Vietnam Spring School

vietnam spring school

 

The purpose of this research school is to showcase recent developments in Rough Path Theory (RPT) in machine learning (ML) for understanding complex multimodal data streams. This 5-day spring school is composed of 9 mini courses delivered by leading experts in this field, to cover topics from methodological innovation to real-world applications. Two invited keynote talks, along with a panel discussion, will further broaden the scope of the mini courses and stimulate the discussion on general mathematical approaches in ML and data science.

Scientific committee

Juergen Jost, Max Planck Institute for Mathematics in the Sciences, Germany

Terry Lyons, University of Oxford, United Kingdom

Organising committee

Duc Luu, MIS-Germany & Institute of Mathematics, VAST, Vietnam

Hao Ni, University College London, United Kingdom

Viet Hung Pham, Institute of Mathematics, VAST, Vietnam

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Speakers

Terry Lyons The connection between rough paths and data science
Juergen Jost Mathematical approaches for the analysis of data
Harald Oberhauser Describing laws of stochastic processes with expected signature moments and cumulants
Maud Lemercier Signature Kernel Methods
Cris Salvi Infinite width-depth regimes of Recurrent ResNet
Emilio Rossi Ferrucci Foundation of rough path theory
Yue Wu An introduction to the log-ODE method
Hao Ni Generative models for time series generation: a rough path approach
Weixin Yang Developing the path signature methodology and its applications to some real-world machine learning challenges
Duc Luu Tracking attractors via discrete rough paths
Nam Vo Machine Learning Methods for the Understanding of the Human Genome