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Research projects in Information Technology

Displaying 1 - 10 of 97 projects.

Detecting Threats in Temporal Networks

A PhD is available for an Australian citizen with our industry partner The AiLECS Lab. The project is Detecting Threats in Temporal Networks. The project aims to better understand communications in networks of criminal activity.

This will be led by OPTIMA AI Dr John Betts and OPTIMA CI Prof. Peter Stuckey at Monash University, and Dr Janis Dalins and Dr Campbell Wilson at The AiLECS Lab.

Brief description of the project:

Supervisor: John Betts

AI-augmented coaching, reporting and its assessment

This project will develop novel AI-augmented gaming coaching and National Disability Insurance Scheme (NDIS) reporting methods for Crank Crew members with autism spectrum disorder (ASD), psychosocial and other barriers. Moreover, the quality of these methods will be assessed in the context of improved coaching, NDIS reporting efficiencies and Crew member outcomes.

Supervisor: Dr Levin Kuhlmann

Brain network mechanisms underlying anaesthetic-induced loss of consciousness

This project focuses on brain network mechanisms underlying anaesthetic-induced loss of consciousness through the application of simultaneous EEG/MEG and neural inference and network analysis methods. In this work we study the effects putative NMDA antagonists xenon, a potent anaesthetic, and nitrous oxide, a weak anaesthetic, on anesthetic-induced changes in brain mechanisms and networks.
Supervisor: Dr Levin Kuhlmann

Model-based depth of anaesthesia monitoring

This project involves model-based depth of anaesthesia monitoring using autoregressive moving average modelling and neural mass and neural field modelling of the electroencephalographic (EEG) signal. This will be achieved through frequency domain and time domain state and parameter estimation techniques to infer model states and parameters in real time to simultaneously track the anaesthetic brain states while inferring underlying physiological changes.
Supervisor: Dr Levin Kuhlmann

Epileptic Seizure Prediction

Seizure prediction algorithms will be developed using the one-of-a-kind ultra-long-term human intracranial EEG dataset obtained from the Neurovista Corporation clinical trial of their Seizure Advisory System, or data from other implantable or wearable devices. This involves consideration of both feature-based machine learning or data science approaches and neural mass parameter estimation approaches to classify the EEG and predict seizures. Recent approaches focus on critical slowing as a marker for seizure susceptability and the influence of brain rhythms.

Supervisor: Dr Levin Kuhlmann

Safe Neuro-symbolic Automated Decision Making with Mathematical Optimisation

Planning is the reasoning side of acting in Artificial Intelligence. Planning automates the selection and the organisation of actions to reach desired states of the world as best as possible. For many real-world planning problems however, it is difficult to obtain a transition model that governs state evolution with complex dynamics.

Supervisor: Dr Buser Say

Safe Continuous-time Automated Decision Making with Mathematical Optimisation

SCIPPlan is a mathematical optimisation based automated planner for domains with i) mixed (i.e., real and/or discrete valued) state and action spaces, ii) nonlinear state transitions that are functions of time, and iii) general reward functions. SCIPPlan iteratively i) finds violated constraints (i.e., zero-crossings) by simulating the state transitions, and ii) adds the violated constraints back to its underlying optimization model, until a valid plan is found.

Supervisor: Dr Buser Say

End-to-End Prediction and Optimisation for Neuro-Symbolic Artificial Intelligence

Optimisation methods, such as mixed integer linear programming, have been very successful at decision-making for more than 50 years. Optimisation algorithms support basically every industry behind the scenes and the simplex algorithm is one of the top 10 most influential algorithms. Major success stories include rostering nurses in hospitals, managing chains of organ transplants, planning production levels for manufacturing, routing delivery trucks for transport, scheduling power stations and electricity grids, to name just a few.

Supervisor: Dr Edward Lam

Branch-and-Cut-and-Price Algorithms for Computing Cost-Effective and Time-Efficient Delivery Routes for Trucks and Drones

Transport and logistics businesses today use a large fleet of trucks and vans to deliver packages widely across a city. Deciding which package should be loaded on to which vehicle and deciding which package should be prioritised are surprisingly difficult computational tasks. State-of-the-art high-performance algorithms are used to calculate routes for the vehicles in order to minimise costs and maximise efficiency.

Supervisor: Dr Edward Lam