Current

Workshop

AI Structured Learning 2025 Workshop

Welcome to the 2025 workshop hosted by the CHAIR theme Structured learning, which is held for the third year.

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Overview

In this workshop we broadly cover topics related to Structured Learning targeting specifically the following questions:

  1. How can we encode structure (e.g., scientific domain knowledge) into learning systems?
  2. How does domain knowledge affect uncertainty quantification and out-of-distribution predictions?
  3. Can these insights enable us to solve problems in a data and computationally-efficient manner? We will have a particular focus on inverse and surrogate modeling.
  4. How can these strategies help scientific discovery?

The workshop consists of four thematic areas covered across 2 days:

  1. Data efficiency and generalization
  2. Uncertainty quantification
  3. Inverse problems
  4. Applications

 

Event page with registration and full information

 

Registration is opening soon. The workshop is free to attend for academic researchers. Participants from industry will be subject to a registration fee.

The workshop is organized by the CHAIR Theme on Structured Learning: Rocío Mercado (CSE), Moritz Schauer (MATH), Axel Ringh (MATH), Stefano Sarao Manelli, and Simon Olsson (CSE).

 

Structured learning

This theme focuses on how to make use of structure in data to build machine learning (ML) and artificial intelligence (AI) systems which are safer, more trustworthy and generalize better. Structure includes the relationship between data, in time and space, and how the predictions change when data is transformed in specific ways, for example rotated or scaled. These topics are abstract and general but have a direct impact on the use of AI and ML in the sciences and in applications such as drugs and materials design, or medical imaging.