Date and time: Thursday 28 May 2026, 13:00-14:00 CEST
Speaker: Luis Gustavo Nonato, University of São Paulo, Brazil
Title: Data Science for High-Stakes Decisions – Crime Analytics, Environmental Illegality, and Explainability
Where: Digital Futures hub, Osquars Backe 5, floor 2 at KTH main campus OR Zoom
Directions: https://www.digitalfutures.kth.se/contact/how-to-get-here/
OR
Host: Vania Ceccato, vania.ceccato@abe.kth.se.
Bio: Luis Gustavo Nonato received his PhD in Applied Mathematics from the Pontifícia Universidade Católica do Rio de Janeiro, Brazil, in 1998. He is currently a Full Professor at the Institute of Mathematical and Computer Sciences of the University of São Paulo (ICMC–USP), São Carlos, Brazil.
Prof. Nonato has held international visiting positions at leading research centers, including the Center for Data Science at New York University (2016–2018) and the Scientific Computing and Imaging Institute at the University of Utah (2009–2010). He has played an active leadership role in the visualization and data science communities, serving on the program committees of major conferences such as IEEE Visualization and EuroVis, as Associate Editor of Computer Graphics Forum and IEEE Transactions on Visualization and Computer Graphics, and as Editor-in-Chief of the SBMAC SpringerBriefs in Applied Mathematics and Computational Sciences.
His research interests span data visualization, machine learning, and data science, with a strong emphasis on developing analytical methods for complex, real-world problems. Prof. Nonato is particularly committed to bridging academia, industry, and government, leading collaborative initiatives with the private sector and establishing partnerships with public agencies.
Nonato visits Stockholm in May-July 2026 as a Scholar-in-Residence at Digital Futures, hosted by Vania Ceccato.
Abstract: This presentation provides an overview of my research group’s efforts to develop mathematical and computational frameworks for analyzing high-stakes systems. Our research is organized around three interconnected pillars.
- Crime Analytics focuses on modeling spatio-temporal data to support the understanding of crime dynamics and enhance public safety.
- Timber Trade Network Analysis applies network science methods to characterize trade structures and to distinguish legal from illegal flows, contributing to environmental monitoring and governance.
- Explainable Artificial Intelligence (XAI) addresses the need for transparency, interpretability, and trust in machine learning models deployed in sensitive societal and environmental contexts.
The overarching goal of this presentation is to provide a panoramic view of these research directions to foster opportunities for future interdisciplinary collaborations.
