Research projects in Information Technology

This PhD project will develop intelligent, data-driven methods that enable electric vehicles (EVs) to serve as mobile energy storage while remaining readily available for transportation. The research will investigate how EV travel, routing, charging and discharging decisions can be jointly optimised using real-time and predicted information about travel demand, traffic conditions, renewable energy availability, electricity prices, charging infrastructure and battery status.

This PhD project will develop intelligent, data-driven methods for integrating diverse autonomous vehicles, such as buses, shuttles, pods, cars and micromobility vehicles, into public transport networks. The research will investigate how heterogeneous fleets can be dynamically routed, scheduled and repositioned in response to changing passenger demand, traffic conditions and public transport schedules, while accounting for differences in vehicle capacity, speed, energy use and service roles.

Play is fundamental and one of the main methods children learn skills, gain independence, and practice daily tasks that lead into adulthood. TechToy Library is an innovative, co-designed initiative creating toys specifically designed for children with cerebral palsy, empowering them to play, learn, and connect. Through collaboration with families, allied health professionals, and people with lived experience, the project develops inclusive toys using cutting-edge technologies like augmented reality and 3D customisation.

For decades, the primary way people who are blind or have low vision (BLV) interact with computers has been through screen readers. These linearise an application or document interface, presenting content as synthetic speech or on a single-line braille display, which the user navigates using keyboard shortcuts or touch gestures on a smartphone. Screen readers have a major limitation: they cannot show graphics. Instead, they rely on alternative text descriptions, which are often incomplete or inadequate.

World models are becoming an important direction in artificial intelligence and robotics. Instead of responding only to what is currently observed, an intelligent agent can use an internal model of the world to represent its surroundings, anticipate what may happen next, and reason about the consequences of possible actions. This ability is particularly important for robots operating around people, where behaviour is dynamic, uncertain, and strongly influenced by context.

Vision-Language-Action (VLA) models are changing the way robots perceive their surroundings, interpret instructions, reason about tasks, and interact with the physical world. By bringing vision, language, and action into a common framework, these models offer a promising route towards robots that can operate more naturally in complex and unfamiliar environments.

Presence is the subjective feeling of "being there" that differentiates virtual reality (VR) systems from other forms of computer interfaces, and has long been thought to be beneficial in applications of VR such as training and psychotherapy. The quantification of presence has largely revolved around the use of subjective questionnaires, despite longstanding problems with existing questionnaires.

This project develops an AI-informed, community-grounded framework to understand and mitigate antimicrobial resistance (AMR) risks within a planetary health context. Building on the risk modelling from Project 1, we will conduct focus groups and interviews with communities in high- and low-risk zones to capture local knowledge, behaviours, and risk perceptions related to AMR.

Large language models increasingly adapt using external feedback to alter inference-time behaviour, persistent state or model parameters. However, the signals that drive these changes may be noisy, biased, incomplete, delayed, correlated with the model’s own errors, or progressively underused during long-context and multi-step processing. This project studies reliable adaptation as a general feedback-mediated problem by distinguishing failures at the feedback source, during signal transmission and during model updating.

This research aims to design a sustainable framework for optimizing distributed computing systems to enhance performance while minimizing energy consumption. Existing scheduling algorithms often fail to consider workload heterogeneity, resulting in suboptimal performance and increased energy costs. This proposal addresses these gaps by introducing dynamic task assignment and workload-aware scheduling algorithms that dynamically adapt to system demands.