Data Science and Machine Learning Techniques for Model Parameterisation in Biomedical and Environmental Applications
Abstract"Time series data arise as the output of a wide range of scientific experiments and clinical monitoring techniques. Typically the system under study will either be undergoing time varying changes which can be recorded, or the system will have a time varying signal as input and the response signal will be recorded. Familiar everyday examples of the former include ECG and EEG measurements (which record the electrical activity in the heart or brain as a function of time), whilst examples of the latter occur across scientific research from cardiac cell modelling to battery testing. Such recordings contain valuable information about the underlying system under study, and gaining insight into the behaviour of that system typically involves building a mathematical or computational model of that system which will have embedded within in key parameters governing system behaviour. The problem that we are interested in is inferring the values of these key parameter through applications of techniques from machine learning and data science. Currently used methods include Bayesian inference (Markov Chain Monte Carlo (MCMC), Approximate Bayesian Computation (ABC)), and non-linear optimisation techniques, although we are also exploring the use of other techniques such as probabilistic programming and Bayesian deep learning. We are also interested in developing techniques that will speed up these algorithms including parallelisation, and the use of Gaussian Process emulators of the underlying models Application domains of current interest include modelling of the cardiac cell (for assessing the toxicity of new drugs), understanding how biological enzymes work (for application in developing novel fuel cells), as well as a range of basic science problems. Application domains of current interest include modelling of the cardiac cell (for assessing the toxicity of new drugs), understanding how biological enzymes work (for application in developing novel fuel cells), as well as a range of basic science problems. "
Prerequisites: some knowledge of Python