Rchr
J-GLOBAL ID:201901012105162948   Update date: Mar. 25, 2024

Mototake Yoh-ichi

モトタケ ヨウイチ | Mototake Yoh-ichi
Affiliation and department:
Job title: Associate Professor
Research field  (2): Soft computing ,  Bio-, chemical, and soft-matter physics
Research keywords  (8): Data driven science ,  ベイズ推論 ,  位相的データ解析 ,  複雑系 ,  計算物理 ,  説明可能AI ,  Statistical machine learning ,  Complex system
Research theme for competitive and other funds  (6):
  • 2022 - 2027 Development of machine learning methods for discovering symmetries in pattern dynamics
  • 2022 - 2025 革新的セラミック材料設計のための材料パターン情報学の創成
  • 2021 - 2025 解釈可能AIによるパターンダイナミクスの数理構造抽出と材料情報学への応用
  • 2020 - 2022 Constructing a reduced model of a pattern formation process on the basis of topological data analysis
  • 2020 - 2021 代数幾何的学習理論の物理データ分析への応用手法の検討
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Papers (35):
  • Hitoshi IZUNO, Masahiko Demura, Masayoshi Yamazaki, Satoshi Minamoto, Junya Sakurai, Kenji Nagata, Yoh-ichi Mototake, Daisuke Abe, Keisuke Torigata. Search for high-creep-strength welding conditions considering HAZ shape factors for 2 1/4Cr-1Mo steel. Welding in the World. 2024
  • A. Okuno, Y. Morishita, Y. Mototake. Autoregressive with Slack Time Series Model for Forecasting a Partially-Observed Dynamical Time Series. IEEE Access. 2024
  • Tsutomu T. Takeuchi, Kai T. Kono, Suchetha Cooray, Atsushi J. Nishizawa, Koya Murakami, Hai-Xia Ma, Yoh-Ichi Mototake. Quantification of Galaxy Distribution with Topological Data Analysis and Detection of the Baryon Acoustic Oscillation. Proceedings of the Institute of Statistical Mathematics. 2023. 71. 2. 159-187
  • Yoh-ichi Mototake. Extracting Nonlinear Symmetries From Trained Neural Networks on Dynamics Data. NeurIPS 2023 Workshop: AI for Science from Theory to Practice. 2023
  • Shunya Tsuji, Ryo Murakami, Hayaru Shouno, Yoh-ichi Mototake. Revealing the Mechanism of Large-scale Gradient Systems Using a Neural Reduced Potential. NeurIPS 2023 Workshop: Machine Learning and the Physical Sciences. 2023
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MISC (6):
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Lectures and oral presentations  (6):
  • 物理学者と学習機械の効果的な協業に向けて:学習済み深層ニューラルネットワークからの解釈可能な物理法則抽出
    (ディープラーニングと物理学2020 オンライン 2020)
  • 第2回日米独先端科学(JAGFoS)シンポジウム
    (第2回日米独先端科学(JAGFoS)シンポジウム 2019)
  • The 4th Workshop on Self-Organization and Robustness of Evolving Many-Body Systems
    (The 4th Workshop on Self-Organization and Robustness of Evolving Many-Body Systems 2019)
  • 第2回教育・コミュニケーションロボット研究開発シンポジウム
    (第2回教育・コミュニケーションロボット研究開発シンポジウム 2018)
  • 神経回路学会時限研究会「ニューラルネットの温故知新」
    (神経回路学会時限研究会「ニューラルネットの温故知新」 2016)
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Education (4):
  • 2013 - 2016 The University of Tokyo Graduate School of Arts and Sciences Master Course of Department of General Systems Studies
  • 2010 - 2013 The University of Tokyo College of Arts and Sciences Master Course of Department of General Systems Studies
  • 2008 - 2010 Hokkaido University Faculty of Science Master Course of Department of Physics
  • 2004 - 2008 Tohoku University Faculty of Science Department of physics
Professional career (1):
  • Ph.D (The University of Tokyo)
Work history (3):
  • 2023/01 - 現在 Hitotsubashi University Graduate School of Social Data Science Associate professor
  • 2019/04 - 2022/12 The Institute of Statistical Mathematics
  • 2016/04 - 2019/03 The University of Tokyo
Awards (4):
  • 2019 - SWARM2019 The 3rd International Symposium on Swarm Behavior and Bio-Inspired Robotics, Best Paper Award Finalists
  • 2017 - 東京理科大学 脳学際研究部門第1回公開シンポジウム 最優秀発表賞
  • 2016 - 日本人工知能学会 2016 Annual Conference Award
  • 2015 - SWARM 2015 The First International Symposium on Swarm Behavior and Bio-Inspired Robotics, Best Student Paper Award Finalists
Association Membership(s) (4):
日本コンピュータ化学会 ,  放射光学会 ,  日本人工知能学会 ,  THE PHYSICAL SOCIETY OF JAPAN
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