Data Analytics has evolved over the years from Descriptive (what has happened) to Diagnostic (why did it happen) to Predictive (what could happen) and Prescriptive (what action could be taken). It is currently in the process of shifting towards Cognitive Analytics, which aims to create cognitive capabilities by learning from interactions with humans, environments and situated artifacts. In parallel, Computational Intelligence (CI), predominantly built upon data analytics, has recently made significant progress owing to increased computation power, improved machine learning algorithm performance, and the availability of big data. It has shown huge potential and to some extent reached industrial strengths offering real- world deployment opportunities such as face-recognition-based security checks and image analysis based medical diagnosis. Nevertheless, CI based applications suffer from challenges around explanability and interpretability due to the opaque nature of learning algorithms. This can lead to the lack of trust and has been identified as a key barrier to the uptake and acceptability of AI innovations.
There is also a gap from the knowledge learned (models and patterns) to problem-solving, namely the capabilities of reasoning and inference compared with application business logic for decision support or application specific functions. Existing CI techniques are still struggling in reasoning and inference. Both data analytics and CI highlight the need of trusted cognitive intelligence in terms of technological evolution of AI techniques as a daily technology and their adoption at scale becoming a driver for economy development.
This project aims to address the aforementioned challenge by marrying strengths of computational intelligence, data analytic and human-level intelligence. It will develop models, algorithms, methods and technologies that enable and support the synergy, symbiosis, and augmentation of human and artificial intelligence. Specifically, the project will first develop symbolic modelling and representation of human-level cognition and decision-making processes, then explore two approaches to enhancing CI systems with explainable and interpretable capabilities. The first is to develop methods to bootstrap or train CI algorithms with the semantic, expandable cognitive models, thus making the CI based learnt findings explainable and interpretable. The second approach is to view CI-based agent/systems and humans as a hybrid intelligent system combining machines' strengths in effective and efficient discovery of implicit knowledge or hidden patterns from large-scale data, and humans’ advantage of conducting cognitive analysis such as reasoning and making instinct judgments under dynamic and multiple factors.
The project will focus on developing methods and mechanism for shared awareness and the collaborative ways, i.e. joint plans and strategies, to achieve shared goals with appropriate explanations in different circumstances and for different purposes. The project is aimed at developing generalizable advanced AI techniques applicable to different use scenarios such as smart healthcare and personalized learning, esp. for people with cognitive impairment. It is expected to generate high-value scientific outputs in top-tier journals and provide inputs to research grant applications. The project is built upon extensive research expertise on data analytics, AI and cognitive analytics in School of Computing, and promote Ulster’s strategic research directions under ongoing initiative such as data science, CARL and AI Centre of excellence.
If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.
The University offers the following awards to support PhD study and applications are invited from UK, EU and overseas for the following levels of support:
Full award (full-time PhD fees + DfE level of maintenance grant + RTSG for 3 years).
This scholarship will cover full-time PhD tuition fees and provide the recipient with £15,000 maintenance grant per annum for three years (subject to satisfactory academic performance). This scholarship also comes with £900 per annum for three years as a research training studentship grant (RTSG) allocation to help support the PhD researcher.
Part award (full-time PhD fees + 50% DfE level of maintenance grant + RTSG for 3 years).
This scholarship will cover full-time PhD tuition fees and provide the recipient with £7,500 maintenance grant per annum for three years (subject to satisfactory academic performance). This scholarship also comes with £900 per annum for three years as a research training studentship grant (RTSG) allocation to help support the PhD researcher.
Fees only award (PhD fees + RTSG for 3 years).
This scholarship will cover full-time PhD tuition fees for three years (subject to satisfactory academic performance). This scholarship also comes with £900 per annum for three years as a research training studentship grant (RTSG) allocation to help support the PhD researcher.
The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £ 15,009 per annum for three years. EU applicants will only be eligible for the fee’s component of the studentship (no maintenance award is provided). For Non-EU nationals the candidate must be "settled" in the UK. This scholarship also comes with £900 per annum for three years as a research training studentship grant (RTSG) allocation to help support the PhD researcher.
Due consideration should be given to financing your studies; for further information on cost of living etc. please refer to: www.ulster.ac.uk/doctoralcollege/postgraduate-research/fees-and-funding/financing-your-studies
As Senior Engineering Manager of Analytics at Seagate Technology I utilise the learning from my PhD ever day
Adrian Johnston - PhD in InformaticsWatch Video
Friday 7 February 2020
Late March 2020
The largest of Ulster's campuses
Monday 2 December 2019
Subject: Computer Science and Informatics
When applying for this PhD opportunity please quote reference number: