Research projects in Information Technology

This project aims to employ advanced machine learning techniques to analyse text, audio, images, and videos for signs of harmful behaviour. Natural language processing algorithms are utilized to examine vast amounts of textual data, identifying keywords, phrases, and sentiment that may indicate extremist views or intentions. Analysing audio involves techniques such as speech recognition, keyword analysis, contextual analysis, audio feature extraction, and machine learning models to identify and assess potentially dangerous content.

The future of autonomous driving systems holds great promise, offering a solution to address the challenges associated with human errors and the mental fatigue of driving. However, there are trade-offs between the modularity (henceforth interpretability) and the efficiency in existing end-to-end modular autonomous driving models. In this PhD project, student is expected to conduct research in the area of end-to-end modular autonomous driving using computer vison and deep learning methods.

In recent years, the rise in cybercrimes has significantly increased the vulnerability of the open internet to various threats and cyber-attacks. Among these, phishing stands out as one of the most perilous crimes worldwide. In a phishing attack, perpetrators create fraudulent websites that mimic legitimate ones (such as fake bank websites). These deceptive sites lure users into disclosing sensitive financial, personal, and confidential information. This project seeks to introduce machine learning and artificial intelligence techniques to effectively detect phishing websites.

The detection of human activities is crucial for effective monitoring purposes. The challenge lies in accurately and promptly identifying various types of activities from videos and images captured in diverse, real-world environments. Both classical machine learning methods and deep learning techniques can be employed to tackle this task.

Deepfakes, derived from "deep learning" and "fake," involve techniques that merge the face images of a target person with a video of a different source person. This process creates videos where the target person appears to be performing actions or speaking as the source person. In a broader context, deepfakes encompass other categories such as lip-sync and puppet-master. Lip-sync deepfakes alter videos to synchronize mouth movements with a provided audio track.

The energy transition to net zero is in full swing! We at Monash University's Faculty of Information Technology (FIT) are in the unique position that we support the transition across an immensely broad range of topics: from model-predictive building control and community battery integration to wind farm optimisation and multi-decade investment planning, we support clever algorithms and data crunching to make decisions automatically and to let humans make informed decisions, too. 

Anomaly detection methods address the need for automatic detection of unusual events with applications in cybersecurity. This project aims to address the efficacy of existing models when applied to real-life data. The goal is to generate new knowledge in the field of time series anomaly detection [1,2] through the invention of methods that effectively learn to generalise patterns of normal from real-life data.

This project is similar in flavour to the Conscious AI project but rather than come from a Philosophical/Neuroscience/Math/Theory angle, this project aims to build self-aware neural networks that are constructed in a way that is inspired by what we know about self-awareness circuits in the brain and the field of self-aware computing. The project will advanced state of the art AI for NLP or vision or both and embed self-awareness modules within these systems.

In collaboration with people from Monash materials engineering, neuroscience and biochemistry we are developing living AI networks where neurons in a dish are grown to form biological neural networks that can be trained to do machine learning and AI tasks in a similar way to artificial neural networks. In this project you will develop machine learning theory that is consistent with the learning that occurs within these biological neural networks, so that these networks can be leveraged for AI applications.

Creating efficient and beneficial user agent interaction is a challenging problem. Challenges include improving performance and trust and reducing over and under reliance. We investigate the development of Explainable AI Systems that can provide explanations of AI agent decisions to human users. Past work on plan explanations primarily focused on explaining the correctness and validity of plans.