About Me
I am a postdoctoral researcher at the University of Sheffield, working with Professor Richard Clayton. My current work focuses on developing computational tools for cardiovascular digital twins as part of the CVD-Net program grant. My main research area centres on the identifiability of input parameters. Primarily, I employ a range of identifiability and sensitivity analysis techniques to accurately capture the dynamics of the cardiovascular system. We are developing novel methods that enable us to navigate parameter space, facilitating the personalisation of cardiovascular models to patient-provided medical data. We are also intrested in social population modelling through agent based models to quantify the impact new healthcare digital twins may have on society. I am also interested in the use of the Julia programming language for computational research.
My research combines computational statistics, mathematics, and data science. I have a broad interest in both applying and developing tools for uncertainty quantification and calibration to address social issues, generating new insights through forecasting and prediction. I am also committed to integrating my research into teaching, fostering a positive learning environment through modern methodologies, including high-performance computing, Julia, and physics-informed neural networks.
In the long term, I aim to establish a distinct research identity in the application and development of uncertainty quantification and forecasting methods across diverse fields. My goal is to build an interdisciplinary research group, prioritising interpretability and impact for end-users, such as policymakers, clinicians, and the public. If you have any questions regarding my work, please feel free to reach out to me on any platform.
Biography
I obtained a BSc in mathematics from Newcastle university, then a MSc in Mathematical Biology from Heriot-Watt University where my thesis title was as follows Pattern formation in Stochastic Partial Differential Equations. During my studies, I focused on modelling and simulation while also having expertise in optimisation methods, bayesian statistics, sensitivity analysis and group theory. I then completed a PhD in computational statistics focusing on developing new methods for model forecasting and sensitivity/identifiability analysis. The thesis tries to strike the right balance between theoretical development and applied impact in the methods developed. The work within the thesis is applicable to any field despite the focus been concentrated on cardiovascular models.
Computational Skills
I am well skilled in: High performance computing, Matlab, Python, R and most recently Julia. Since the begginning of my PhD I have been using a range techniques from this language. The idea behind the Julia language is that it provides the functionality which is given by Python and R however exhibits speed that seen by C. I have been using a range of machiene learning, Ai and modelling packages. In the near future, I am looking to contribute a modelling package to the Julia community which would provide users with a range of objects which could be easily put together to create a 0D model of the cardiovascular system. I am an active memeber within the Julia community looking to contribute to any projects where Julia could be utilised. In the near future I look to use both machine learning (Lux.jl, SciMLSensitivity.jl) and bayesian statistics (Turing.jl) packages to see how they can apply to the cardiovascular system.
I am also a memeber of the Julia-Epi community which aim to use Julia to solve problems within epidimiology. I have a pre print out which looks at a new model for the spread of malaria and answers the question as to, even though deaths descrese each year it is often the case that the number of infections remains high each year. Although my current interests are narrow I feel the application of the techniques would be relevant to any situation where modelling is relevant.
Papers
Please see my google scholar for a list of my published papers - Send me an email to learn more about what I have going on!
PhD Thesis - Development of Identifiability and Sensitivity analysis methods
MSc Thesis - Pattern formation in Stochastic Partial Differential Equations
Software
CirculationModels.jl - is a Julia modelling library, that builds on the ModelingToolkit.jl package, which is part of Julia’s SciML framework. It allows efficient and quick implementation of lumped parameter models using an acausal modelling approach. Due to just-in-time compilation and multiple dispatch, models created using this framework achieve a speed-up of one to two orders of magnitude compared to Matlab and Python, and comparable speeds to native C implementations, while using a high-level approach. The library is modular and extensible.
We believe this library will be useful (dare we say, could be a game changer?) for many colleagues working in this field.
Talks & Posters
INVITED TALKS —
Oct 2024. New variance estimators for efficent and accurate global sensitivity analysis, Sheffield Mathematics.
Jul 2024. The personalisation of cardiovascular models using Julia. Julia Con 2024, Eindhoven.
Apr 2024. Subset selection methods for personalised medicine. SofTMech research Group Seminar Series, University of Glasgow.
Jan 2024. The Personalisation of Cardiovascular Models: Leveraging Sensitivity Analysis. Complex Systems Group Seminar Series, University of Sheffield.
