International Conference:
Differential Equations for Data Science 2025 (DEDS2025)

See the new main page: DEDS2025

  • Dates: February 11(Tue)–13(Thu), 2025


  • Place: Kyoto University, Japan

    • Lecture room 110, Department of Mathematics, Faculty of Science, Kyoto University   (Address: Kitashirakawa Oiwake-cho, Sakyo-ku, Kyoto 606-8502, Japan)


  • Links: DEDS2024,   DEDS2023,   DEDS2022,   DEDS2021


  • Aim:

    • This conference is mainly devoted to new mathematical aspects on machine learning algorithms, big data analysis, and other topics in data science area, from a viewpoint of differential equations. In recent years, several interesting connections between differential equations and data science have been found and attract attention from researchers of differential equations. In this conference, we will gather such researchers of differential equations who have interest in data science and try to shed new light on mathematical foundations on the topics in machine learning/data science.


  • Keywords:

    • ODE, PDE, Delay DE, Neural ODE, Machine learning, Deep learning, Data science, Big data, Reservoir computing (RC), Physical RC, Graph Laplacian, Universal approximation theory, Edge of chaos, Echo state property, Graphon, Dynamical System, Singular valued decomposition, Variational auto encoder



  • Supports:

    • MIRS, Kanazawa University Link
    JST, CREST, JPMJCR2014 Link


  • Organizers:

    • Hayato Chiba (Tohoku University, JP)
    Thomas de Jong (Kanazawa University, JP)
    Yoshikazu Giga (The University of Tokyo, JP)
    Lyudmila Grigoryeva (University of St. Gallen, CH)
    Boumediene Hamzi (California Institute of Technology, US)
    Masato Kimura (Kanazawa University, JP)
    Hiroshi Kokubu (Kyoto University, JP)
    Kohei Nakajima (The University of Tokyo, JP)
    Hirofumi Notsu (Kanazawa University, JP, Chair)
    Juan-Pablo Ortega (Nanyang Technological University, SG)
    Julius Fergy Rabago (Kanazawa University, JP)