Big Data Institute (2020–2022) focused on medical image analysis for the PathLAKE project. Currently, she leads AI projects with over $10M in funding, including ARC grants and industry collaborations. Her research spans applications in robotics, healthcare, and remote sensing. Notable contributions include developing machine learning algorithms for histopathology image analysis and robust multi-robot localization systems. Awards include the UNSW Doctoral Thesis Award (2020). She actively supervises PhD students in topics like multimodal fusion and 3D visual understanding. Publications highlight advancements in AI-driven medical imaging, hyperspectral transformers, and illumination-aware video compression. Her work bridges theoretical machine learning with practical applications in healthcare, robotics, and environmental monitoring.

Niko Suenderhaufمشاهده پروفایل

استاد

• Robotic Vision
• Computer Vision
• Machine Learning
• +۷ مورد دیگر

Professor Niko Suenderhauf is a leading researcher in robotic vision and AI at Queensland University of Technology (QUT). He focuses on enabling robots to understand and interact with their environment through advancements in SLAM, scene understanding, and semantic mapping. His work bridges robotics, computer vision, and machine learning, aiming to create reliable, relatable, and sustainable robotic systems that collaborate with humans to address societal challenges like climate change and aging populations. His key achievements include pioneering graph-based SLAM methods integrated into the ROS ecosystem, developing object-based semantic SLAM systems, and demonstrating the efficacy of deep learning for place recognition. Collaborations with institutions like DeepMind and Amazon reflect his interdisciplinary approach. Awards include recognition for groundbreaking SLAM research and highly cited publications. Research interests emphasize open-world navigation, privacy-preserving vision, and embodied AI, with a focus on practical applications in construction, environmental monitoring, and domestic robotics. His work on uncertainty quantification and robust perception ensures systems operate safely in unpredictable environments. Professor Suenderhauf actively engages in education, mentoring students across undergraduate and postgraduate levels, and emphasizes the societal impact of technology through collaborations with neuroscience, law, and design experts.

Thanh Nguyen Canhمشاهده پروفایل

مدرس

• Simultaneous Localization and Mapping (SLAM)
• Semantic SLAM
• Active SLAM
• +۷ مورد دیگر

Thanh Nguyen Canh is a Lecturer at University of Engineering and Technology, Vietnam National University (UET VNU) and Teaching/Research Assistant at Japan Advanced Institute of Science and Technology (JAIST), School of Information Science. Primary affiliation is with the Robotics Lab at both institutions where he conducts research in SLAM systems and computer vision. PhD in Information Science at JAIST (2024.10-present) MS in Information Science at JAIST (2022.9-2024.9) BS in Robotics Engineering at UET VNU (2018.8-2022.8) Research focuses on advancing SLAM technologies through semantic understanding and active exploration. Key specialties include Visual-inertial odometry , probabilistic semantic mapping , and multi-sensor fusion for UAV navigation. Current projects integrate deep learning with traditional SLAM pipelines to create robust environmental representations. Publication activity centers on practical robotics applications, with the 2023 ICCAIS paper demonstrating object-oriented semantic mapping for UAV navigation. Research output emphasizes implementable solutions with active GitHub repositories showing continuous development in SLAM systems and reinforcement learning. Supervision involves teaching robotics curriculum at UET while mentoring research assistants at JAIST. Current projects provide hands-on experience with ROS, point cloud processing, and deep learning frameworks. Laboratory work occurs within the Robotics Lab environment at JAIST/UET, utilizing GitHub-hosted tools like probabilistic_semantic_mapping and S3M_SLAM for collaborative development. The lab maintains strong focus on real-world robotics applications with particular emphasis on aerial vehicle navigation systems.