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AIML Special Presentation: HARNESS: Hierarchical Abstractions and Reasoning for Neuro-Symbolic Robotic Systems—From Perception to Autonomy

Hamid Rezatofighi

In this talk, Dr Rezatofighi will present HARNESS: Hierarchical Abstractions and Reasoning for Neuro-Symbolic Robotic Systems—From Perception to Autonomy, showcasing our work under the DARPA Assured Neuro-symbolic Reasoning (ANSR) program. This project focuses on building fully autonomous systems that unite perception, reasoning, and planning through neuro-symbolic frameworks, emphasising explainability, performance robustness and assurance.

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AIML Research Seminar: Gaëlic Bechu

Gaelic Bechu

Gaëlic Bechu is a PhD student at the Centre for Augmented Reasoning, focusing on the role of AI in embodied systems, including robotics. In this seminar, he will share the latest developments in his PhD research, providing insights into his ongoing work and its applications.

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AIML Special Presentation: Data-centric Computer Vision – A Practice

Dr Xin Yu

Dr Xin Yu is a senior lecturer at the University of Queensland. He is an Australian Research Council (ARC) Discovery Early Career Researcher Award 2023-2025 (DECRA) recipient and an awardee of the prestigious Google Research Scholar Program in 2021. He received his PhD degrees from both the Australian National University and Tsinghua University. His research interests involve various Computer Vision and Machine Learning topics, especially in visual data quality enhancement and recovery, human movement understanding, medical imaging analysis, and multimodal data understanding.

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Lifeblood Information Session

Lifeblood at AIML

Did you know that a plasma donation is needed in Australia every 18 seconds? Giving plasma is an easy, rewarding, and truly life-saving way we can give back to the community. Andrew Chadwick from Lifeblood Australia visited AIML to give a presentation to discuss how donors can get engaged with Lifeblood Australia, highlighting the eligibility requirements needed in order to donate plasma.

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AIML Research Seminar - Professor Erik Dam

Professor Erik Dam

There are thousands of 3D medicals scans available with potential for understanding disease progression and phenotypes. However, without accurate and detailed annotations, machine learning methods are challenged. In particular, biomechanics models need a dense, anatomically meaningful coordinate system to simulate physiology or to do focal statistics across populations or across time. One such analysis is to understand progression of knee osteoarthritis, through statistics of shape models including bones, cartilages, and ligaments derived from thousands of knee MRI. 

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