Mathieu Guillame-Bert

Carnegie Mellon University, Institut national de recherche en sciences et technologies du numérique

Papers

Total Citations

H-Index

About

Mathieu Guillame-Bert is a researcher whose work bridges machine learning, temporal reasoning, and industrial automation. His most impactful contribution, "Data-Driven Classification of Screwdriving Operations" (2017, 18 citations), demonstrates a practical application of data-driven methods to manufacturing—specifically, classifying fine-grained assembly tasks using sensor data. This work highlights his ability to translate complex pattern recognition into real-world process monitoring and quality control. Earlier, Guillame-Bert introduced the Temporal Interval Tree Associative Rules (Tita rules) model in "Planning with Inaccurate Temporal Rules" (2012, 2 citations). This innovative framework addresses a fundamental challenge in AI planning: handling uncertainty, temporal inaccuracy, and incomplete temporal orders. Tita rules support operators for synchronicity, chaining, and disjunctive timing, offering a flexible tool for domains where precise temporal knowledge is unavailable. While his citation counts reflect a focused, niche impact, his contributions are notable for their originality—combining rigorous temporal logic with applied data science. Guillame-Bert’s work is especially relevant for researchers interested in industrial AI, temporal pattern mining, and the intersection of symbolic reasoning with data-driven approaches.