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Research projects in Information Technology

Displaying 31 - 40 of 99 projects.


Multimodal Output Generation to Assist Blind People for Data Exploration and Analysis

In the big-data era, the proliferation of data and the widespread adoption of data analytics have made data literacy a requisite skill for all professions, not just specialist data scientists. At the core of data literacy is the ability to detect patterns and trends, or identify outliers and anomalies from data. However, these requirements often rely on visualisations, which creates a severe accessibility issue for blind people.

Supervisor: Dr Lizhen Qu

Combating Human Bias in Teaching and Learning

Human bias, which refers to the differentiative notions, mindsets, and stereotypes that we may preconceive towards different groups of people, has been witnessed in a variety of settings in our daily life, including in teaching and learning. It has been reported that even the most experienced and well-intended teachers may hold biases that they are not aware of towards students. What is worse, such biases often affect teachers’ subsequent interaction with students, and further exacerbate the achievement gaps between different groups of students.

Supervisor: Dr Guanliang Chen

Combating Machine Bias in Teaching and Learning

Education, undoubtedly, is one of the most fundamental means for people to gain personal and professional development. Given its importance, both researchers and practitioners have endeavored to apply various technologies to construct numerous educational systems and tools to facilitate teaching and learning in the past decades. However, it has been widely demonstrated that such systems and tools tend to display bias to certain groups of students.

Supervisor: Dr Guanliang Chen

Science as a public good: improving how we do research with game theory and computation

Science is a public good. The benefits of knowledge are or should be available to everyone, but the way this knowledge is produce often responds to individual incentives [1]. Scientist are not only cooperating with each other, but in many cases competing for individual gains. This structure may not always work for the benefit of science.

Explainable Thermal to Visible Image Translation

Matching thermal spectrum face images against visible spectrum face images has received increased attention in the literature, due to its broad applications in the military, commercial, and law enforcement domains. Thermal emissions from the face images are less sensitive to changes in ambient lighting.

Supervisor: Dr Cunjian Chen

Explainable and Robust Deep Causal Models for AI assisted Clinical Pathology

We are living in the era of the 4th industrial revolution through the use of cyber physical systems. Data Science has revolutionised the way we do things, including our practice in healthcare. Application of artificial intelligence/machine learning to the big data from genetics and omics is well recognized in healthcare, however, its application to the data reported everyday as part of the clinical laboratory testing environment for improvement of patient care is under-utilized. 

Supervisor: Dr Lizhen Qu

Learning from massive amounts of EEG data

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].

Supervisor: Mahsa Salehi

Active Visual Navigation in an Unexplored Environment

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.