Research projects in Information Technology

#sustainability

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.

The Opportunity

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.

The future of AI technology is an ecosystem of many artificial agents acting with autonomy on behalf of human subjects. Examples include, a plethora of self-driving cars, or hordes of automated trading bots in a market. In these scenarios, interactions between artificial agents and human decision makers are abundant [1]. The core of AI has so far focused on applications in which agents help humans (aligned interests), or agents completely oppose humans (zero-sum games).