This scholarship is open to Australian and New Zealand Citizens and Permanent Residents
The project is a partnership with Turning Point and will focus on the co-design and HCI elements of the larger program of work.
Research projects in Information Technology
Displaying 31 - 40 of 187 projects.
POSITION FILLED: Enhancing Service User Care Pathway Experience through AI-Driven Personalisation
This scholarship is open to Australian and New Zealand Citizens and Permanent Residents
The project is a partnership with headspace National Youth Mental Health Foundation
We are seeking a highly motivated and innovative PhD student interested in exploring the opportunities for using AI to enhance personalisation of services and resource recommendations, ultimately optimising the overall user journey. This project will improve the care pathway experience for young people and families accessing mental health services through the headspace website.
Supervisor: Teresa Wang
Conscious AI
What makes a machine conscious? This PhD would be at the intersection of Philosophy, AI and neuroscience. You would study the latest neuroscience based theories about how consciousness emerges in the brain, as well as the latest AI methods and examine what if any consciousness current AI methods might have and how we might define whether an AI is conscious based on what we know about consciousness in the brain. This wouldn't be a typical machine learning PhD, as many aspects can only be examined on a philosophical and theoretical level.
Supervisor: Dr Levin Kuhlmann
Enhancing Privacy Preservation in Machine Learning
This research project aims to address the critical need for privacy-enhancing techniques in machine learning (ML) applications, particularly in scenarios involving sensitive or confidential data. With the widespread adoption of ML algorithms for data analysis and decision-making, preserving the privacy of individuals' data has become a paramount concern.
Supervisor: Dr Hui Cui
Generative AI for Recommender Systems
A recommender system is a subclass of information filtering/retrieval system that provides suggestions for items that are most pertinent to a particular user without an explicit query. Recommender systems have become particularly useful in this information overload era and have played an essential role in many industries including Medical/Health, E-Commerce, Retail, Media, Banking, Telecom and Utilities (e.g., Amazon, Netflix, Spotify, Linkedin etc).
Supervisor: Teresa Wang
Causal Reasoning for Mental Health Support
This Ph.D. project aims to combine causal analysis with deep learning for mental health support. As deep learning is vulnerable to spurious correlations, novel causal discovery and inference methods will be developed to identify and reason over causal relationships among all associations from the data in literature. As the number of causal relationships is usually much smaller than that of associations, the proposed techniques will achieve explainability by making causes and effects interpretable to psychologists.
Supervisor: Dr Lizhen Qu
Bayesian-network models for human-machine collaboration to protect pollinator-plant interactions in agriculture and natural ecosystems
Ecological systems are dynamic and complex. Many ecosystems support human food production and in turn are impacted by human food production activity. This creates feedback loops between ecosystems, human society and our agriculture, that are typical of complex systems. Ecosystem and social system modelling therefore, including simulation, can play a key role to understand food production and ecosystem interactions.
Supervisor: Assoc Prof Alan Dorin
Formal Explainability in Artificial Intelligence
Artificial Intelligence (AI) models are widely used in decision making procedures in many real-world applications across important areas such as finance, healthcare, education, and safety critical systems. The fast growth, practical achievements and the overall success of modern approaches to AI guarantees that machine learning AI approaches will prevail as a generic computing paradigm, and will find an ever growing range of practical applications, many of which will have to do with various aspects of humans' lives including privacy and safety.
Supervisor: Alexey Ignatiev
AI models for skin conditions management and diagnosis
Problem:
Almost 1 million people in Australia suffer from a long-term skin condition. Without early intervention, skin conditions become chronic conditions with significant health, psychosocial and economic impacts, including anxiety, depression and social isolation. Access to safe, timely, high-quality specialist care leads to better outcomes for individuals. With roughly 2 dermatologists per 100,000 Australians, it’s not surprising how hard it is to have access to dermatologist expertise.Solution:
Supervisor: Dr Yasmeen George
Large language models for detecting misinformation and disinformation
The proliferation of misinformation and disinformation on online platforms has become a critical societal issue. The rapid spread of false information poses significant threats to public discourse, decision-making processes, and even democratic institutions. Large language models (LLMs) have shown tremendous potential in natural language understanding and generation. This research aims to harness the power of LLMs to develop advanced computational methods for the detection and mitigation of misinformation and disinformation. More specific objectives are:
Supervisor: Assoc Prof Thanh Thi Nguyen