PhD Study : Cognition Modelling in Hybrid Intelligence for Human Digital Twins

Apply and key information  

Summary

A human digital twin (HDT) is a replica of a human in a real physical world. Research on HDT involves physical 2D/3D modelling of human body and organs, multi-dimension behaviour modelling including their dynamic, and the modelling and replica of the unique capability of a human – understanding, learning and decision making. A HDT should be able to observe (via sensing), learn, evolve, act and anticipate, through real time monitoring, behaviour analysis, prediction and decision making, for the benefits of its counterpart in the physical world.

The proposed project aims to advance cognition modelling towards hybrid intelligence for human digital twinning by modelling and computing the mental action and process of understanding and decision making. This project aims to develop cognitive models that can manipulate learned knowledge from data analytics based on human-level problem-solving skills to support decision making, and which will enable a human digital twin to perform this learning, understanding and acting process automatically. It will develop models, methods and technologies that enable and support the synergy, symbiosis, and augmentation of human and artificial intelligence.

Specifically, the project will first develop cognitive modelling and representation of human-level cognition and decision-making processes such as knowledge graphs. It will then explore a new approach to enhancing data analytic process with explicit semantics and metadata, thus enabling a HDT to consume the knowledge for various business related manipulations. The project will combine 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 is aimed at developing generalizable advanced AI techniques applicable to different use scenarios. It is expected to be undertaken in the context of the PwC-funded ARC project, and generate high-value scientific outputs in top-tier journals and provide inputs to research grant applications.

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.

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%
  • Experience using research methods or other approaches relevant to the subject domain
  • Work experience relevant to the proposed project
  • 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

Recommended reading

[1]Wang W., Ning H., Shi F., Dhelim S., Zhang W., Chen L., A Survey of Hybrid Human-Artificial Intelligence for Social Computing, IEEE Transactions on Human-Machine Systems, DOI: 10.1109/THMS.2021.3131683, vol.52, Issue 3, pp.468-280, June 2022.

[2]Chen L., Ning H., Nugent C., Yu Z., Hybrid Human-Artificial Intelligence, IEEE Computer, 10.1109/MC.2020.2997573, vol.53, no.8, pp.14-17, 2020.

[3]Chen L., Nugent CD., Wang H., A Knowledge-Driven Approach to Activity Recognition in Smart Homes, IEEE Transactions on Knowledge and Data Engineering, ISSN: 1041-434707, Vol.24 No.6, pp.961-974, June 2012

[4]Chen L., Nugent CD., Okeyo G., An Ontology-based Hybrid Approach to Activity Modeling for Smart Homes, IEEE Transactions on Human-Machine Systems (THMS), vol.44, no.1, pp.92-105, Feb. 2014.

[5]Chen L., Hoey J., Nugent C.D., Cook D.J., Yu Z., Sensor-Based Activity Recognition, IEEE Transactions on Systems, Man, and Cybernetics-Part C, doi: 10.1109/TSMCC.2012.2198883, vol.42, no.6, pp.790-808, 2012.

[6]W. Shengli, “Is human digital twin possible?” Computer Methods and Programs in Biomedicine Update, vol. 1, p. 100014, 2021.

[7]M. E. Miller and E. Spatz, “A unified view of a human digital twin,” Human-Intelligent Systems Integration, pp. 1–11, 2022.

[8]D. Mourtzis, J. Angelopoulos, N. Panopoulos, and D. Kardamakis, “A smart iot platform for oncology patient diagnosis based on ai: Towards the human digital twin,” Procedia CIRP, vol. 104, pp. 1686–1691, 2021.

[9]M. Lauer-Schmaltz, P. Cash, J. Hansen, and A. Maier, “Designing human digital twins for behavior-changing therapy and rehabilitation: a systematic review,” Proceedings of the Design Society, vol. 2, pp. 1303–1312, 2022.

[10]I. Toshima, S. Kobashikawa, H. Noto, T. Kurahashi, K. Hirota, and S. Ozawa, “Challenges facing human digital twin computing and its future prospects,” NTT Technical Review, vol. 18, no. 9, pp. 19–24, 2020.

[11]A. Fuller, Z. Fan, C. Day, and C. Barlow, “Digital twin: Enabling technologies, challenges and open research,” IEEE access, vol. 8, pp. 108 952–108 971, 2020.

[12]P. Saariluoma, A. Karvonen, and L. Sorsamaki, “Human digital twins ¨ in acquiring information about human mental processes for cognitive mimetics,” in Frontiers in Artificial Intelligence and Applications. IOS Press, 2021.

The Doctoral College at Ulster University

Key dates

Submission deadline
Monday 27 February 2023
04:00PM

Interview Date
week commencing 17 April 2023

Preferred student start date
18 Sept 2023

Applying

Apply Online  

Contact supervisor

Professor Luke Chen

Other supervisors