Timely access to information is essential to perform many economic and social activities. In today’s digital age, information is more and more often provided in digital form. However, the unreliability of Internet access can make it difficult for people living in rural areas to access information online, resulting in missed opportunities to pursue economic and social ventures.
Research projects in Information Technology
The United Nations Development Programme has identified access to information as an essential element to support poverty eradication. People living in poverty are often unable to access information that is vital to their lives, such as information on their entitlements, public services, health, education or work opportunities. Timely access to information is essential to perform many economic, social and leisure activities. In today’s digital age, information is more and more often provided in digital form.
Collaborative problem-solving (CPS) has widely been recognised as an essential skill for success in the 21st century. Because of this, many researchers have focused on trying to better understand CPS in efforts to find out when it is effective, when it is not, and how to make it a teachable skill.
Nowadays more and more intelligence software solutions emerge in our daily life, for example the face recognition, smart voice assitants, and autonomous vehicle. As a type of data-driven solutions, intelligent components learn their decision logic from data corpus in an end-to-end manner and act as a black box. Without rigorous validation and verification, intelligent solutions are error-prone especially when deployed in the real world environment. To monitor, identify, mitigate and fix these defects becomes extremely important to ensure their service quality and user experience.
People with disabilities are excluded from the assistive technology creation process because the methods and tools that are used are inaccessible. This leads to missed opportunities to create more accessible technologies for everyone including assistive technologies. This project will engage people with disabilities in the technology creation process at many levels, from engagement activities, input into designs and creation of technology and the facilitation of independent making of assistive technologies.
Graph neural networks (GNNs) are emerging techniques for AI. As many chemical compounds and proteins in biology can be modelled as graphs, GNNs have great potentials for drug discovery. This research will investigate new GNN based techniques to accelerate the process of drug discovery.
Blockchain technology and its popular cryptocurrencies such as bitcoin and Ethereum have most revolutionary technological advances in recent history, capable of transforming businesses, government, and social interactions. However, there is a darker side to this technology which is the immense energy consumption and potential climate impact of the blockchain and cryptocurrencies.
Graph machine learning, graph neural networks, in particular, is the frontier of deep learning. There has been an exponential growth of research on graph neural networks (GNNs) in the last few years, mainly focusing on how to develop accurate GNN models. The trustworthiness of GNNs is less considered. In this project, we will explore how to develop trustworthy GNN models. The following key aspects will be taken into consideration when developing GNN models.
Connected vehicles need to be aware of their surrounding environments. This is impossible without being dependent on many sensory inputs. Sensor data is continually collected and analysed, in real-time in order to perform time-critical and delay-sensitive actions. There are two major challenges 1) limited computational resources (processing power and memory) on cars, 2) transfer of large sensory data to the cloud may is not feasible.
There are two ways to improve the reliability of machine learning applications: (1) on the reliability of the machine learning model or algorithm and (2) on the reliability of the code implementing the application. This project will mainly focus on the latter case, for which our fellow researchers have not started exploiting it yet. This project hence aims at supporting developers to implement reliable machine learning applications, both at the development phase and release phase.