Workshop on Scientific Methods for Understanding Deep Learning

2nd Edition - International Conference on Learning Representations (ICLR) 2026

While deep learning continues to achieve impressive results on an ever-growing range of tasks, our understanding of the principles underlying these successes remains largely limited. This problem is usually tackled from a mathematical point of view, aiming to prove rigorous theorems about optimization or generalization errors of standard algorithms, but so far they have been limited to overly-simplified settings. The main goal of this workshop is to promote a complementary approach that is centered on the use of the scientific method, which forms hypotheses and designs controlled experiments to test them. More specifically, it focuses on empirical analyses of deep networks that can validate or falsify existing theories and assumptions, or answer questions about the success or failure of these models. This approach has been largely underexplored, but has great potential to further our understanding of deep learning and to lead to significant progress in both theory and practice. The secondary goal of this workshop is to build a community of researchers, currently scattered in several subfields, around the common goal of understanding deep learning through a scientific lens.

Submission Deadline: Feb 4 ‘26 (AOE) on OpenReview.
The workshop will be held on April 26 in Rio de Janeiro, Brazil.
For latest news about the workshop, follow @scifordl on X/Twitter.


Keynote Speakers

David Bau

Northeastern University

Jeremy Cohen

Flatiron Institute

Julia Kempe

NYU & Meta

Richard Baraniuk

Rice University & OpenStax

Matthieu Wyart

Johns Hopkins University & EPFL


Panelists

David Bau

Northeastern University

Jeremy Cohen

Flatiron Institute

Julia Kempe

NYU & Meta

Richard Baraniuk

Rice University & OpenStax

Matthieu Wyart

Johns Hopkins University & EPFL


Organizers

Zahra Kadkhodaie

Flatiron Institute & New York University

Florentin Guth

Flatiron Institute & New York University

Sanae Lotfi

New York University

Davis Brown

UPenn & Pacific Northwest National Lab

Antonio Sclocchi

University College London

Sharvaree Vadgama

University of Amsterdam

Jamie Simon

Imbue & UC Berkeley

Eero Simoncelli

NYU & Flatiron Institute


Questions?

Contact us at scienceofdl.workshop@gmail.com or @scifordl.

Sponsors

We thank the Simons Foundation for their generous financial support.
Simons Foundation Logo