Dates: September 28–October 1, 2026

Venue: Seminar room A and D (3rd floor), Faculty of Engineering Building 6 (No. 79), Hongo campus, The University of Tokyo.

https://www.u-tokyo.ac.jp/content/400020145.pdf

Aims and Scope:  

Stochastic processes provide a fundamental framework for modeling complex random phenomena across science and engineering, while modern machine learning offers powerful tools for inference, prediction, and decision-making from data. In recent years, the interaction between stochastic processes and machine learning has grown increasingly close, driven by both methodological developments and emerging applications that benefit from probabilistic modeling and data-driven approaches. This workshop aims to foster interdisciplinary discussions on recent advances at the interface of stochastic processes, machine learning, and related fields, ranging from theoretical developments to methodological innovations and applications. The workshop, which originates at the joint events managed by Ulm University in Germany, the University of Tokyo, Tohoku University, and the Institute of Statistical Mathematics (ISM), welcomes attendance from any other institutes. 

Organizing Institutes:

• Institute of Statistical Mathematics Japan

• Tohoku University Japan

• Ulm University Germany

• The University of Tokyo Japan

The Organizers:

• Satoshi Kuriki (Institute of Statistical Mathematics)

• Alexander Lindner (Ulm University)

• Yasumasa Matsuda (Tohoku University)

• Teppei Ogihara (The University of Tokyo)

• Evgeny Spodarev (Ulm University)

Supports:

• Grant-in-Aid for Scientific Research(B) 25K03083 Teppei Ogihara (the University of Tokyo）

• Institute of Statistical Mathematics