16 - 20 March 2026
The Institute of Statistical Mathematics, Tokyo, Japan
The inference of target properties from observational data is a fundamental paradigm underlying all branches of science. When data are sparse, indirect, or contaminated by noise, the reconstruction of underlying physical or structural parameters inevitably leads to inverse problems. The rapid advancement of computational techniques in data science, including signal processing, optimization theory, statistics, and machine learning, has updated the methods for solving such problems. For instance, the imaging of black holes with the Event Horizon Telescope and the microscopy techniques such as cryo-electron microscopy (cryo-EM) and Ptychography would have been difficult to achieve without the recent progress in data science. This workshop focuses on inverse problems in sensing technologies and data-driven methodologies, aiming to explore and discuss the recent developments in these approaches and their applications.
Kohei Yatabe, Tokyo University of Agriculture and Technology
Kazunori Akiyama, MIT
Ryoichi Horisaki, The University of Tokyo
Colin Fox, Otago University
Eric Chassande-Mottin, CNRS and APC, Paris
Stefan Catheline, INSERM, Lyon
We accept applications for oral presentations. Please note that the presentation must be provided on-site once it is accepted. Send your title and abstract through this link. Due date is 15 Dec 2025.
19 Oct 2025: website open
15 Dec 2025: due date of application for a talk
31 Dec 2025: decision on acceptance
10 Jan 2026: registration open
31 Jan 2026: due date of registration for on-site participants (up to the capacity of the venue)
28 Feb 2026: due date of registration for online participants
Shiro Ikeda, The Insitute of Statistical Mathematics
Nicolas Le Bihan, CNRS, GIPSA-Lab
JSPS KAKENHI (International Leading Research ) 23K20035
ISM
CNRS MITI interdisciplinary research program
ANR, French National Research agency under the RICOCHET program