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Research projects in Information Technology

Displaying 1 - 10 of 191 projects.

Securing Generative AI for Digital Trust

Project description: Generative AI models work by training large networks over vast quantities of unstructured data, which may then be specialised as-needed via fine-tuning or prompt engineering. In this project we will explore all aspects of this process with a focus on increasing trust in the model outputs by reducing or eliminating the incidence of bugs and errors.
Supervisor: Xingliang Yuan

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

Few-Shot Molecular Property Prediction for Computational Drug Discovery

This PhD project will study novel few-shot molecular property prediction algorithms. Compared with the traditional prediction methods that require a large quantity of training data, the studied methods are more practical, with the ability to make predictions with few training data. The few training data is more compatible with real-world cases, where only few molecules are identified having the targeting properties to act as training data. The developed molecular property prediction algorithms will be applied to screen drug molecules targeted at treating GPCR-related diseases. 

Supervisor: Dr Daokun Zhang

Recordkeeping for Empowerment of Marginalised Communities in Australia

This PhD project will explore how individuals in a marginalised community in Australia access, create, and manage information and their preferences for oral, written or digital tools to preserve information for the medium to long-term. The emphasis of the project will be to support and strengthen community-based initiatives and to investigate the factors that influence the choice of tools by different groups and the longevity and sustainability of recordkeeping practices.

Supervisor: Dr Viviane Hessami

LLM models for learning and retrieving software knowledge

The primary objective of this project is to enhance Large Language Models (LLMs) by incorporating software knowledge documentation. Our approach involves utilizing existing LLMs and refining them using data extracted from software repositories. This fine-tuning process aims to enable the models to provide answers to queries related to software development tasks.

Supervisor: Aldeida Aleti

Information Visualisation: the design space of experimental methodologies

Empirical studies in Information Visualisation research have become more commonplace in the past two to three decades. While formerly the research focus was primarily on utilising the power of novel technologies for presenting data and information in innovative ways, perspectives have changed over time so that evaluating the worth of visualisations (for user, for task, for context) is now considered a crucial stage of the research process.

Leveraging Artificial Intelligence for Deorphanisation of G protein-coupled Receptors: Predictive Models and Ligand Design (scholarship provided)

The objective of this project is to use machine learning techniques to help with the drug discovery by modelling structural and sequential data. This project is supported by a supervision team with both machine learning background and Pharmaceutical background with real Pharmaceutical data labeled and ready to use. As we all know, Monash University ranks #2 in the world for Pharmacy and Pharmacology and drug discovery is of significant social benefit.

Supervisor: Teresa Wang