Rough Path and Machine Learning Research Group


Rough Path and Machine Learning

Rough Path and Machine Learning is a group of active researchers at the Department of Mathematics at UCL with the aim to develop mathematical and numerical toolsets based on rough path theory to advance machine learning research on analysing multi-modal complex data.

Data streams are ubiquitous in everyday life. They record complex sequential information, which can be generated by individual activity, business processes and sensors; examples range from electronic financial trading records to human-computer interactions.  Machine learning has achieved significant success in analysing and extracting useful information from multimodal data streams. Rough Path Theory (RPT), originated as a branch of stochastic analysis, provides a mathematical and principled approach for summarising complex data streams over time intervals in terms of their effects on the system; an approach that can be efficient, concise, robust to variable sampling frequency and missing data. Our group aims to channel the mathematical insights from RPT to advance and innovate methodologies of machine learning for analysis of streamed data, and validate the proposed methods in real-world applications, e.g. computer vision, health and finance.

RPML Research


Our interdisciplinary research is in the interplay between stochastic analysis, machine learning and numerous applications.

RPML Publications


A list of the preprints and publications of our group.

RPML codes


Supplementary code examples for our research papers showcase how to use our methods to tackle real-world data challenges.

RPML Events


Our group organizes seminars, workshops and conferences. The information on past and upcoming events can be found here.

Our rough path and machine learning group is supported by the EPSRC Program grant entitled Unparameterised multi-modal data, high order signatures, and the mathematics of data science (DataSig Team).