Skip to main content

Research projects in Information Technology

Displaying 1 - 10 of 192 projects.


Human-in-the-loop AI for Microgrid Management

Project description

Microgrids aggregate distributed energy resources, bringing energy security to customers, from individual residences to businesses. Microgrids can contribute to net-zero transition to mitigate climate change in the energy sector by integrating renewables, storage, and consumer energy resources, such as demand-responsive loads.

Supervisor: Dr Hao Wang

Physics-informed Neuro-symbolic AI to Capture CO2

The ability to capture carbon is expected to play a key role in the fight against climate change. A new apparatus is being developed at Monash University to capture carbon dioxide (CO2) from the air. This important technology provides a pathway to decrease the CO2 concentration in the atmosphere. A model of the apparatus needs to be developed in order to validate and understand its daily operations.

Supervisor: Dr Buser Say

Computational Australian Sign Language Generation

Computational modelling of sign languages aims to develop technologies that can serve as means for understanding (i.e., recognising) and producing (i.e., generating) a particular sign language. This field of research aims to provide the technological means for two-way mediated communication between hearing and deaf people. However, research on the computational modelling of sign languages is still in its infancy.

Supervisor: Dr Kalin Stefanov

Optimal design of electric bus fleet and charging stations

One of the main roadblocks to widespread electric buses is the charging of their batteries. Charging an electric battery takes substantially longer than filling up a petrol tank: 30-60 minutes with a fast charger, up to hours with a slow one. The drawback of fast chargers is that they are more costly and that it only takes a few of them to generate a substantial load on the current grid. This means that, compared to petrol buses, some changes might need to be introduced in how electric buses are operated.

Supervisor: Dr Pierre Le Bodic

Computational Australian Sign Language Recognition

Computational modelling of sign languages aims to develop technologies that can serve as means for understanding (i.e., recognising) and producing (i.e., generating) a particular sign language. This field of research aims to provide the technological means for two-way mediated communication between hearing and deaf people. However, research on the computational modelling of sign languages is still in its infancy.

Supervisor: Dr Kalin Stefanov

Situation Reasoning in IoT environments

Situation-awareness is the key to enabling intelligent IoT applications. Situation reasoning is used to aggregate multiple contextual information from physical and social sensors using reasoning methods, and convert them into useful high-level knowledge, i.e. situational intelligence. 

Disentangled Representation Learning for Synthetic Data Generation and Privacy Protection

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.

Supervisor: Dr Lizhen Qu

[NextGen] Secure and Privacy-Enhancing Federated Learning: Algorithms, Framework, and Applications to NLP and Medical AI

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. 

Supervisor: Xingliang Yuan

Explainable AI (XAI) in Medical Imaging

Are you interested in applying your AI/DL knowledge to the medical domain?

Point of Care MRI - the PoCeMR projecct

Portable point of care medical devices have revolutionised the way in which people receive medical treatment. It can bring timely and adequate care to people in need but also opens up the opportunity to address the healthcare inequality for the rural and remote.