Dear all,
The
MODE Collaboration is organizing its
Sixth Workshop on Differentiable Programming for Experiment Design, to be held in
Kolumbari (Crete) from 1 to 7 September 2026: registration and abstract submission is now open at
https://indico.cern.ch/event/1655754/ .
You are all invited to come and present your MODE-related work!!! We would appreciate it if you could advertise this to potentially interested colleagues!
This is the sixth installment of a series the MODE Collaboration has started, with the aim of creating a community of scientists interested in, and possibly working on,
the design of experiments assisted by machine learning techniques to solve optimization problems in a highly dimensional parameter space, while making the optimization process
aware of the high-level physics goals and of budgetary constraints of several kinds. Automatic differentiation is a computer science technique that powers up all modern applications of gradient-based optimization problems: in the context of
MODE, this allows the construction of a fully differentiable pipeline, to achieve the simultaneous optimization of all design parameters through the use of deep learning and reinforcement learning techniques.
The first installment of the
workshop series has led to a white paper signed by
MODE authors together with an extended community of physicists and computer scientists (
https://doi.org/10.1016/j.revip.2023.100085), and has raised the interest of several
scientific Collaborations that are facing complex design optimization problems.
You will be hosted in the conference venue, promoting the spirit of a scientific retreat where serendipitous conversations may lead to collaborations and scientific breakthroughs.
If you would consider attending in person but have limited travel funds, we might be able to contribute to the expense. All information to request support (we will prioritize young participants) is available on the registration page and website.