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Department of Computer Science

Controlled Synthesis of Virtual Patient Populations with Multimodal Representation Learning

Primary supervisor

Additional supervisors

  • Alejandro Frangi

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

This project invites PhD candidates who are interested in developing generative models that can deal with complex multimodal patient data, contributing to safer, better and faster innovations of medical products via in-silico trials.

In-silico clinical testing/trials (ISTs) are an emerging approach to produce evidence for medical product innovation and regulation. Key to delivering ISTs are generative models, which can produce controlled virtual populations from vast real-world data sources. The generated virtual populations can expand/enrich the diversity of anatomical and physiological scenarios under which novel treatments can be tested within the in-silico trial framework. This thus ensures a broader and more equitable coverage of safety and efficacy for a given medical device.

You will work with multimodal data coming from radiological imaging, electronic health records, and wearable sensor data, etc. The synthesised virtual patient populations will comprise plausible instances of anatomy (from imaging) and physiology (from wearable sensors), and will not be traceable to any specific real patient data. The synthesis will be controllable, e.g., to impose specific conditions on the target virtual population to reproduce concrete inclusion and exclusion criteria in subsequent in-silico trials. You are expected to address modelling challenges in multimodal synthesis by considering at least one of the following aspects:
??? Limited availability of data (e.g., missing modality and insufficient patient instances) with potential inclusion biases.
??? Effective incorporation of domain medical knowledge in the synthesis (e.g., constraints arising from anatomical or physiological considerations).
??? Novel synthesis performance metrics that qualify and quantify the plausibility and representativity of synthetic populations.
??? Patient privacy protection and proven model trustworthiness.

The solutional development will build on state-of-the-art techniques in machine learning and computer vision areas, including but not limited to generative modelling, multimodal learning, zero-shot/few-shot learning, geometric deep learning, manifold learning, and differential privacy. You will have access to data resources available through INSILEX and INSILICO Programmes. The supervisory team has a strong record of research success in machine learning, medical imaging and computer vision in general, and supported by a unique cadre of clinical experts that will provide contextual guidance to the student.

We are eagerly inviting strong and passionate applicants who have:
??? High interest in this project, preferably being experienced in some of the above-mentioned machine learning or computer vision areas.
??? An excellent undergraduate degree in Computer Science or Mathematics (or related discipline), and preferably, a relevant M.Sc. degree.
??? Very good experience with computer programming of mathematical, imaging and/or machine learning models and algorithms.
??? Excellent report writing and presentation skills.
??? Excellent ability to communicate with fellow students and colleagues, and importantly medical experts.

Qualified applicants are strongly encouraged to informally contact the supervising academics Dr. Tingting Mu ( and Prof. Alejandro Frangi ( to discuss your application and research proposal prior to applying.

Person specification

For information


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

  • 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?