Analytics Theory and applications for teaching children with AUTISM spectrum disorder
PhDs and postgraduate research
Self-funded PhD students only
School of Computing
October and February
Applications accepted all year round
Applications are invited for a self-funded, 3-year full-time or 6-year part-time PhD project, to commence in October or February.
Machine enhanced therapy/intervention for children with ASD has been propelled by the innovations in human-machine interfaces and computer vision. Impressive results in the sensing and analytics of ASD children’s behaviour have enabled the avenue for more dexterous interaction taking into account their less preference for interacting with non-human agents. Despite the significant attention in machine assisted healthcare for children with ASD, an educational purpose targeted machine assisted system is still missing. The knowledge and valuable data from machine enhanced therapy/intervention has not been converted into actionable application in special education yet.
The goal of this PhD project is to develop a better understanding of how machine assisted education systems are more effective at a reduced burden of human intervention and build a machine assisted education system for children with ASD. To achieve this aim, the state-of-the-art human behaviour sensing and analytics techniques will be transformed into a real application with an emphasis on the curriculum design, affective computing and system integration. The outcome of this PhD project will enable the special education school users to reduce their repeated workload in daily teaching while observing the progress of children behaviour/knowledge with quantitative measurements.The project will involve building a virtual environment based curriculum and knowledge visualisation in the special education domain of ASD, developing an affective computing framework for children behaviour analysis comprising gaze estimation, expression recognition and motion recognition, and the contactless sensory system integration for an education targeted platform. Based on the tangible system, a long-term evaluation of the machine assisted education for children with ASD will be conducted in special education schools. Experiments will be run to assess how effectively the burden of teachers is reduced and how ASD children benefit from the machine assisted teaching of knowledge and skills.
Fees and funding
Funding availability: Self-funded PhD students only.
PhD full-time and part-time courses are eligible for the UK Government Doctoral Loan (UK and EU students only).
2021/2022 fees (applicable for October 2021 and February 2022 start)
PhD and MPhil
Home/EU/CI full-time students: £4,500 p/a**
Home/EU/CI part-time students: £2,250 p/a**
International full-time students: £17,600 p/a*
International part-time students: £8,800 p/a*
PhD by Publication
External candidates: £4,407*
Members of staff: £1,720
All fees are subject to annual increase. If you are an EU student starting a programme in 2021/22 please visit this page.
*This is the 2020/21 UK Research and Innovation (UKRI) maximum studentship fee; this fee will increase to the 2021/22 UKRI maximum studentship fee when UKRI announces this rate in Spring 2021.
Some PhD projects may include additional fees – known as bench fees – for equipment and other consumables, and these will be added to your standard tuition fee. Speak to the supervisory team during your interview about any additional fees you may have to pay. Please note, bench fees are not eligible for discounts and are non-refundable.
You'll need a good first degree from an internationally recognised university (minimum upper second class or equivalent, depending on your chosen course) or a Master’s degree in an Civil Engineering or related area. In exceptional cases, we may consider equivalent professional experience and/or Qualifications. English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.
The candidate should have a UK Honours Degree at 2.1 (or equivalent) in Computing Science or related area. A good understanding of OpenCV and related programming skills are ideally preferred for shortlisting the candidates.
How to apply
We’d encourage you to contact Dr Honghai Liu (email@example.com) to discuss your interest before you apply, quoting the project code CCTS4540219.
When you're ready to apply, you can use our online application form and select ‘Computing and Creative Technologies’ as the subject area. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV.
Our How to Apply page also offers further guidance on the PhD application process.