Daniel started his doctoral studies at The Alan Turing Institute in October 2016. Daniel is registered at the University of Warwick. He graduated from the University of Warwick with an integrated master’s degree in mathematics and statistics. His areas of interest are rough path theory and machine learning, with particular application to sound.
Rough path theory is a method of analysis of SDEs driven by highly oscillatory processes, such as Brownian motion. Recent developments in the theory of rough paths have opened the doors for ideas and concepts developed for studying rough paths to be used in a wide variety of applied situations, including the recognition of Chinese handwriting and financial modelling. Combining the rich theory of rough paths to the rapidly growing field of machine learning and big data, the aim of Daniel’s project is to develop tools for sound analysis, recognition and compression.