Themis Palpanas

Délégation Paris 5, Université Paris Cité, University of California, Riverside

Papers

Total Citations

162

H-Index

About

Themis Palpanas is a leading researcher in data series mining, with a focus on developing efficient algorithms for motif and discord discovery. His major contributions center on the **Matrix Profile** framework, a powerful primitive that has revolutionized how we analyze time-series data. Palpanas advanced this field with the **Matrix Profile X** (2018, 77 citations), which addressed critical scalability and usability challenges, enabling applications across robotics, entomology, seismology, medicine, and climatology. He further extended this work with **VALMOD** (2018, 21 citations) and **Matrix Profile Goes MAD** (2020, 62 citations), introducing variable-length motif and discord discovery—a breakthrough that eliminates the need for users to pre-specify motif lengths, making the tools more adaptive and practical. His research has had a profound impact, with his most-cited papers collectively amassing over 160 citations, underscoring their influence on both academic research and real-world applications. Palpanas’s work is notable for its emphasis on making data series mining accessible and automated, empowering scientists in diverse fields to uncover hidden patterns and anomalies in complex temporal data.