Dr Feras Dayoub
Senior Lecturer
School of Computer and Mathematical Sciences
Faculty of Sciences, Engineering and Technology
I work at the intersection of computer vision, machine learning, and robotics, specializing in Embodied AI and Robotic Vision as part of the Australian Institute for Machine Learning (AIML) at the University of Adelaide. I co-direct CROSSING, a French-Australian laboratory focused on human-autonomous agent teaming. In addition to my role at AIML, I hold an Adjunct position at Queensland University of Technology (QUT) and serve as an Associate Investigator with the QUT Centre for Robotics. Previously, I was a Chief Investigator at the ARC Centre of Excellence for Robotic Vision. My research is dedicated to advancing the reliable deployment of computer vision and machine learning on mobile robots in real-world environments. I worked on applied robotic vision projects spanning agricultural innovation, environmental conservation, and autonomous infrastructure monitoring. As an educator, I am passionate about teaching programming, computer vision, machine learning, and robotic perception.
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Appointments
Date Position Institution name 2022 - ongoing Senior Lecturer University of Adelaide 2022 - ongoing Adjunct Senior Lecturer Queensland University of Technology 2019 - 2022 Senior Lecturer Queensland University of Technology 2016 - 2019 Centre Research Fellow Center for Excellence in Robotic Robotics Vision (ACRV) 2012 - 2016 Postdoctoral Research Fellow Queensland University of Technology -
Research Interests
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Journals
Year Citation 2023 Pershouse, D., Dayoub, F., Miller, D., & Sünderhauf, N. (2023). Addressing the Challenges of Open-World Object Detection. 2023 Shi, X., Qiao, Y., Wu, Q., Liu, L., & Dayoub, F. (2023). Improving Online Source-free Domain Adaptation for Object Detection by
Unsupervised Data Acquisition.2023 Chapman, N. H., Dayoub, F., Browne, W., & Lehnert, C. (2023). Predicting Class Distribution Shift for Reliable Domain Adaptive Object Detection. IEEE Robotics and Automation Letters, 8(8), 1-8.
Scopus12022 Hall, D., Talbot, B., Bista, S. R., Zhang, H., Smith, R., Dayoub, F., & Sünderhauf, N. (2022). BenchBot environments for active robotics (BEAR): Simulated data for active scene understanding research. International Journal of Robotics Research, 41(3), 259-269.
Scopus2 WoS22022 Rahman, Q. M., Sunderhauf, N., Corke, P., & Dayoub, F. (2022). FSNet: A Failure Detection Framework for Semantic Segmentation. IEEE Robotics and Automation Letters, 7(2), 1-8.
Scopus13 WoS52022 Miller, D., Sunderhauf, N., Milford, M., & Dayoub, F. (2022). Uncertainty for identifying open-set errors in visual object detection. IEEE Robotics and Automation Letters, 7(1), 215-222.
Scopus33 WoS82021 Talbot, B., Dayoub, F., Corke, P., & Wyeth, G. (2021). Robot navigation in unseen spaces using an abstract map. IEEE Transactions on Cognitive and Developmental Systems, 13(4), 791-805.
Scopus16 WoS72021 Rahman, Q. M., Corke, P., & Dayoub, F. (2021). Run-time monitoring of machine learning for robotic perception: a survey of emerging trends. IEEE Access, 9, 20067-20075.
Scopus38 WoS162020 Haviland, J., Dayoub, F., & Corke, P. (2020). Control of the Final-Phase of Closed-Loop Visual Grasping using
Image-Based Visual Servoing.2020 Arain, B., Dayoub, F., Rigby, P., & Dunbabin, M. (2020). Close-Proximity Underwater Terrain Mapping Using Learning-based Coarse
Range Estimation.2019 Skinner, J., Hall, D., Zhang, H., Dayoub, F., & Sünderhauf, N. (2019). The Probabilistic Object Detection Challenge. 2019 Sünderhauf, N., Dayoub, F., Hall, D., Skinner, J., Zhang, H., Carneiro, G., & Corke, P. (2019). A probabilistic challenge for object detection. Nature Machine Intelligence, 1(9), 443.
WoS12018 Ahn, H. S., Sa, I., & Dayoub, F. (2018). Introduction to the Special Issue on Precision Agricultural Robotics and Autonomous Farming Technologies. IEEE Robotics and Automation Letters, 3(4), 4435-4438.
