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.
Kazunori Akiyama, Heriot-Watt University, UK
Frontier of Black Hole Imaging: Computational Algorithms Driving the Scientific Breakthroughs
Stefan Catheline, INSERM, Lyon, France
Gravitational lense effect in a fabric membrane
Eric Chassande-Mottin, CNRS and APC, Paris, France
Lessons from a decade of gravitational-wave astronomy and advances in polarimetric sensing
Colin Fox, Otago University, New Zealand
Posterior inference in large-scale inverse problems by MCMC, and not MCMC
Ryoichi Horisaki, The University of Tokyo, Japan
Computational imaging with randomness
Kohei Yatabe, Natsuki Akaishi, Keidai Arai, Tokyo University of Agriculture and Technology, Japan
Phase retrieval algorithms for X-ray nanoimaging
Grégoire Doat, Université Grenoble-Alpes, Gipsa-lab, pôle GAIA, France
Geometric phases to infer polarization fluctuations of gravitational waves
Philippe Flores, CNRS - GIPSA-lab, France
Structured low-rank methods for gravitational wave ringdown data analysis
Ammar Mian, Université Savoie Mont-Blanc, France
Robust sparse convolution coding for radar applications
Kazumi Murata, Kanagawa Institute of Technology, Japan
Shota Takahashi, The University of Tokyo, Japan
Akio Taniguchi, Kitami Institute of Technology, Japan
Hiroki Yoneda, Kyoto University, Japan
Image Reconstruction Framework for MeV Gamma-ray Astronom: From INTEGRAL/SPI to COSI
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.
Here is the link to the registration form for both on-site and online participation. The deadline for on-site participation is 31 January 2026 (subject to venue capacity). The deadline for online participation is 28 February 2026.
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: deadline: registration for on-site participants (up to the venue capacity)
28 Feb 2026: deadline: registration for online participants
Shiro Ikeda, The Insitute of Statistical Mathematics
Nicolas Le Bihan, CNRS, GIPSA-Lab
JSPS KAKENHI (International Leading Research ) 23K20035
Research Center for Statistical Machine Learning, The Institute of Statistical Mathematics
CNRS MITI interdisciplinary research program
ANR, French National Research agency under the RICOCHET program