PhD Study : Autonomous Decision Analytics by Integrating Machine Learning and Symbolic Reasoning

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Summary

Background and Challenges

Breakthroughs in artificial intelligence (AI) combined with data analytics and high-performance computing has seen the emergence of society-changing AI applications. However, autonomous decision-making and the black-box design of machine learning make the adoption of AI systems complicated and has led to a resurgence in interest in explainability of AI systems. The challenges emphasize the importance of work that ensures that algorithms are not merely efficient and accurate, but transparent and explainable. The research hypothesis is to investigate whether autonomous decision-making can be conducted in a complementary way through constructing and integrating a group of advanced models in order to achieve flexibility and predictivity to fit with different scenarios and improved transparency and enable explanation. Successful decision making analysis must combine the best qualities of both human analysts and computers, especially in high-level planning. This reflects an integration of two most fundamental human cognitive abilities: the ability to learn from the environment, and the ability to reason from what has been learned.

Aim

This research is focused on reconciling the advantages of robust learning in neural networks and reasoning and interpretability of symbolic representation. The aim of this project is to develop an autonomous decision-making system with a unified framework and methods that supports symbolic learning and reasoning using neural networks, and capable of bridging lower-level information processing (for perception and pattern recognition) and higher-level abstract knowledge (for reasoning and explanation).

Anticipated Outcomes/Impact This project provides an opportunity to combine cutting edge research at the intersection of knowledge representation and machine learning to address the above key challenges. The timeliness of this PhD project becomes also apparent in the potential of the above integration to contribute to the long-standing goal of explainable and interpretable AI. We envision the application of these methods to challenging problems arising in logistics, risk-based decision making, planning, and common-sense reasoning.

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.

  • Sound understanding of subject area as evidenced by a comprehensive research proposal

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.

  • Publications record appropriate to career stage
  • A comprehensive and articulate personal statement
  • Applicants will be shortlisted if they have an average of 75% or greater in a first (honours) degree (or a GPA of 8.75/10). For applicants with a first degree average in the range of 70% to 74% (GPA 3.3): If they are undertaking an Masters, then the average of their first degree marks and their Masters marks will be used for shortlisting.

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

The Doctoral College at Ulster University

Key dates

Submission deadline
Friday 7 February 2020
12:00AM

Interview Date
Late March 2020

Preferred student start date
Mid September 2020

Applying

Apply Online  

Contact supervisor

Dr Jun Liu

Other supervisors