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

  • To hold, or expect to achieve by 15 August, an Upper Second Class Honours (2:1) Degree or equivalent from a UK institution (or overseas award deemed to be equivalent via UK NARIC) in a related or cognate field.
  • 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

    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:

    Vice Chancellors Research Studentship (VCRS)

    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 support grant (RTSG) allocation to help support the PhD researcher.

    Vice-Chancellor’s Research Bursary (VCRB)

    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 support grant (RTSG) allocation to help support the PhD researcher.

    Vice-Chancellor’s Research Fees Bursary (VCRFB)

    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 support grant (RTSG) allocation to help support the PhD researcher.

    Department for the Economy (DFE)

    The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £15,285 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 support 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



The Doctoral College at Ulster University


Reviews

Profile picture of Adrian Johnston

As Senior Engineering Manager of Analytics at Seagate Technology I utilise the learning from my PhD ever day

Adrian Johnston - PhD in Informatics

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Profile picture of Xin Wei

I received the bachelor’s of engineering degree in computer science and technology from Shangrao Normal University, Jiangxi, China, in 2013; and the master’s degree in computer application and technology from the School of Mathematics and Computer Science, Fujian Normal University, China. When I was pursuing a PhD degree at Ulster University, I continued my research on face recognition and image representation.This long journey has only been possible due to the constant support and encouragement of my first supervisor. I also like to thank my second supervisor for his patience, support and guidance during my research studies. My favourite memory was the days of exercising, gathering and playing with my friends here. If I could speak to myself at the start of my PhD, the best piece of advice I would give myself would be "submit more papers to Journals instead of conferences".

Xin Wei - PhD in Computer Science and Informatics


Profile picture of Jyotsna Talreja Wassan

In the whole PhD ordeal, my supervisory team played a tremendous role:- they are three in a million. They are perfect supervisors who perfectly know which milestones or pathways to be taken during research initiatives, and they understand the roles of virtually all stages in the journey of PhD. They showcased superior abilities in managing and motivating me evoking high standards; demonstrating a commitment to excellence. Jane and Haiying guided me as their daughter and Fiona turned out to be the best of friends.I heard from “Eleanor Roosevelt” that “The future belongs to those who believe in the beauty of their dreams.” The dream with which I grew up to become a Doctor one day, has finally come true. In the journey of PhD, I embraced that a PhD is not just the highest degree in Education but rather it is a life experience where perseverance is the key. I can never forget words from my external examiner Prof Yike Guo, from Imperial College London. His words

Jyotsna Talreja Wassan - PhD in Computer Science and Informatics