Jul 2023. Hands on Lumped Parameter Models with CirculatorySystemModels.jl. Workshop, Julia Con 2023, Massachusetts Institute of Technology, USA.
Dec 2021. Semi‑analytic Solutions to a 4‑Element Windkessel. Mathematics Research Seminar, Sheffield Hallam University.
CONTRIBUTED
Sep 2024. New Perspectives on Global Sensitivity Analysis for the Creation of Cardiovascular Digital Twins. Virtual Physiological Human 2024, Eindhoven.
June 2023. Global sensitivity analysis, novel impact on policy decisions. Sensitivity analysis of model outputs conference 2023, Florida.
Jul 2022 - Northen Vascular biology forum. Talk: Circulation Models.jl – A fresh approach to lumped parameter modelling. Poster: CirculationModels.jl - Reproducible, Modular Lumped Parameter Systems For Personalisation.
21/22 - Mathematics seminar Sheffield: Semianalytic solutions to a 4 Element Windkessel, MERI winter poster event: Semianalytic solutions to a 4 Element Windkessel, Creating knowledge congerence: Personaliable parameters of 0D cardiovascular models
20/21 - Mathematical Biology Seminar Scotland: Why infections of malaria are so high?, Society of mathematical biology epidimiological conference: New model of malaria transmission, Society of mathematical biology annual conference:
Prizes
- 2024 Travel Award, Julia Org ‑ grant for attendance and presentation at Julia Con 2024 $ 750
- 2024 Best Student Research, Sheffield Hallam ‑ Annual College Conference £ 100
- 2024 Travel Award - Most promising research, Armourers & Brasiers’ Company £ 750
- 2023 Inspirational Teaching Award Nominations, Sheffield Hallam University
- 2023 Travel Award, Julia Org ‑ grant for attendance and presentation at Julia Con 2023 $ 1000
- 2023 Associate Fellow in Higher Education, AdvanceHE
- 2022 Most Impactful Research, Sheffield Hallam ‑ Creating Knowledge Conference £ 100
Teaching & Supervision
23/24 - MSc, BSc, PhD Supervision, Applied Mathematics (12 Lectures, 150 Students), Introduction to Statistics & uncertainty quantification (6 Lectures, 20 students).
22/23 - Introduction to programming (C & Matlab), MSc projects, BSc projects
21/22 - Maths and Control, BSc Projects
20/21 - Introduction to probability, Modelling biological systems
19/20 - Introduction to calculus, Introduction to Bayesian statistics
Reviewing
- Julia con proceedings
- Journal of Medical Engineering & Technology
- Royal Society Journals (Philosophical Transactions A, Proceedings B, Interface)
- Springer Journals (Annals of Biomedical Engineering, Biomechanics and Modeling in Mechanobiology, Statistical Methods & Applications)
- Journal of Uncertainty Quantification
- Taylor & Francis (Journal of Applied Statistics, Statistics in Medicine, Technometrics)
Experience
bp (Jun 2020 - Oct 2020): Working as an engineer for bp I developed a web application that modernised the data on GHG emissions from assets in the North Sea and made it more accessible. During this time, I have:
1) Networked with senior leadership to access data not widely available
2) Used SQL to query large sets of data to find key points to our project
3) Performed correlational analysis in Python to establish trends and patterns in the emissions data
4) Implemented a non json database to make data analysis easier
5) Written reports to outline weakness and solutions in the current methodology
6) Connected an Azure Oracle database to AWS serverless systems
7) Took the role of Scrum master on several occasions
8) Worked with an agile way remotely as part of an international team
Amazon (May 2019 - Oct 2019): I modelled a real-world project for Amazon, which had direct impacts to the UK network. During this time, I:
1) Utilised rigorous scientific methodology to be able to prove my model and robust statistical analysis to prove the significance of my ideas.
2) Produced and implemented the machine learning model within 13 weeks.
3) Wrote backend code in python to make data extraction simpler.
4) Wrote classes in python to enable transformation of large data sets into smaller, simpler ones for senior management to understand.
5) Published weekly documents that inform the whole amazon network about my work.
6) Conducted meetings with senior management to present my model.
7) Worked remotely with a mentor and team across Europe.