Research projects in Information Technology

Public transportation is vital for sustainable urban mobility, yet challenges like inefficient first- and last-mile connectivity, and over-reliance on private cars hinder its effectiveness. Autonomous vehicles (AVs) offer transformative potential by enabling diverse, on-demand mobility solutions tailored to specific trip needs, enhancing connectivity, and reducing emissions. However, current research often overlooks the complexities of mixed-vehicle environments, and the development of optimal deployment, routing, and charging strategies.

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.

Planning is the reasoning side of acting in Artificial Intelligence. Planning automates the selection and the organisation of actions to reach desired states of the world as best as possible. For many real-world planning problems however, it is difficult to obtain a transition model that governs state evolution with complex dynamics.

In SmartScaleSystems (S3), we aim to design and build resource management solutions to learn from usage patterns, predict future needs, and allocate resources to minimize latency, energy consumption, and costs of running diverse applications in large-scale distributed systems. This project offers researchers and students a chance to explore cutting-edge concepts in AI-driven infrastructure management, distributed computing, and energy-aware computing, preparing them for impactful roles in industry and research.

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.