PhD Study : Intelligent Data Analytics - novelty detection in critical systems

Summary

Context and Rationale

Novelty detection refers to the process of identifying unforeseen anomalies that deviate from normal behaviours in data. It is very important in real world applications especially involving big data acquired from safety-critical systems, where novel conditions rarely occur and knowledge about a novelty is extremely limited or completely unavailable, whilst in such systems a very large number of data samples of the normal condition are usually available.

This project will develop novelty detection algorithms that make use of the sufficient available normal data to train/construct a reliable model, which in turn can be used to predict if new data is normal or abnormal.

Research Methodology

Existing computational novelty detection techniques can broadly be classified into 5 general categories, depending mainly on the assumptions made about the nature of the training data [1]: (i) probabilistic; (ii) distance-based; (iii) reconstruction-based; (iv) domain-based; and (v) information theoretic techniques. Their corresponding limitations are: (i) little control over inherent variability when the training set’s size is small; (ii) inability to efficiently cope with high-dimensional data; (iii) sensitive to pre-defined number of parameters; (iv) difficulty in choosing appropriate kernel function to control the size of boundary enclosing normal data; and (v) difficulty in associating a novelty score with a test point.

To solve these limitations of the state-of-the-art techniques [2], this project will focus on interdisciplinary fundamental research, such as employing the level set methods [3] and bioinspired computational [4] theories, to propose novel hybrid approaches for novelty surveillance on time-varying data, e.g. in capital market, healthcare, autonomous vehicles and other areas.

Relevance of the study

Artificial intelligence has spurred an era of data analytics that has the potential to revolutionise the way we work and live and many industries are realising that the data they collect have substantial value that can be leveraged to improve products, processes, services and productivity. The project will access real world datasets provided by industrial partners of the Cognitive Analytics Research Laboratory (CARL) or by research collaborators of the CARL team. CARL has its centre of operations in the Intelligent Systems Research Centre but is an Ulster University wide initiative focused on exploiting our track record of research excellence into neuro-inspired cognitive analytics, machine learning and computational intelligence.

The successful candidate should have an excellent mathematical foundation and will work within CARL and collaborate with multiple partners in academia and industry to thoroughly validate new algorithms and to create impactful technologies that can address problems experienced by industry today.

Essential criteria

Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.

We may also consider applications from those who hold equivalent qualifications, for example, a Lower Second Class Honours Degree plus a Master’s Degree with Distinction.

In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications.

• Experience using research methods or other approaches relevant to the subject domain

Desirable Criteria

If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.

• First Class Honours (1st) Degree
• Masters at 70%
• For VCRS Awards, Masters at 75%
• Publications - peer-reviewed

Funding and eligibility

The University offers the following levels of support:

Vice Chancellors Research Studentship (VCRS)

The following scholarship options are available to applicants worldwide:

• Full Award: (full-time tuition fees + £19,000 (tbc))
• Part Award: (full-time tuition fees + £9,500)
• Fees Only Award: (full-time tuition fees)

These scholarships will cover full-time PhD tuition fees for three years (subject to satisfactory academic performance) and will provide a £900 per annum research training support grant (RTSG) to help support the PhD researcher.

Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Please note: you will automatically be entered into the competition for the Full Award, unless you state otherwise in your application.

Department for the Economy (DFE)

The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £19,000 (tbc) 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 support grant (RTSG) allocation to help support the PhD researcher.

• Candidates with pre-settled or settled status under the EU Settlement Scheme, who also satisfy a three year residency requirement in the UK prior to the start of the course for which a Studentship is held MAY receive a Studentship covering fees and maintenance.
• Republic of Ireland (ROI) nationals who satisfy three years’ residency in the UK prior to the start of the course MAY receive a Studentship covering fees and maintenance (ROI nationals don’t need to have pre-settled or settled status under the EU Settlement Scheme to qualify).
• Other non-ROI EU applicants are ‘International’ are not eligible for this source of funding.
• Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Due consideration should be given to financing your studies. Further information on cost of living