Organization

Management Group X00

Hear investigatorKoji HashimotoKyoto University, Graduate School of Science
A01-PIAkio TomiyaInternational Professional University of Technology in Osaka, Faculty of Technology
A02-PIMihoko NojiriHigh Energy Accelerator Research Organization, Institute of Particle and Nuclear Studies
A03-PITomi OhtsukiSophia University, Faculty of Science and Technology
B01-PIAkinori TanakaRIKEN, Center for Advanced Intelligence Project
B02-PIYoshiyuki KabashimaThe University of Tokyo, Graduate School of Science
B03-PIKenji FukushimaThe University of Tokyo, Graduate School of Science
A03Masatoshi ImadaWaseda University, Waseda Research Institute for Science and Engineering
A03Yuki NagaiJapan Atomic Energy Agency, Center for Computational Science & e-Systems
Co-InvestigatorYuji HironoOsaka University, Graduate School of Science
Interface InvestigatorRyui KanekoWaseda University, Faculty of Science and Engineering
Interface InvestigatorAkiyoshi SannaiKyoto University, Graduate School of Science
Interface InvestigatorNorihiro TanahashiKyoto University, Graduate School of Science

Goal A

Project A01
Unification of Computational Physics and Machine Learning

Principal InvestigatorAkio TomiyaInternational Professional University of Technology in Osaka, Faculty of Technology
Co-InvestigatorKouji KashiwaFukuoka Institute of Technology, Faculty of Information Engineering
Co-InvestigatorHiroshi OhnoUniversity of Tsukuba, Center for Computational Sciences
Co-InvestigatorTetsuya SakuraiUniversity of Tsukuba, Information and Systems

Project A02
Innovative advances in particle physics through machine learning

Principal InvestigatorMihoko NojiriHigh Energy Accelerator Research Organization, Institute of Particle and Nuclear Studies
Co-InvestigatorMasako IwasakiOsaka Metropolitan University, Graduate School of Science
Co-InvestigatorNoriko TakemuraKyushu Institute of Technology, Faculty of Computer Science and Systems Engineering
Co-InvestigatorJunichi TanakaThe University of Tokyo, The International Center for Elementary Particle Physics
Co-InvestigatorHajime NagaharaOsaka University, Institute for Datability Science

Project A03
Frontiers of Condensed Matter Physics Pioneered by Neural Network

Principal InvestigatorTomi OhtsukiSophia University, Faculty of Science and Technology
Co-InvestigatorMasatoshi ImadaWaseda University, Waseda Research Institute for Science and Engineering
Co-InvestigatorJunichiro OheToho University, Faculty of Science Department of Physics
Co-InvestigatorEiji SaitohThe University of Tokyo, Graduate School of Engineering
Co-InvestigatorShunsuke DaimonThe University of Tokyo, Graduate School of Engineering
Co-InvestigatorYuki NagaiJapan Atomic Energy Agency, Center for Computational Science & e-Systems

Project A04
Quantum/Gravity and Machine Learning

Principal InvestigatorKoji HashimotoKyoto University, Graduate School of Science
Co-InvestigatorKentaroh YoshidaSaitama University, Graduate School of Science
Co-InvestigatorSotaro SugishitaKyoto University, Graduate School of Science
Co-InvestigatorMasaki MurataSaitama Institute of Technology, The Department of Engineering Department of Information System

Goal B

Project B01
Mathematics and application of deep learning

Principal InvestigatorAkinori TanakaRIKEN, Center for Advanced Intelligence Project
Co-InvestigatorRyo KarakidaNational Institute of Advanced Industrial Science and Technology, Information Technology and Human Factors
Co-InvestigatorMasato TakiRikkyo University, Graduate School of Artificial Intelligence and Science

Project B02
Statistical mechanics approach to high-dimensional machine learning

Principal InvestigatorYoshiyuki KabashimaThe University of Tokyo, Graduate School of Science
Co-InvestigatorHajime YoshinoOsaka University, Cybermedia Center
Co-InvestigatorTomoyuki ObuchiKyoto University, Graduate School of Informatics
Co-InvestigatorAyaka SakataThe Institute of Statistical Mathematics, Department of Statistical Inference & Mathematics

Project B03
Approach from topological geometry toward machine learning

Principal InvestigatorKenji FukushimaThe University of Tokyo, Graduate School of Science
Co-InvestigatorShotaro ShibaOkinawa Institute of Science and Technology, Physics and Biology Unit
Co-InvestigatorKen ShiozakiKyoto University, Yukawa Institute for Theoretical Physics
Co-InvestigatorTatsuhiro MisumiKindai University, Faculty of Science and Engineering

Publicly Offered Reserch

Publicly Offered Research E01
Research using machine learning in quantum, elementary particle, spacetime, gravity, and related sciences

