Creative design and manufacturing
Transform everything into data science
Lecture: ICMMA 2018
Lecture: Nano Tech 2019
Lecture: The 12th SPSJ International Polymer Conference (IPC2018)
Lecture: The 39th Japan Symposium on Thermophysical Properties
Lecture: Panasonic 100th Anniversary CROSS-VALUE INNOVATION FORUM 2018
Panel Discussion: High Performace Computing Infrastructure(HPCI) 第6回材料系ワークショップ
Lecture: High Performace Computing Infrastructure(HPCI) 第6回材料系ワークショップ
Lecture: 10th International Workshop on Combinatorial Materials Science and Technology (COMBI2018)
Keynote Lecture: 日本金属学会 2018 年秋期（第163回）講演大会
Keynote Lecture: The 10th MEI3 Center International Symposium
Workshop:The Frontiers of Applied Bayesian Inference and Computation
Lecture: Technical Information Institute Co.Ltd Seminar「マテリアルズ・インフォマティクスを活用した 材料設計の動向、応用事例」
Lecture: H30年度 ポスト「京」重点課題（７） 第3回CDMSI研究会
Panel discussion: 統計数理研究所創立75周年記念 産学連携シンポジウム
Lecture: Materials informatics
Publication of a discussion paper in Annals of the Institute of Statistical Mathematics.
English website is now available
2Creative Design and Manufacturing Process based on Data Science
We recognize the importance of being at the absolute leading edge position in the manufacturing industry. This cannot be done by data science alone. Most of the classical data science analysis tools are designed for interpolating predictions. Data science used to be a science of predictions based on pattern recognition from existing data.
3The mission of this center
We have accumulated state-of-the-art data science knowledge here, for instance, machine learning, optimization theory, data assimilation, Bayesian inference, materials informatics, etc. We are devoted to foster innovative methods for design and manufacturing based on co-created values through industry-academia collaboration.
4Smart Manufacturing in the Perspective of Data Science
Manufacturing process is laborious, time-consuming and rather expensive, in general, which circulates intellectually demanding product design, computational modeling and simulation, and physical experiments. We aim to achieve innovative leaps in various fields of manufacturing approaches with state-of-the-art technologies in data science.