"Methodological advancement in rough paths and data science" of ICIAM2023 Tokyo

  • Wu, Y. (Organiser)
  • Hao Ni (Organiser)

Activity: Participating in or organising an event typesOrganiser of special symposia


Minisymposium of ICIAM2023 Tokyo

Rough path theory is an emerging mathematical technology that captures macroscopically interactions of highly oscillatory streamed data. Formally, it extends the domain of definition for the calculus of deterministic controlled differential equations, allowing them to be driven by complex signals, potentially rougher than Brownian motion. This area has built bidirectional connections with data science and machine learning, enabling the development of novel, mathematics-informed methods for efficiently analyzing time series data, e.g. PDE-based Signature kernel, path development layer with Lie group representation. This minisymposia series facilitates the discussion of new methodological innovations on this interface between rough paths and data science.

Organizer(s) : Hao Ni, Yue Wu

Classification : 60L20, 60L70, 60L10, 62M45

Speakers Info :
Christian Bayer (The Weierstrass Institute for Applied Analysis and Stochastics)
Thomas Cass (Imperial College London)
Emilio Rossi Ferrucci (University of Oxford)
James Foster (University of Bath)
Satoshi Hayakawa (University of Oxford)
Chong Liu (ShanghaiTech University)
Qi Meng (Microsoft Research)
Hao Ni (University College London)
Harald Oberhauser (University of Oxford)
Josef Teichmann (ETH Zurich)
Danyu Yang (Chongqing University)
Period24 Aug 2023
Event typeOther
LocationTokyo, JapanShow on map