Michele Linardi

Délégation Paris 5, Université Paris Cité

Papers

Total Citations

162

H-Index

About

Michele Linardi is a leading researcher in data series mining, with a primary focus on motif and discord discovery—fundamental primitives for analyzing time-series data across diverse fields such as robotics, entomology, seismology, medicine, and climatology. Her most impactful contribution is the development of the Matrix Profile X framework (2018, 77 citations), which advanced the state-of-the-art by enabling efficient, variable-length motif discovery without requiring users to pre-specify motif lengths. Building on this, she co-authored the Matrix Profile Goes MAD method (2020, 62 citations), which unified motif and discord detection into a single, scalable algorithm, significantly reducing computational overhead and expanding practical utility. Her work on VALMOD (2018, 21 citations) further refined variable-length motif discovery, addressing key limitations in existing tools. Collectively, Linardi’s research has made data series mining more accessible and powerful, directly enabling real-world applications in anomaly detection, pattern recognition, and scientific discovery. With over 160 total citations, her algorithms are widely adopted in both academic and industrial settings, cementing her reputation as a key innovator in scalable time-series analytics.