Research projects in Information Technology

In many branches of science (e.g., Artificial Intelligence, Engineering etc.), the modelling of the problem is done through the use of functions (e.g., f(x) = y). On a very high-level, we can think of Machine Learning as the problem of approximating function f from the pair of measurements (x,y), and Optimization as the problem of finding the value of input x that maximizes the output y given function f.

Automating code generation, SQL query formulation, and data preprocessing pipelines is a crucial step toward intelligent and efficient software development. This project aims to leverage large language models (LLMs) to address these challenges by developing a comprehensive framework that seamlessly integrates LLM capabilities for generating accurate and optimised code, constructing complex SQL queries, and automating data transformations.

Recent advancements in foundation models have significantly improved AI systems' capabilities in autonomous tool usage and complex reasoning. However, their potential for location-based and map-driven reasoning—crucial for optimising navigation, resource discovery, and logistics—remains underexplored. This project aims to address key challenges in this domain, including interpreting complex map visuals, performing spatial and temporal reasoning, and managing multi-step decision-making tasks.

Artificial Intelligence (AI) is revolutionizing the field of Magnetic Resonance Imaging (MRI) by enabling faster, more accurate, and cost-effective image reconstruction. This project explores cutting-edge AI methodologies, focusing on combining data-driven approaches with physics-informed models to tackle challenges in MRI reconstruction. By integrating MRI acquisition physics directly into neural networks, we aim to improve the interpretability and robustness of reconstruction techniques.

This PhD project is part of a larger project that aims to explain the uncertainty of Machine Learning (ML) predictions. To this effect, we must quantify uncertainty, devise algorithms that explain ML predictions and their uncertainty to different stakeholders, and evaluate the effect of the conveyed information.

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. 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.

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.