Teo completed a mathematics BSc at Bath in 2016 and an MSc in mathematical modelling and scientific computing at Oxford in 2017 - specialising in probability, statistics, and numerical analysis throughout.
Teo completed a mathematics BSc at Bath in 2016 and an MSc in mathematical modelling and scientific computing at Oxford in 2017 – specialising in probability, statistics, and numerical analysis throughout. His Master’s thesis analysed the behaviour of ODEs driven by smooth noise functions; and he also spent a summer researching a numerical approach to solving an optimal stopping problem on a measure valued stochastic process. He remains interested in many areas of SAMBa and is excited to build deeper knowledge in a breadth of topics before choosing a direction to specialise. His hobbies include basketball, music, exercise, and film.
Research project title: Stochastic differential equations and machine learning
Supervisor(s): Tony Shardlow, Eike Müller
Project description: Statistical machine learning and neural network methodologies have seen significant development in recent years with the advent of faster computation and the discovery of efficient optimisation algorithms. Methods based on such techniques have provided state-of-the-art 26results in many high dimensional data tasks, such as image and speech recognition, artificial intelligence, and more recently, in applied mathematics problems. This project is leveraging developments in machine learning to improve methodologies for stochastic differential equations, with particular attention paid to applications in contaminant dispersal. Teo is investigating how deep learning and Bayesian methods can be used to solve a range of problems in this area, such as inferring appropriate PDE and SDE models from contaminant dispersal data, and efficiently approximating solutions to the high dimensional Fokker-Planck equations associate with current models.
Students joining SAMBa in 2017