Rate My Professor Tony Lindgren

Lindgren’s research specializes in machine learning, constraint programming, and logic programming, with applications in rule-based classification, predictive maintenance, vehicle fault diagnosis, and optimization for heavy trucks. He contributes to projects including RAPIDS – Reliable Adaptive Predictive maintenance and Intelligent Decision Support, REMAP – Reliable Multimodal Adaptive Predictive Maintenance, Extreme Food Risk Analytics (EFRA), and Improvement of Machine Learning Interpretability Algorithms. Key publications feature "CICLe: Conformal In-Context Learning for Largescale Multi-Class Food Risk Classification" (ACL Findings, 2024), "Automotive fault nowcasting with machine learning and natural language processing" (Machine Learning, 2024), "SCANIA Component X dataset: a real-world multivariate time series dataset for predictive maintenance" (Scientific Data, 2025), and "Z-Time: efficient and effective interpretable multivariate time series classification" (Data Mining and Knowledge Discovery, 2024). Awards include Best Industry Paper at IEEE ICPHM (2022), Best Presentation at ICAIR (2016), and Best Student Paper at ECML (2004). He serves on program committees for ECML PKDD, IJCAI, and others, and acts as opponent and examiner for doctoral theses.