Research projects in Information Technology

AI is now trending, and impacting diverse application domains beyond IT, from education (chatGPT) to natural sciences (protein analysis) to social media.

This PhD research focuses on the fusing AI research and cybersecurity research, notably one current direction is on advancing the latest generative AI models for cybersecurity, or vice versa: using cybersecurity to attack AI. 

The student is free to discuss with the supervisor on which topic is of the most interest to him/her, in order to shape the PhD topic. 

Many machine learning (ML) approaches have been applied to biomedical data but without substantial applications due to the poor interpretability of models. Although ML approaches have shown promising results in building prediction models, they are typically data-centric, lack context, and work best for specific feature types. Interpretability is the ability of an ML model to identify the causal relationships among variables. It is crucial for uncovering new insights, justifying a model performance and ultimately building trust in users for further applications.

Rodent behavioural testing is the study of the neural mechanisms underlying emotions [1].  It is used in the study of almost all mental conditions, including PTSD [2], OCD [3] and autism [4].  For example, to measure anxiety, researchers may place a rodent in a large tub, record a top-down video and measure the time spent near the safety of walls [2]. These videos also contain rich information about behavioural patterns, but scoring this manually is time consuming.

Antimicrobial resistance (AMR) is one of the most significant and immediate threats to health in Australia and globally. We are working on harnessing new technologies such as artificial intelligence and next-generation sequencing and to improve the diagnosis, treatment and prevention of AMR infections.

The specific aims of this project are:

1. Rapidly identify AMR and predict treatment responses through use of genomics and machine learning in a clinical context.

This PhD project aims to mitigate the data scarcity of new NLP and Multimodal applications by developing novel active learning algorithms. In this project, the student will leverage large foundation models, such as ChatGPT and GPT4, incorporating the cutting-edge techniques in the other areas, such as reinforcement learning, causality and GFlowNets, to devise novel active learning algorithms for NLP and multimodal applications.

In recent years, social media have become a common plattforms for criminals to stalk, intimidate, manipulate and abuse vulnerable citizens, such as women and youth. A recent survey of students in grades 6 to 9 found that the rates of electronic bullying for girls were between 16% and 19%, whereas the rates for boys were between 11% and 19%. 33.47% of sexually abused girls reported experiencing cyberbullying compared to 17.75% of nonsexually abused girls.

Privacy-Enhancing Technologies (PETs) are a set of cryptographic tools that allow information processing in a privacy-respecting manner. As an example, imagine we have a user, say Alice, who wants to get a service from a service provider, say SerPro. To provide the service, SerPro requests Alice's private information such as a copy of her passport to validate her identity. In a traditional setting, Alice has no choice but to give away her highly sensitive information. 

PhD Studentship Description:

Synthetic data generation has drawn growing attention due to the lack of training data in many application domains. It is useful for privacy-concerned applications, e.g. digital health applications based on electronic medical records. It is also attractive for novel applications, e.g. multimodal applications in meta-verse, which have little data for training and evaluation. This project focuses on synthetic data generation for audio and the corresponding multimodal applications, such as mental health chatbots and digital assistants for negotiations.

Federated learning (FL) is an emerging machine learning paradium to enable distributed clients (e.g., mobile devices) to jointly train a machine learning model without pooling their raw data into a centralised server. Because data never leaves from user clients, FL systematically mitigates privacy risks from centralised machine learning and naturally comply with rigorous data privacy regulations, such as GDPR and Privacy Act 1988.