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Department of Computer Science

Trustworthy Multi-source Learning

Primary supervisor

Additional supervisors

  • Samuel Kaski

Additional information

Contact admissions office

Other projects with the same supervisor


  • Competition Funded Project (Students Worldwide)

This research project is one of a number of projects at this institution. It is in competition for funding with one or more of these projects. Usually the project which receives the best applicant will be awarded the funding. Applications for this project are welcome from suitably qualified candidates worldwide. Funding may only be available to a limited set of nationalities and you should read the full department and project details for further information.

Project description

Morden machine learning tasks such as multi-modal learning, multi-view learning, domain adaptation/transfer learning, zero/few-shot learning, human-in-the-loop modelling, etc. share similar modelling strategies and frameworks. They could be unified as a process of learning from multiple information sources aiming at predictions based on input obtained from different but relevant data sources. On the other hand, there are high noise, missing entries, heterogeneousness, and misalignment (or called gap, shift) between the learned representations/distributions across data sources etc. in these learning tasks. Therefore, how to systematically integrate these uncertainties and heterogeneity while discovering their similarities across tasks become crucial in feature learning.

This project aims at (1) developing a unified theory and algorithms for trustworthy learning from multi-source data, to address common modelling and data challenges raised in the aforementioned learning tasks, and (2) further applying the developed techniques to real-world applications. The aimed theories and frameworks for algorithm development will optimise data usage in hidden representation spaces and improve learning efficiency.

Funding Notes

This project will be funded by a 4 year studentship in partnership with A*STAR Institutes Singapore. Successful candidates will spend their time in both Manchester (years 1 and 4) and Singapore (years 2-3) of the PhD Programme and funding covers tuition fees, stipend and travel allowances. We are able to offer a limited number of studentships to applicants outside the UK. Therefore, full studentships will only be awarded to exceptional quality candidates, due to the competitive nature of this scheme.

Supervisory Team

The supervisory team includes Tingting Mu (Manchester Primary Supervisor), Joey Tianyi Zhou (A*STAR Primary Supervisor) and Samuel Kaski (Manchester Co-supervisor). The supervisory team has a strong record of research success in all the aforementioned learning tasks, with their work published in top-tier machine learning venues, such as TPAMI, JMLR, NerIPS, ICML, etc. The knowledge and research experience of the team will strongly support the proposed project.

How to Apply

Please see application instruction from this link:

Person specification

For information


Applicants will be required to evidence the following skills and qualifications.

  • This project requires mathematical engagement and ability substantially greater than for a typical Computer Science PhD. Give evidence for appropriate competence, as relevant to the project description.
  • You must be capable of performing at a very high level.
  • You must have a self-driven interest in uncovering and solving unknown problems and be able to work hard and creatively without constant supervision.


Applicants will be required to evidence the following skills and qualifications.

  • You will have good time management.
  • You will possess determination (which is often more important than qualifications) although you'll need a good amount of both.


Applicants will be required to address the following.

  • Comment on your transcript/predicted degree marks, outlining both strong and weak points.
  • Discuss your final year Undergraduate project work - and if appropriate your MSc project work.
  • How well does your previous study prepare you for undertaking Postgraduate Research?
  • Why do you believe you are suitable for doing Postgraduate Research?