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.

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.

In EdgeVLMOpt (EVO): Optimizing Vision-Language Models for Resource-Constrained Edge Devices, we aim to develop efficient and scalable techniques to enable the deployment of advanced vision-language models (VLMs) on edge hardware. While VLMs have demonstrated strong capabilities in multimodal reasoning and understanding, their high computational and memory demands pose significant challenges for real-time, on-device applications.

In EdgeFusionAI (EFAI): Real-Time Multi-Sensor Multi-Modal Intelligence on Edge Devices, we aim to design and develop efficient techniques for fusing heterogeneous sensory data, including vision, LiDAR, radar, and other modalities, to enable robust and real-time decision-making on resource-constrained edge platforms. This project focuses on building intelligent systems capable of integrating diverse data sources while addressing the challenges of limited computation, memory, and energy availability at the edge.

Development of a GIS-Based Model for Active Citizenry

Street-Level Environment Recognition On Moving Resource-Constrained Devices

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.

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

This project focuses on the use of AI in Medical Imaging (e.g. CT, MRI, X-Ray, Ultrasound, etc). The work includes segmentation and classification; for example, segmenting tumour from the medical images, and then classify the grade of the tumour. We will use various Deep Learning techniques, such as CNN, and will experiment with a variety of Deep Learning frameworks, such as U-Net, ResNet, etc.

Modern map-based systems and location-based services rely heavily on the ability to efficiently provide navigation services and the capability to search points of interests (POIs) based on their location or textual information. The aim of this project is to build a next-generation navigation system by addressing limitations in the current systems – such as allowing more meaningful distance measures, modeling uncertainty in data sources and queries, and exploiting rich information from several data sources.