Modern software allows us to draw symbols (such as Chinese characters, or mathematical symbols) that the computer will then recognise and turn into type. How can these systems be improved, so that they run faster and more accurately?
A key tool is machine learning, whereby the software is 'taught' on a large set of examples, and then draws on its learning to make predictions for subsequent examples. This sort of approach is very widespread, and understanding the mathematical underpinnings is crucial to being able to improve the software in future. Oxford Mathematician Dr Hao Ni is part of a research group working at the frontiers of this subject.
Dr Ni recently spoke at the Oxford Mathematics North meets South colloquium, which was started during this academic year, in which two early career researchers give short talks introducing their research area to the whole department, with the aim of fostering understanding and collaboration between mathematicians working in the north (pure mathematics) and south (applied mathematics) wings of the Andrew Wiles Building, the home of Oxford Mathematics. Dr Ni described how the theory of rough paths can be applied to the study of non-parametric statistics on streamed data and particularly to the problem of regression where the input variable is a stream of information and the dependent response is also (potentially) a path or a stream.
To find out more and to hear Dr Ni speak about her work to the public, come to SoapboxScience in Oxford on Saturday 18th June. SoapboxScience is a novel public outreach platform for promoting women scientists and the science they do.