Yasuo Tabei
Yasuo Tabei studies efficient processing of large datasets. His team applies succinct data structures to machine learning and knowledge discovery. Tabei coauthored a KDD 2016 paper with Saigo, Yamanishi and Puglisi on scalable partial least-squares regression over grammar-compressed data matrices. With Yamanishi and Pagh, Tabei co-authored an ICDM 2019 paper on space-efficient feature maps for string-alignment kernels. He also coauthored work on scalable similarity search. With Nishimoto and Kanda, he co-authored an ICALP 2022 paper on optimal-time run-length Burrows-Wheeler-transform construction in bounded space.
Timeline
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2016
Learning on compressed matrices
Tabei coauthored a KDD 2016 paper with Saigo, Yamanishi and Puglisi on scalable partial least-squares regression over grammar-compressed data matrices.
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2019
Efficient feature maps
With Yamanishi and Pagh, Tabei co-authored an ICDM 2019 paper on space-efficient feature maps for string-alignment kernels. He also coauthored work on scalable similarity search.
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2022
Compressed-index construction
With Nishimoto and Kanda, he co-authored an ICALP 2022 paper on optimal-time run-length Burrows-Wheeler-transform construction in bounded space.
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As of October 2026
Succinct information processing
His team develops data structures that permit operations while data stays compressed and applies them to large-scale AI and knowledge discovery.
Sources and credits
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