Research projects in Information Technology

We are excited to offer a fully funded PhD position at the Faculty of Engineering, Monash University (Australia). This project focuses on developing new algorithms to equip social robots with the social, cognitive, and communicative skills needed to autonomously engage in meaningful, long-term human-robot interactions.

In this project, we aim to pioneer foundational models specifically designed for time series data—a critical step forward in handling vast and complex temporal datasets generated across domains like healthcare, finance, environmental monitoring, and beyond. While recent advancements in foundation models have shown tremendous success in NLP and computer vision, the unique characteristics of time series data, such as temporal dependencies and lack of rich semantic make it challenging to leverage these models directly for time series tasks.

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.

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.

This project focuses on developing algorithms capable of automatically identifying and categorizing mobile ringtones. This involves leveraging machine learning techniques to analyze audio signals from mobile devices and classify them into different categories or types of ringtones. The activities of the project include gathering a diverse dataset of audio samples representing various types of mobile ringtones.

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.

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.

This project aims to develop robust algorithms capable of identifying and analyzing fingertips extracted from both static images and video footage. Machine learning techniques, particularly computer vision and pattern recognition methods, will be utilized to automate the process of fingertip detection. These methods will be trained to learn patterns from fingertip features and detect them using object detection approaches. A dataset of fingertip images and videos, annotated with ground truth information will be collected.

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.