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AIML Research Seminar: Anatomically Aware Brain MRI Segmentation of the Cerebral Vasculature
- Date: Tue, 19 Mar 2024
- Location: AIML
As humans, we learn to know what is abnormal by establishing an understanding of what the ‘norm’ actually is. Medical tasks require specific knowledge (e.g. radiologists need to be highly trained), and much is known about healthy, ‘normal’ brain anatomy- documented by anatomical atlases and brain models. This knowledge can then be applied to identify abnormal brain structure from medical images, to identify pathology.
AIML Research Seminar: Computational Algorithms for Human Behaviour Analysis - From Research Endeavor to Industry Relevance
- Date: Tue, 5 Mar 2024
- Location: AIML
Minh provided an overview of his research endeavours and interests, aiming to spark discussions about potential collaborative ventures.
AIML summer research student presentations
- Date: Fri, 23 Feb 2024
- Location: AIML
Our talented summer research students presented their cutting-edge machine learning projects, from medical breakthroughs to AI advancements. They provided an exciting opportunity to witness the future of technology unfold.
[Read more about AIML summer research student presentations]
AIML Special Presentation: Beyond Sight: Robots Mastering Social and Physical Awareness
- Date: Tue, 13 Feb 2024
- Location: AIML
In the rapidly advancing field of robotics, understanding both social and physical dynamics is crucial for seamlessly integrating robots into dynamic human-centric spaces. Operating effectively in such environments requires a robust visual perception system capable of comprehending physical scenes while anticipating and understanding nuanced human social behaviours.
AIML Guest Presentation: Optimisation-centric Generalisations of Bayesian Inference
- Date: Wed, 24 Jan 2024
- Location: AIML
Dr Knoblauch summarises a recent line of research and advocate for an optimization-centric generalisation of Bayesian inference. The main thrust of this argument relies on identifying the tension between the assumptions motivating the Bayesian posterior and the realities of modern Bayesian Machine Learning. Our generalisation is a useful conceptual device, but also has methodological merit: it can address various challenges that arise when the standard Bayesian paradigm is deployed in Machine Learning—including robustness to model misspecification, robustness to poorly chosen priors, and inference in intractable models
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