About Me
I am a second year PhD student at Sheffield Hallam University working on examining the personalisability of 0D models to cardiovascular problems. My main areas of research are concerned with the identifiability of input parameters. Primarily, I have used a range of identifiability and sensitivity analysis techniques in order to accurately understand the dynamics of a cardiovascular system. We are developing new technqiues which allow us to understand how easily we could conduct a walk through parameter space in order to personalise a cardiovascular model to medical data provided by paitents. I am also intersted in using the computational language Julia along with my research. I am activley working with and developing more physiological models of how the aortic valve dynamics behave.
My long term ambition is to apply the techniques of Kalaman Filtration to identify if this will allow us to obtain better estimates of input parameters. If you would like to ask mysef anymore questions on this subject, 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 am now at the Sheffield Hallam university working under the supervision of: Professor Ian Halliday (UoS), Dr Xu Xu and Dr Torsten Schenkel.
Computational Skills
I am well skilled in: 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 and bayesian statistics 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.
Reviewing
I have contributed to Julia Con 2023 by reviewing talk proposals.
Papers
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
07/12/22 - 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
21/22 - Creating Knowledge conference - Best contriubtion to research.
Teaching & Supervision
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
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.