Japanese
| 機械学習・AI技術 ・高性能行列計算技術を基盤とした新しい機械学習・AI技術の開発と応用 ・医療、農業、インフラ、ものづくり分野など各種応用分野と連携した機械学習・AI技術の開発と応用 |
| 分散データ非共有統合解析技術 ・複数機関が分散保持するデータの 生データを共有せずに統合解析するデータコラボレーション解析技術の開発 ・企業や自治体、病院等の実データを用いたデータコラボレーション技術の応用 |
| 高並列数値計算アルゴリズム技術 ・スーパーコンピュータ向けの超並列行列計算（固有値分解、線形方程式など）アルゴリズムおよび高性能実装法の開発 ・  開発する超並列アルゴリズムの振動解析、電子状態計算等の実アプリケーションへの応用 |
English
| Machine Learning and AI→Details |
| Integrated Data Analysis on Multiple Institutions In recent years, as data collection and accumulation have become easier, various companies and institutions have been accumulating their own data and working on analysis using artificial intelligence (AI). To improve the performance of AI analysis, it is necessary to collect a sufficient amount of data. Therefore, it is expected that highly accurate analysis will be possible if data held by multiple institutions can be integrated for analysis. However, it is difficult to share the original data containing personal information and trade secrets across institutions. Therefore, there is a need for technology that enables integrated analysis across corporate and institutional boundaries without sharing the original data. |
| Numerical Computation Techniques in Highly Parallel Computer simulations are required in broad areas in industry and science from nano-level to cosmic scales, such as the development of new materials, functional analysis of protein and DNA, efficient design and development of products for vehicles, accurate weather prediction, supernova explosion, and so on. This computational group studies high-performance algorithms and develops computer programs, collaborating with researchers such as in life science.
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