Potential PhD projects
If you are interested in studying for a PhD at the University of Glasgow with me, please contact me for details of potential projects.
- Personalized cardiac modelling towards the digital twin (parameter inferences, biomarker discovery, etc);
- Multi-physics modelling in cardiac mechanics by coupling blood flow, soft tissue mechanics and electrophysiology with experimental data, including human, rabbit and rat, etc.
- Immersed boundary methods for fluid-structure interaction with applications to heart valves, coronary flow, medical devices, etc;
- Constrained-mixture theory-based growth and remodelling in the heart (myocardial infarction, right ventricle dysfunction, congenital heart)
- Physics-informed surrogate modelling in biomechanics (Gaussian process, inverse problem, uncertainty quantification, sensitivity study, etc);
- Clinical image processing and machine learning-based model construction (deformable image registration, automatic segmentation using machine learning-based methods, 3-D geometry reconstruction and co-registration, etc);
PhD projects
Yuzhang Ge (2020 - 2025) Application of statistical emulation in parameter inference of personalized cardiac model. Minor revision.
Antesar Mohammed A Al Dawoud (2019 - 2024) Mathematical modelling of electrophysiology in myocytes. Minor revision. Theses title: Applications of parameter inference and modelling in cardiac single-cell action potential models. https://theses.gla.ac.uk/84800
David, Dalton (2019 – 2023) Physics-informed emulation with applications in soft-tissue mechanics, co-supervisor, funded by University of Glasgow. https://theses.gla.ac.uk/84552
Yingjie Wang (2019 – 2023) Mathematical modelling cardiac perfusion after myocardial infarction, leading supervisor, funded by Fee-Waiver from University of Glasgow and Chinese CSC scholarship. Minor Revision. https://theses.gla.ac.uk/83789
Yalei Yang (2018 - 2022) Statistical modelling of cardiac perfusion imaging, co-supervisor, co-funded by GSK and University of Glasgow. Thesis title: Myocardial perfusion modelling: MR image processing and statistical inference. https://theses.gla.ac.uk/83057
Alan Lazarus (2017 - 2021) Statistical emulation of biomechanical cardiac models, cosupervisor, co-funded by GSK and University of Glasgow. Thesis title: Surrogate modelling of a patient-specific mathematical model of the left ventricle in diastole. https://theses.gla.ac.uk/82895
Debao Guan (2017 – 2021) Mathematical modelling of Growth and remodeling in healthy and diseased heart, leading supervisor, funded by Fee-Waiver from University of Glasgow and Chinese CSC scholarship. Thesis title: Mathematical modelling of cardiac function: constitutive law, fibre dispersion, growth and remodelling, doi: 10.5525/gla.thesis.82424, http://theses.gla.ac.uk/82424. William Jack Prize for best PhD in Mathematical Sciences, 2021 – 2022
Peter Mortensen (2016 – 2020), informal supervision. Mathematical modelling of the Intercell Coupling of Cardiac Cells, funded by SofTMech centre. Thesis title: Mathematical modelling of the electrical and mechanical properties of cardiac cells coupled with non-muscle cells. doi: 10.5525/gla.thesis.82217, https://theses.gla.ac.uk/82217.
Liuyang Feng (2015 – 2019), informal supervision. Thesis title: Fluid-structure interaction models of mitral valve and left atrium. doi: 10.5525/gla.thesis.76776, https://theses.gla.ac.uk/76776.
Other invovled PhD thesis
Qi, Nan (2016) Modelling of soft tissue and fluid structure interaction with physiological applications. PhD thesis, University of Glasgow. https://theses.gla.ac.uk/7264
Chen, Weiwei (2015) A coupled left ventricle and systemic arteries model. PhD thesis, University of Glasgow. https://theses.gla.ac.uk/7037/
Ma, Xingshuang (2014) Dynamic simulation of the mitral valve. PhD thesis, University of Glasgow. https://theses.gla.ac.uk/4896/
selected MSc projects
Adrian Bartko (10.2023 – 04.2024), Immersed Boundary methods for fluid-structure interaction. University of Glasgow.
Siyu Wang (06.2024 - 08.2024), Discovery of soft tissue strain energy function using machine learning. University of Glasgow
Dhurim, Cakiqi (09.2020 – 04.2021), machine learning based surrogate modelling in cardiac mechanics, submitted on April 2020