This PhD project is funded by a successful ARC Discovery Project grant: "Improving human reasoning with causal Bayesian networks: a user-centric, multimodal, interactive approach" and the successful applicant will work as part of a larger research team.
Research projects in Information Technology
Information overload is now the normal state of affairs for knowledge workers. In keys areas such as COVID-19 research or Machine Learning, the volume of new content is growing seemingly exponentially in recent years. We need to apply recent developments in natural language processing, information retrieval and machine learning to address the problem of information overload by, for instance, developing and integrating advanced search, community detection and summarisation techniques to work on our own literature, or on content such as PubMed or arXiv. For instance, Alibaba's iDST (Inst
In recent years, the production and sales of Electric Vehicles (EVs) have been known as an important growth worldwide. This evolution is mainly due to the severe limits regarding the greenhouse gas emission that cannot be respected by internal combustion vehicles. The expected growth in EV adoption creates large opportunities for grid integration, through flexible smart charging and vehicle to grid (V2G) or vehicle to premises (V2P) to moderate peak demand.
Knowledge graphs are important tools to enable next generation AI through providing explanation for different applications such as question answering. Knowledge graphs are typically sparse, noisy, and incomplete. Knowledge graph reasoning aims to solving this problem by reasoning missing facts from the large scale knowledge base. This project aims to develop novel reinforcement learning technique for knowledge graph reasoning. The developed techniques will be further generalised to more general graphs with graph neural networks.
This project will investigate new methods and tools to support software developers in systematically and effectively identifying relevant/critical users and (direct and indirect) stakeholders for a given project and application domain such as eHealth. The project is interdisciplinary. It combines Software Engineering (SE) and Human-Computer Interaction (HCI) research findings with Design Thinking approaches and industry best practices. The following tasks will be performed:
This project will investigate new methods and tools to support software developers in systematically and effectively eliciting, capturing and modeling human-centric aspects (age, gender, ethnicity, physical and mental limitations, etc.) of user and (direct and indirect) stakeholder needs. The project is interdisciplinary.
Effective allocation of tasks is essential for any socially living group. This project investigates self-organised task allocation, ie groups in which tasks are not centrally assigned to individuals. In self-organised groups, individuals rather select their tasks autonomously based on their own choices and preferences. Under which conditions does this achieve the desired group outcomes?
We have a range of potential research projects on offer in partnership with VIFM - https://www.vifm.org/ - looking at ML techniques in predicting forensic diagnoses / image analysis, across multiple data types found at VIFM. These include atomic data, text data and text documents, medical images and clinical photographs.
This research has high potential to support our IT for social good agenda in addition to its technical attractiveness.
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.
On their own, traffic accidents cause 1.3 million fatalities every year – and improper situational awareness is often a major cause. This project aims to exploit big spatio-temporal data to design intelligent techniques for scheduling and offloading tasks to the cloud and peer vehicles. This will ultimately meet the Quality of Service (QoS) requirements of time-critical road safety applications and increase situational awareness by automatically identifying unsafe road conditions and risky driving behaviors – and sending alerts in real time to affected vehicles.