PhD Studentship: Kernel methods for system identification with application to the analysis and classification of cardiovascular time series

Project title: Kernel methods for system identification with application to the analysis and classification of cardiovascular time series

PhD Supervisor: Andrea Lecchini-Visintini

Supervisory Team: Dr Andrea Lecchini-Visintini, Prof David Simpson

Project description

An emerging research trend in cardiovascular sciences focuses on discovering useful diagnostic information from the dynamic analysis of concurrent physiological measurements, such as the electrocardiogram (ECG), continuous blood pressure, cerebral blood velocity, CO2 levels (capnography) etc. The aim is faster and better targeted treatments for conditions such as stroke and head injury, resulting in improved brain protection and better outcomes for patients. In this context, the objective of this PhD project is to develop new methods for the analysis of such time series of cardiovascular measurements.

System identification denotes the task of building mathematical models of dynamical systems starting from time series of input and output data. It can be seen as a generalisation of learning a functional relation but where the output depends also on past inputs. It includes building “black-box” models, but also the validation of physics-based models. System identification is often rooted in the characterisation of dynamical systems studied in control engineering. This gives system identification a distinctive mathematical background and an advantage in applicative domains including aerospace, processes, and biomedical engineering.

System identification has been mostly approached using statistical parametric estimation. However, in recent years, a new machine learning approach to system identification has emerged based on kernel and regularisation techniques. This approach offers advantages over classical statistical methods, especially when using real-world data where less strict assumptions give more degrees of freedom for tuning the solution. This PhD project aims to contribute to the development of this approach and take it in new directions and into new applications in the biomedical field. Developments of particular interest are the extensions to multivariate systems and to population formulations where data from similar but different systems are combined, the latter being particularly relevant to biomedical data.

The project will have a focus on blood flow and its physiological control in the brain. In this area, the project will benefit from the availability of physics-based models, and of real-world recordings from healthy subjects and clinical patients, and includes collaboration with clinical partners at Southampton General Hospital. The project is also linked with the activities of the international research network CARNet (https://www.car-net.org/). 

Entry Requirements

A very good undergraduate degree (at least a UK 2:1 honours degree, or its international equivalent).

Closing date: 31^st August 2023 for standard admissions. Later applications may be considered depending on the funds remaining in place.  Early applications are encouraged, applicants will be considered on a rolling basis with funding panel allocations taking place every one/two months.

Funding: For UK students, tuition fees and a stipend starting at £17,668 p.a, for up to 3.5 years. International students can apply but should rely on an external source of funding to match fees at the international level..

How To Apply

Apply online: Search for a Postgraduate Programme of Study (soton.ac.uk). Select programme type (Research), 2023/24, Faculty of Physical Sciences and Engineering, next page select “PhD Elect & Elect Eng (Full time)”. In Section 2 of the application form you should insert the name of the supervisor Andrea Lecchini-Visintini.

Applications should include:

• Curriculum Vitae
• Degree Transcripts to date
• Two reference letters

For further information please contact: feps-pgr-apply@soton.ac.uk

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