Scopus4 WoS22018 Hall, D., Dayoub, F., Perez, T., & McCool, C. (2018). A rapidly deployable classification system using visual data for the application of precision weed management. Computers and Electronics in Agriculture, 148, 107-120.
Scopus21 WoS112017 Bawden, O., Kulk, J., Russell, R., McCool, C., English, A., Dayoub, F., . . . Perez, T. (2017). Robot for weed species plant-specific management. Journal of Field Robotics, 34(6), 1179-1199.
Scopus144 WoS752017 Sa, I., Lehnert, C., English, A., McCool, C., Dayoub, F., Upcroft, B., & Perez, T. (2017). Peduncle Detection of Sweet Pepper for Autonomous Crop Harvesting-Combined Color and 3-D Information. IEEE Robotics and Automation Letters, 2(2), 765-772.
Scopus95 WoS592016 Sa, I., Ge, Z., Dayoub, F., Upcroft, B., Perez, T., & McCool, C. (2016). Deepfruits: A fruit detection system using deep neural networks. Sensors (Switzerland), 16(8), 1-23.
Scopus916 WoS539 Europe PMC1182015 Dayoub, F., Morris, T., & Corke, P. (2015). Rubbing shoulders with mobile service robots. IEEE Access, 3, 333-342.
Scopus8 WoS82011 Dayoub, F., Cielniak, G., & Duckett, T. (2011). Long-term experiments with an adaptive spherical view representation for navigation in changing environments. Robotics and Autonomous Systems, 59(5), 285-295.
Scopus50 WoS37- Clement, B., Dubromel, M., Santos, P. E., Sammut, K., Oppert, M., & Dayoub, F. (n.d.). Hybrid Navigation Acceptability and Safety. Proceedings of the AAAI Symposium Series, 2(1), 11-17.
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Books
Year Citation 2020 Garg, S., Sünderhauf, N., Dayoub, F., Morrison, D., Cosgun, A., Carneiro, G., . . . Milford, M. (2020). Semantics for Robotic Mapping, Perception and Interaction: A Survey (Vol. 8). United States: Now Publishers.
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Book Chapters
Year Citation 2017 Perez, T., Bawden, O., Kulk, J., Russell, R., McCool, C., English, A., & Dayoub, F. (2017). Overview of mechatronic design for a weed-management robotic system. In D. Zhang, & B. Wei (Eds.), Robotics and Mechatronics for Agriculture (1st ed., pp. 23-49). Boca Raton, USA: CRC Press.
DOI2015 Dayoub, F., Cielniak, G., & Duckett, T. (2015). Eight weeks of episodic visual navigation inside a non-stationary environment using adaptive spherical views. In L. Mejias, P. Corke, & J. Roberts (Eds.), Springer Tracts in Advanced Robotics (Vol. 105, pp. 379-392). SPRINGER-VERLAG BERLIN.
DOI Scopus2 WoS22011 Dayoub, F., Cielniak, G., & Duckett, T. (2011). Long-term experiment using an adaptive appearance-based map for visual navigation by mobile robots. In Lecture Notes in Computer Science (Vol. 6856 LNAI, pp. 400-401). Springer Berlin Heidelberg.
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Conference Papers
Year Citation 2024 Wilson, S., Fischer, T., Dayoub, F., Miller, D., & Sünderhauf, N. (2024). SAFE: Sensitivity-Aware Features for Out-of-Distribution Object Detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV 2023) (pp. 23508-23519). online: IEEE.
DOI Scopus112024 Wu, R., Wang, H., Dayoub, F., & Chen, H. T. (2024). Segment beyond View: Handling Partially Missing Modality for Audio-Visual Semantic Segmentation. In Proceedings of the AAAI Conference on Artificial Intelligence Vol. 38 (pp. 6100-6108). Online: Association for the Advancement of Artificial Intelligence (AAAI).
DOI2024 Yuan, D., Maire, F., & Dayoub, F. (2024). Cross-Attention between Satellite and Ground Views for Enhanced Fine-Grained Robot Geo-Localization. In Proceedings - 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024 (pp. 1238-1245). Online: IEEE.