Tomohiro OtsukaTohoku University, Advanced Institute for Materials Research機械学習による半導体量子ハードウェア最適化と応用
Naotaka SuzukiThe University of Tokyo, Kavli Institute for the Physics and Mathematics of the UniverseML algorithm development for identifying supernovae from JWST x HST data
Takahiro YamamotoNagoya University, Graduate School of ScienceRingdown gravitational wave data analysis for testing gravity theories by deep leanring
Kikawa TatsuyaKyoto University, Graduate School of ScienceDevelopment of unfolding method using machine learning to unravel mystery of the matter-dominant universe
Masafumi FukumaKyoto University, Graduate School of Science世界体積ハイブリッドモンテカルロ法における機械学習を用いた配位生成の研究
Masakiyo KitazawaKyoto University, Yukawa Institute for Theoretical PhysicsApplying machine learning to event selections in relativistic heavy-ion collisions
Junpei MaedaKobe University, Graduate School of ScienceEstablishment of Deep Learning triggers on heterogeneous computers for particle physics experiments
Hajime OtsukaKyushu University, Graduate School of Science機械学習を用いた弦の有効理論の構築
Taikan SueharaKyushu University, Graduate School of ScienceParticle reconstruction with deep learning and its application to detector development
Tomo TakahashiSaga University, Faculty of Science and EngineeringProbing primordial fluctuations with machine learning
Hirotaka TakahashiTokyo City UniversityClassification of Glitch Noise and Burst Gravitational Wave Search with Artificial Intelligence and Adaptive Time-Frequency Analysis
Ryo KitamuraJapan Atomic Energy Agency機械学習を利用した大強度ビーム位相空間パラメータ再構成手法の開発
Tomoya NaitoRIKEN Interdisciplinary Theoretical and Mathematical Sciences Program Multi-body wave functions using unsupervised deep neural networks

Publicly Offered Research E02
Research using machine learning in condensed matter physics and related material sciences

Takashi YoshidomeTohoku University, Graduate School of Engineeringタンパク質水和の「深層学習モデル」の展開:水和熱力学量の高速計算
Hideyuki MizunoThe University of Tokyo, Graduate School of Arts and Sciences深層学習で切り拓くガラス物理:局在振動の形成機構解明
takeshi KawasakiNagoya University, Graduate School of ScienceConstruction of structure extraction techniques using interpretable machine learning in glassy systems
Molina JohnKyoto University, Graduate School of EngineeringPhysics Informed Machine Learning for Complex Flows
Yusuke NomuraKeio University, Faculty of Science and TechnologyFrontier of Machine Learning Methods for Quantum Many-body Systems
Masahito MochizukiWaseda University, Faculty of Science and Engineeringスピン模型のトポロジカル相転移を検出する汎用的な機械学習手法の開発
Youhei YamajiNational Institute for Material ScienceOptimization of many-body basis for excitation spectrum simulations of quantum materials
Shiro SakaiRIKEN Center for Emergent Matter Science隠れ層のフェルミオン自由度を用いた強相関電子系のスペクトル計算法の開発
Isao WatanabeRIKEN Nishina Center for Accelerator-Based Science機械学習で変革する次世代のmuSRデータ解析
Yoshihiro MichishitaRIKEN Center for Emergent Matter ScienceMachine-Learning-assisted Construction of Appropriate Frame
Ryosuke AkashiNational Institutes for Quantum Science and Technologyニューラルネットワークを用いた密度汎関数理論の展開
Tomohiko KonnoNational Institute of Information and Communications TechnologyDeep learning for discovery of new superconductors

Publicly Offered Research E03
Research to develop machine learning methods to solve various problems in physics

Hideyuki MiyaharaHokkaido University, Graduate School of Information Science and Technology化学反応系の情報幾何的解析と機械学習への応用
Kazue KudoOchanomizu UniversityUnraveling factors that determine the difficulty of learning in quantum machine learning
Keiichi KitamuraYokohama National University, Faculty of Engineering特徴面検出・解像度向上を自動で相互に行うAI積極融合型の新しい数値流体力学
Naoto NakanoMeiji University, Graduate School of Advanced Mathematical SciencesA statistical physics and dynamical system approach to input-output integrated random networks
Masanobu HorieRICOS Co. Ltd.Machine Learning for Various Partial Differential Equations

International advisory committee

Tetsuo HatsudaRIKEN iTHEMS director
Matthias TroyerMicrosoft, Distinguished Scientist
Jesse ThalerIAIFI Director
Masashi SugiyamaRIKEN AIP director

Senior Scientific Research Specialist

Yoshinobu Kawahara (2022.08~)Osaka University, Graduate School of Information Science and Technology
Toshihiro Fujii (2023.08〜)Osaka Metropolitan University, Graduate School of Science/Nambu Yoichiro Institute of Theoretical and Experimental Physics
Akihiro Minamino (2022.08~2023.07)Yokohama National University, Faculty of engineering

Management office

RepresentativeKoji HashimotoKyoto University, Graduate School of Science
Research Outcome MissionTomi OhtsukiSophia University, Faculty of Science and Technology
HP/ML MissionYuki NagaiJapan Atomic Energy Agency, Center for Computational Science & e-Systems
Management officerTBA
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