Machine Learning for Sensing in Distributed Wireless Systems

by Simon Höckerbo | Dec 9, 2025

PI: Oleg Sysoev, Linköping University co-PI: Ove Edfors, Lund University Wireless sensing in distributed multiple-input multiple-output (D-MIMO) systems is an emerging research direction which opens new opportunities for sensing tasks such as localization or intrusion...

Spatio-Temporal Machine Learning for Inferring Gas Dynamics in Urban Environments

by Simon Höckerbo | Dec 9, 2025

PI: Fredrik Lindsten, Linköping University co-PI: Natascha Kljun, Lund University Gas advection and dispersion dynamics shape air quality, greenhouse gas fluxes, and emergency response capacity. Yet, current modeling approaches are incapable of inference from diverse...

Safety-Critical Intelligent Machines with Cloud-Assisted AI and Control

by Simon Höckerbo | Dec 9, 2025

PI: Maria Kihl, Lund University co-PI: Marian Codreanu, Linköping University This project will focus on safety-critical intelligent machines, e.g. collaborative robots, rescue drones, or vehicles with advanced driver-assistance systems, with cloud-assisted AI and...

AI Frameworks for Hardware Security Verification Case Study: Post-Quantum Cryptography (PQC)

by Simon Höckerbo | Dec 9, 2025

PI: Thomas Johansson, Lund University Co-PI: Cristofer Englund, Halmstad University Collaborators: Assoc. Prof. Qian Guo, Lund University, Assoc. Prof. Mahdi Fazeli, Halmstad University Current hardware verification methods are inadequate in identifying important...

Federated Learning for Heterogeneous and Inconsistent Data

by Simon Höckerbo | Dec 9, 2025

PI: Anders Eklund, Linköping University co-PI: Mikael Nilsson, Lund University Federated learning (FL) promises to revolutionize AI by enabling collaborative training without sharing sensitive data (see figure). However, current FL methods fail catastrophically with...