DOI Scopus32024 Abou-Chakra, J., Dayoub, F., & Sunderhauf, N. (2024). ParticleNeRF: A Particle-Based Encoding for Online Neural Radiance Fields. In Proceedings - 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024 (pp. 5963-5972). Online: IEEE.
DOI Scopus22024 Garg, S., Rana, K., Hosseinzadeh, M., Mares, L., Sünderhauf, N., Dayoub, F., & Reid, I. (2024). RoboHop: Segment-based Topological Map Representation for Open-World Visual Navigation. In Proceedings - IEEE International Conference on Robotics and Automation Vol. 35 (pp. 4090-4097). Yokohama, Japan: IEEE.
DOI Scopus42024 McLeod, S., Chng, C. K., Ono, T., Shimizu, Y., Hemmi, R., Holden, L., . . . Chin, T. J. (2024). Robust Perspective-n-Crater for Crater-based Camera Pose Estimation. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (pp. 6760-6769). Seattle: IEEE.
DOI2024 Yuan, D., Maire, F., & Dayoub, F. (2024). Temporal Attention for Cross-View Sequential Image Localization. In IEEE International Conference on Intelligent Robots and Systems (pp. 7429-7436). Abu Dhabi, United Arab Emirates: IEEE.
DOI2023 Wilson, S., Fischer, T., Sunderhauf, N., & Dayoub, F. (2023). Hyperdimensional Feature Fusion for Out-of-Distribution Detection. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2023) (pp. 2643-2653). Online: IEEE.
DOI Scopus92022 Corke, P., Dayoub, F., Hall, D., Skinner, J., & Sünderhauf, N. (2022). What Can Robotics Research Learn from Computer Vision Research?. In Proceedings of the 19th International Symposium of Robotic Research (ISRR 2019), as published in Springer Proceedings in Advanced Robotics Vol. 20 (pp. 987-1003). Cham, Switzerland: Springer.
DOI2021 Moskvyak, O., Maire, F., Dayoub, F., Armstrong, A. O., & Baktashmotlagh, M. (2021). Robust Re-identification of Manta Rays from Natural Markings by Learning Pose Invariant Embeddings. In DICTA 2021 - 2021 International Conference on Digital Image Computing: Techniques and Applications (pp. 1-8). online: IEEE.
DOI Scopus20 WoS12021 Bista, S. R., Hall, D., Talbot, B., Zhang, H., Dayoub, F., & Sünderhauf, N. (2021). Evaluating the Impact of Semantic Segmentation and Pose Estimation on Dense Semantic SLAM. In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 5328-5335). online: IEEE.
DOI Scopus9 WoS62021 Miller, D., Sunderhauf, N., Milford, M., & Dayoub, F. (2021). Class anchor clustering: A loss for distance-based open set recognition. In Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV 2021) (pp. 3569-3577). online: IEEE.
DOI Scopus102 WoS312021 Moskvyak, O., Maire, F., Dayoub, F., & Baktashmotlagh, M. (2021). Keypoint-aligned embeddings for image retrieval and re-identification. In Proceedings - 2021 IEEE Winter Conference on Applications of Computer Vision, WACV 2021 (pp. 676-685). online: IEEE.
DOI Scopus24 WoS92021 Zhang, H., Wang, Y., Dayoub, F., & Sünderhauf, N. (2021). VarifocalNet: An IoU-aware Dense Object Detector. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 8510-8519). online: IEEE.
DOI Scopus707 WoS2122021 Rahman, Q. M., Sunderhauf, N., & Dayoub, F. (2021). Per-frame mAP Prediction for Continuous Performance Monitoring of Object Detection during Deployment. In Proceedings of the IEEE Winter Conference on Applications of Computer Vision Workshops (WACVW 2021) (pp. 152-160). online: IEEE.
DOI Scopus15 WoS72021 Rahman, Q. M., Sünderhauf, N., & Dayoub, F. (2021). Online Monitoring of Object Detection Performance During Deployment. In Proceedings of the 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021) (pp. 4839-4845). online: IEEE.
DOI Scopus9 WoS32021 Moskvyak, O., Maire, F., Dayoub, F., & Baktashmotlagh, M. (2021). SEMI-SUPERVISED KEYPOINT LOCALIZATION. In ICLR 2021 - 9th International Conference on Learning Representations (pp. 1-11). Virtual only: Open Review.
