13.05.2026 - Distinguished Lecture Series: Mário Figueiredo (University of Lisbon)
We are pleased to announce our upcoming Distinguished Lecture Series talk by Mário Figueiredo (University of Lisbon)! The talk will take place online on Wednesday, May 13th, 9:45 - 11:15 CET. Please join via the following webex link: https://unistuttgart.webex.com/unistuttgart-en/j.php?MTID=mba528edda0e89a55469967ddde04552c
Mário Figueiredo is an IST Distinguished Professor and holder of the Feedzai Chair on Machine Learning at Instituto Superior Técnico (IST), University of Lisbon. He also serves as a group leader at Instituto de Telecomunicações and is the Director of the ELLIS Unit Lisbon. His research career spans a broad range of topics, including statistical machine learning, image processing, optimization, inverse problems, and increasingly, causal inference and discovery. His contributions have been recognized with the EURASIP Individual Technical Achievement Award, the IEEE W. R. G. Baker Award, the IAPR Pierre Devijver Award, the IEEE Signal Processing Society Best Paper Award, and with the grade of Fellow of the IEEE, IAPR, EURASIP, and ELLIS. He is a member of the Lisbon Academy of Sciences and the Portuguese Academy of Engineering.
Title: Causal Discovery from Observations
Causal Discovery from Observations
Modern machine learning excels at identifying correlations. However, to make real impact, we must understand causality, the “why” behind the data, and uncover the underlying causal mechanisms behind the observations. This is the core challenge in causal discovery. This pursuit of causal understanding is foundational for the next generation of AI. It is the key to building genuinely explainable AI (XAI) that can justify its decisions with causal claims rather than just complex correlations. Although identifying causal links traditionally requires experiments (interventions), this is often impossible, impractical, or unethical. The central challenge, therefore, is learning cause-and-effect from purely observational data. In this talk, after briefly surveying the field, I discuss recent advances in this area, focusing on the fundamental problem of distinguishing cause from effect (i.e., does X→Y or Y→X?) from bivariate data.
Date: Wednesday, May 13, 2026
Time: 9:45 - 11:15 CET
Place: Online; join via the following Webex-link: https://unistuttgart.webex.com/unistuttgart-en/j.php?MTID=mba528edda0e89a55469967ddde04552c
Looking forward to seeing you all there! No registration necessary.