ICIAM 2023

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DataSıg will have a strong presence at two minisymposia at this year's ICIAM in Tokyo.



Sessions organised by members of the DataSıg team:

[00322] Methodological advancement in rough paths and data science

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.

Organisers: Hao Ni, Yue Wu


Christian Bayer WIAS
Thomas Cass Imperial College London
Ilya Chevyrev University of Edinburgh
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 Zürich
Danyu Yang Chongqing University

[00323] Integrating rough paths into domain applications

Streamed data are ubiquitous. In this context, a key challenge is to quantify our understanding and account for the interaction between channels. Rough path theory provides new insights for producing actionable inference for multimodal path-like data. The path signature is a mathematical object with desirable approximation properties and geometric interpretation which leads to more effective features and analysis. Further, the expected signature provides a powerful way to describe empirical measures on streams. Applications include award-winning machine learning methods in healthcare and finance, as well as commercial-quality Chinese handwriting software. We expose new challenges and work on applications in this area.

Organisers: Terry Lyons, Lingyi Yang


Paola Arrubarrena Imperial College London
Bruno Dupire Bloomberg
Elena Gal University of Oxford
Blanka Horvath University of Oxford
Mohamed Ibrahim University of Leeds
Florian Krach ETH Zürich
Darrick Lee University of Oxford
Maud Lemercier University of Oxford
Hang Lou University College London
Jason Rader University of Oxford
Benjamin Walker University of Oxford
Lingyi Yang The Alan Turing Institute
Other sessions with speakers from the DataSıg team:

[00059] Numerical solutions for differential equations: Probabilistic approaches and statistical perspectives

Many applications involve predicting the dynamics of a system by solving differential equations. Due to the increased demand for predictive power of these models, numerically solving a differential equation is now often combined with parameter estimation or uncertainty quantification. This paradigm shift drives the need for probabilistic approaches that are compatible with statistical inference, or that improve the robustness of inference to possibly inaccurate mathematical models. The talks in this minisymposium will present recent work that addresses these challenges for deterministic ODEs and PDEs, by using ideas from numerical analysis, probability theory, and Bayesian statistical inference.

Speakers include:

Yue Wu University of Strathclyde

[00201] Data-Driven Methods for Rough PDEs

Recently there has been an increased interest in applying data driven methods to learn partial differential equations (PDEs). For example, operator learning has been developed to learn maps between infinite-dimensional function spaces and has shown success in the context of smooth PDEs. However, these methods perform poorly in areas where PDEs are less well-behaved; for instance, when equations are parameterized by non-smooth functions or when the PDE involves stochasticity. This mini-symposium invites experts on novel methods for learning stochastic and ill-conditioned multiscale PDEs. Topics will include numerical methods for SPDEs, learning in multiscale settings, and advances in operator learning.

Speakers include:

Cristopher Salvi Imperial College London