Scopus142020 Moskvyak, O., Maire, F., Dayoub, F., & Baktashmotlagh, M. (2020). Learning Landmark Guided Embeddings for Animal Re-identification. In 2020 IEEE Winter Applications of Computer Vision Workshops (WACVW) (pp. 12-19). online: IEEE.
DOI Scopus9 WoS82020 Hall, D., Dayoub, F., Skinner, J., Zhang, H., Miller, D., Corke, P., . . . Sunderhauf, N. (2020). Probabilistic object detection: Definition and evaluation. In Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020 (pp. 1020-1029). online: IEEE.
DOI Scopus81 WoS382019 Halodova, L., Dvorrakova, E., Majer, F., Vintr, T., Mozos, O. M., Dayoub, F., & Krajnik, T. (2019). Predictive and adaptive maps for long-term visual navigation in changing environments. In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 7033-7039). Macau, China: IEEE.
DOI Scopus20 WoS132019 Miller, D., Dayoub, F., Milford, M., & Sunderhauf, N. (2019). Evaluating merging strategies for sampling-based uncertainty techniques in object detection. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2019) (pp. 2348-2354). online: IEEE.
DOI Scopus84 WoS382019 Rahman, Q. M., Sunderhauf, N., & Dayoub, F. (2019). Did You Miss the Sign? A False Negative Alarm System for Traffic Sign Detectors. In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 3748-3753). online: IEEE.
DOI Scopus21 WoS122019 Miller, D., Sünderhauf, N., Zhang, H., Hall, D., & Dayoub, F. (2019). Benchmarking sampling-based probabilistic object detectors. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Vol. 2019-June (pp. 42-45).
Scopus112018 Abbas, A., Maire, F., Shirazi, S., Dayoub, F., & Eich, M. (2018). A dynamic planner for object assembly tasks based on learning the spatial relationships of its parts from a single demonstration. In T. Mitrovic, B. Xue, & X. Li (Eds.), Proceedings of AI 2018: Advanced in Artificial Intelligence 31st Australasian Joint Conference Vol. 11320 LNAI (pp. 759-765). Wellington, New Zealand: Springer International Publishing.
DOI Scopus22018 McFadyen, A., Dayoub, F., Martin, S., Ford, J., & Corke, P. (2018). Assisted Control for Semi-Autonomous Power Infrastructure Inspection Using Aerial Vehicles. In IEEE International Conference on Intelligent Robots and Systems (pp. 5719-5726). online: IEEE.
DOI Scopus3 WoS12018 Miller, D., Nicholson, L., Dayoub, F., & Sunderhauf, N. (2018). Dropout Sampling for Robust Object Detection in Open-Set Conditions. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2018) (pp. 3243-3249). Brisbane, Australia: IEEE.
DOI Scopus186 WoS752018 Abbas, A., Maire, F., Dayoub, F., & Shirazi, S. (2018). Combining learning from demonstration and search algorithm for dynamic goal-directed assembly task planning. In ACRA 2018 Proceedings Vol. 2018-December. Online: Australian Robotics and Automation Association. 2017 Dayoub, F., Sunderhauf, N., & Corke, P. I. (2017). Episode-Based Active Learning with Bayesian Neural Networks. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW 2017) Vol. 2017-July (pp. 498-500). Honolulu, Hawaii, USA: IEEE.
DOI Scopus7 WoS32017 Hall, D., Dayoub, F., Kulk, J., & McCool, C. (2017). Towards unsupervised weed scouting for agricultural robotics. In Proceedings - IEEE International Conference on Robotics and Automation (pp. 5223-5230). online: IEEE.
DOI Scopus322017 Hall, D., Dayoub, F., Perez, T., & McCool, C. (2017). A transplantable system for weed classification by agricultural robotics. In 2017 IEEE International Conference on Intelligent Robots and Systems Vol. 2017-September (pp. 5174-5179). Vancouver, BC, Canada: IEEE.
DOI Scopus5 WoS42016 Sunderhauf, N., Dayoub, F., McMahon, S., Talbot, B., Schulz, R., Corke, P., . . . Milford, M. (2016). Place categorization and semantic mapping on a mobile robot. In Proceedings - IEEE International Conference on Robotics and Automation Vol. 2016-June (pp. 5729-5736). online: IEEE.
