Research projects in Information Technology

Recent advances in technology mean we can now reappraise the exploration of the past as a future-aligned endeavour. The definition of the ‘past’ here is broad; the reconstruction of a bygone world may derive from relatively recent written texts or photographic archives, from centuries old remains uncovered in archaeological excavations, or even far back in ‘deep time’, to the long-vanished ecologies evidenced in the fossil record.

The existing deep learning based time series classification (TSC) algorithms have some success in multivariate time series, their accuracy is not high when we apply them on brain EEG time series (65-70%). This is because there is a large variation between EEG data of different subjects, so a TSC model cannot generalise on unseen subjects well. In this research project we investigate self-supervised contrastive learning to encode the EEG data. This way we can better model the distribution of our EEG data before classifying it to different mental status. See a recent work here [1].

Identifying vulnerabilities in real-world applications is challenging. Currently, static analysis tools are concerned with false positives; runtime detection tools are free of false positives but inefficient to achieve a full spectrum examination. A generic, scalable and effective vulnerability detection platform, taking advantage of both static and dynamic techniques, is desirable. To further overcome the shortcomings of these techniques, deep learning is more and more involved in static vulnerability localization and improving fuzzing efficiency.

Over the past decades, we have witnessed the emergence and rapid development of deep learning. DL has been successfully deployed in many real-life applications, including face recognition, automatic speech recognition, and autonomous driving, etc. However, due to the intrinsic vulnerability and the lack of rigorous verification, DL systems suffer from quality and security issues, such as the Alexa/Siri manipulation and the autonomous car accidents. Developing secure and trustworthy DL systems is challenging, especially given the limited time budget.

The project involves design, implementation and evaluation of rule-based chatbot to support students when they study information from multiple texts, e.g., reading a few articles about global warming. The bot will support students' self-regulated learning skills which were theorised to promote learning achievements and boost motivation.

This research will unfold over the following 3 phases:

1. Reviewing the literature on self-regulated learning and creating a set of responses from the bot

2. Developing rule-based chatbot

PhD project abstract

In this project, the goal is to develop a new method (using computer vision and machine learning techniques) for robotic navigation in which goals can be specified at a much higher level of abstraction than has previously been possible. This will be achieved using deep learning to make informed predictions about a scene layout and navigating as an active observer in which the predictions inform actions.

This project aims to define the human-centric features of mobile applications (apps) reflecting end-user human values and to model mobile app defects violating those values. The PhD candidate will compile a corpus of existing mobile apps and develop an automated “app feature values miner” to incrementally develop a new taxonomy and characterisation of human values associated with mobile app features that are to be validated with end-users and software engineers. In the next step, the PhD candidate will define a set of human values-based “anti-patterns” for mobile app features, i.e.

Short description

As a pregnancy approaches term (the point at which the foetus is considered fully developed), decisions are made about the timing of birth and the way babies are born. These decisions are incredibly challenging for clinicians and pregnant women. Digital health records, advances in big data, machine learning and artificial intelligence methodologies, and novel data visualisation capabilities have opened up opportunities for a dynamic, ‘Learning Health System’ – where data can be harnessed to inform real-time and personalised decision-making.