Upcoming events

Student Tour of AIML

Date: Tue, 18 May 2021, 4:00 pm - 5:00 pm
Location: AIML, The University of Adelaide, Lot Fourteen l Cnr North Terrace & Frome Road, ADELAIDE SA 5000 Australia

We are opening our doors at AIML to high school students to enable them to discover more about the exciting work we are doing in our world-class research in machine learning, artificial intelligence, computer vision and deep learning!

AIML Research Showcase 2023

Date: Wed, 16 Aug 2023
Location: Australian Wine Centre

More than 100 people from across South Australian government, business and industry came together to get a rare insight into the latest in responsible artificial intelligence (AI) research.

AIML Lightning Talk: Engineering Intelligent Digital Systems

Date: Fri, 1 Sep 2023
Location: AIML

The Centre for Research on Engineering Software Technologies (CREST) team has been developing a trustworthy modularised Digital Twin Platform (DTP) that can be applied to autonomous and safety-critical cyber-physical systems (CPSs), such as smart factories, smart healthcare systems, and autonomous vehicles. The CREST DTP provides simulatable models of target systems (a.k.a digital twin models) based on runtime data streamed from real target systems.

AIML Mid-Week Seminar: Can biological neurons further improve our understanding of artificial neural networks and vice versa?

Date: Wed, 6 Sep 2023
Location: AIML

In the VPNL, we record from visual neurons in the dragonfly brain. A set of these neurons respond to optic-flow, encoding the dragonfly's own motion through the world. This underlies their remarkable ability to hover nearly stationary, then rapidly patrol at over 60KM/hour. Other neurons respond to tiny targets amidst cluttered surrounds, even predicting the target's future location following an occlusion. These target-detecting neurons can implement a winner-takes-all network, allowing for the selection of a single target amidst distractors (e.g. feasting on midges in a swarm).

AIML Mid-Week Seminars: What cat is that? and Machine learning for ecology: Using a CNN to understand the impacts of linear infrastructures on microhylid frogs in Papuea New Guinea

Date: Wed, 18 Oct 2023
Location: AIML

What cat is that? Victor Caquilpan's research project focused on using computer vision models to identify and track feral cats in camera trap images. Feral cats are a significant threat to the environment and accurately identifying them is crucial for conservation efforts. The study explores various methods including deep learning, feature extraction, and image processing to create an effective re-identification (Re-ID) model. The research systematically evaluates these models based on mAP and accuracy. By combining advanced computer vision and machine learning, this research aims to develop efficient solutions for feral cat RE-ID which can help monitor and manage feral cat populations and their impact on wildlife ecosystems.

AIML Mid-week Seminar: Using AI to predict clinical outcomes in dementia

a female doctor working at her computer in her office

Date: Wed, 15 Nov 2023
Location: AIML

In this presentation, Luke discussed the use of artificial intelligence and deep learning to analyse brain images in dementia, cognitively impaired and healthy individuals. The goal is to develop representations of disease progression that can differentiate normal aging from specific pathologies at both group and individual levels. The presentation aims to bridge the gap between group and individual disease progression analyses by combining morphometry, normative, and computational disease modelling techniques. It also outlines various approaches and results obtained so far, with plans to develop comprehensive normative models. This research has the potential to improve dementia severity assessment and guide precision medicine and aged care planning. Future work will explore different patient categories and intervention approaches.

AIML Special Presentation: Explaining the Uncertain - Stochastic Shapley values for Gaussian process models

Date: Mon, 20 Nov 2023
Location: AIML

In the rapidly evolving field of machine learning, it is important to quantify model uncertainty and explain algorithm decisions, especially for safety-critical domains such as healthcare. In this talk, Dr Chau presented a novel approach to explaining Gaussian processes which we term GP-SHAP. our method is based on the popular solution concept of Shapley values extended to stochastic cooperative games, resulting in explanations that are random variables.

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

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 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.