DOI Scopus115 WoS752016 McCool, C., Sa, I., Dayoub, F., Lehnert, C., Perez, T., & Upcroft, B. (2016). Visual detection of occluded crop: For automated harvesting. In Proceedings - IEEE International Conference on Robotics and Automation Vol. 2016-June (pp. 2506-2512). Stockholm, Sweden: IEEE.
DOI Scopus62 WoS382016 Talbot, B., Lam, O., Schulz, R., Dayoub, F., Upcroft, B., & Wyeth, G. (2016). Find my office: Navigating real space from semantic descriptions. In Proceedings - IEEE International Conference on Robotics and Automation Vol. 2016-June (pp. 5782-5787). online: IEEE.
DOI Scopus19 WoS122015 Schulz, R., Talbot, B., Lam, O., Dayoub, F., Corke, P., Upcroft, B., & Wyeth, G. (2015). Robot navigation using human cues: A robot navigation system for symbolic goal-directed exploration. In Proceedings - IEEE International Conference on Robotics and Automation Vol. 2015-June (pp. 1100-1105). Seattle, WA: IEEE COMPUTER SOC.
DOI Scopus28 WoS122015 Dayoub, F., Dunbabin, M., & Corke, P. (2015). Robotic detection and tracking of Crown-of-Thorns starfish. In IEEE International Conference on Intelligent Robots and Systems Vol. 2015-December (pp. 1921-1928). Hamburg, GERMANY: IEEE.
DOI Scopus29 WoS192015 Sünderhauf, N., Shirazi, S., Jacobson, A., Dayoub, F., Pepperell, E., Upcroft, B., & Milford, M. (2015). Place recognition with convnet landmarks: Viewpoint-robust, condition-robust, training-free. In Proceedings of the Robotics: Science and Systems XI Conference (RSS 2015) Vol. 11 (pp. 1-10). online: Robotics: Science and Systems Foundation.
DOI Scopus324 WoS1912015 Hall, D., McCool, C., Dayoub, F., Sünderhauf, N., & Upcroft, B. (2015). Evaluation of features for leaf classification in challenging conditions. In Proceedings - 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015 (pp. 797-804). HI, Waikoloa: IEEE.
DOI Scopus108 WoS692015 Sünderhauf, N., Shirazi, S., Dayoub, F., Upcroft, B., & Milford, M. (2015). On the performance of ConvNet features for place recognition. In IEEE International Conference on Intelligent Robots and Systems Vol. 2015-December (pp. 4297-4304). Hamburg, GERMANY: IEEE.
DOI Scopus456 WoS3272015 Lam, O., Dayoub, F., Schulz, R., & Corke, P. (2015). Automated topometric graph generation from floor plan analysis. In Australasian Conference on Robotics and Automation, ACRA.
Scopus92014 Lam, O., Dayoub, F., Schulz, R., & Corke, P. (2014). Text recognition approaches for indoor robotics: A comparison. In Australasian Conference on Robotics and Automation, ACRA Vol. 02-04-December-2014.
Scopus42014 Morris, T., Dayoub, F., Corke, P., & Upcroft, B. (2014). Simultaneous localization and planning on multiple map hypotheses. In IEEE International Conference on Intelligent Robots and Systems (pp. 4531-4536). Chicago, IL: IEEE.
DOI Scopus5 WoS32014 Morris, T., Dayoub, F., Corke, P., Wyeth, G., & Upcroft, B. (2014). Multiple map hypotheses for planning and navigating in non-stationary environments. In Proceedings - IEEE International Conference on Robotics and Automation (pp. 2765-2770). Hong Kong, PEOPLES R CHINA: IEEE.
DOI Scopus19 WoS122013 Dayoub, F., Morris, T., Upcroft, B., & Corke, P. (2013). Vision-only autonomous navigation using topometric maps. In N. Amato (Ed.), IEEE International Conference on Intelligent Robots and Systems (pp. 1923-1929). JAPAN, Tokyo: IEEE.
DOI Scopus34 WoS212013 Dayoub, F., Morris, T., Upcroft, B., & Corke, P. (2013). One Robot, eight hours, and twenty four thousand people. In Australasian Conference on Robotics and Automation, ACRA.
