Rate My Professor Mathias Verbeke

Verbeke's research centers on the industrial application of artificial intelligence, with a focus on automated, adaptive, and embedded machine learning techniques to address challenges like data quality issues, contextual sensitivity, and multivariate multimodal data in dynamic, resource-constrained industrial environments. He supervises and co-supervises numerous research projects, including Real-Time Anomaly Detection in the Electromagnetic Environment Using AI, Optimizing EMI Monitoring through Compressed Sensing, CausAICA for Causal Actionable Understanding via AI for Root Cause Analysis, and PerIMPro for closing the product-production loop in injection-molded parts. His key publications include 'Inducing probabilistic relational rules from probabilistic examples' (2015, 115 citations), 'HMM with non-emitting states for Map Matching' (2018, 66 citations), 'A statistical relational learning approach to identifying evidence based medicine categories' (2012, 56 citations), 'Data-driven models with physical interpretability for real-time cavity profile prediction in electrochemical machining processes' (2025, 40 citations), and 'Process mining on machine event logs for profiling abnormal behaviour and root cause analysis' (2020, 25 citations). With 546 citations on Google Scholar, his work impacts fields such as anomaly detection, predictive maintenance, and manufacturing process optimization. Verbeke has 99 publications listed in LIRIAS from 2006 to 2026 and serves on committees such as EUSIPCO 2026.