Scopus12010 Dayoub, F., Duckett, T., & Cielniak, G. (2010). Short- and long-term adaptation of visual place memories for mobile robots. In Proceedings of the International Symposium on Remembering Who We Are - Human Memory for Artificial Agents - A Symposium at the AISB 2010 Convention (pp. 21-26).
Scopus22008 Dayoub, F., & Duckett, T. (2008). An adaptive appearance-based map for long-term topological localization of mobile robots. In R. Chatila, A. Kelly, & J. P. Merlet (Eds.), 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS (pp. 3364-3369). Nice, FRANCE: IEEE.
DOI Scopus76 WoS48 -
Preprint
Year Citation 2024 Mallick, P., Dayoub, F., & Sherrah, J. (2024). Wasserstein Distance-based Expansion of Low-Density Latent Regions for
Unknown Class Detection.2024 Yuan, D., Maire, F., & Dayoub, F. (2024). Temporal Attention for Cross-View Sequential Image Localization. 2024 Lin, C. -J., Garg, S., Chin, T. -J., & Dayoub, F. (2024). Robust Scene Change Detection Using Visual Foundation Models and
Cross-Attention Mechanisms.2024 Bockman, J., Howe, M., Orenstein, A., & Dayoub, F. (2024). AARK: An Open Toolkit for Autonomous Racing Research. 2024 Garg, S., Rana, K., Hosseinzadeh, M., Mares, L., Sünderhauf, N., Dayoub, F., & Reid, I. (2024). RoboHop: Segment-based Topological Map Representation for Open-World
Visual Navigation.2024 Abou-Chakra, J., Rana, K., Dayoub, F., & Sünderhauf, N. (2024). Physically Embodied Gaussian Splatting: A Realtime Correctable World
Model for Robotics.2024 Abraham, S. S., Garg, S., & Dayoub, F. (2024). To Ask or Not to Ask? Detecting Absence of Information in Vision and
Language Navigation.2023 Holden, L., Dayoub, F., Harvey, D., & Chin, T. -J. (2023). Federated Neural Radiance Fields. 2023 Wu, R., Wang, H., Dayoub, F., & Chen, H. -T. (2023). Segment Beyond View: Handling Partially Missing Modality for
Audio-Visual Semantic Segmentation.2022 Wilson, S., Fischer, T., Dayoub, F., Miller, D., & Sünderhauf, N. (2022). SAFE: Sensitivity-Aware Features for Out-of-Distribution Object
Detection.
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Current Higher Degree by Research Supervision (University of Adelaide)
Date Role Research Topic Program Degree Type Student Load Student Name 2025 Co-Supervisor Exploring Human-Robot Interaction as a Collaborative Model for One-to-Many Teaming in Aerospace Environments Doctor of Philosophy Doctorate Full Time Mrs Donna Lee Duffy 2024 Principal Supervisor Foundation models for goal-oriented reinforcement learning and intrinsic exploration Master of Philosophy Master Full Time Mr Dustin Wyly Craggs 2024 Principal Supervisor Robust Machine Learning Techniques for RF Signal Classification on Sparse and Noisy Digitally Sampled Radar Data Master of Philosophy Master Full Time Mr Sebastian Luke McCormack Cocks 2024 Co-Supervisor Hand-held Object Identification, Segmentation, and Tracking in the Wild Doctor of Philosophy Doctorate Full Time Mr Huy Anh Nguyen 2024 Principal Supervisor Data-driven physically plausible dexterous manipulation Doctor of Philosophy Doctorate Full Time Mr King Hang Wong 2023 Principal Supervisor 3D Scene Understanding and Change Tracking Doctor of Philosophy Doctorate Full Time Mr Chun-Jung Lin 2023 Co-Supervisor 3D indoor Scene Reconstruction Doctor of Philosophy Doctorate Full Time Mr Wenbo Zhang 2023 Principal Supervisor Semi-supervised object detection for mobile robots Master of Philosophy Master Full Time Mr Xiangyu Shi 2022 Co-Supervisor Enhancing Mars rovers using AI-enabled robotic vision Doctor of Philosophy Doctorate Full Time Mr Lachlan William Holden -
Past Higher Degree by Research Supervision (University of Adelaide)
Date Role Research Topic Program Degree Type Student Load Student Name 2022 - 2024 Co-Supervisor Towards Pedestrian Safety Augmented Reality System Master of Philosophy Master Full Time Mr Renjie Wu
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