Portrait of Keiya Hirashima

Newsお知らせ

Sep 2026
Our paper Architecture-Agnostic Exascale Star-by-Star Simulation of Our Galaxy was selected as an ACM Gordon Bell Prize Finalist at SC26.NEW
Aug 25th, 2026
I received the HIRESO Inc. Award at the 13th IRMAIL Science Grant.NEW
Jul 8th, 2026
My project was selected for the HPCI Excellent Achievement Award at the 13th Project Report Meeting.NEW
Jun 3rd, 2026
Our ASURA-FDPS-ML team received the HPCI Software Award (Development Division Excellence Award).
Apr 21st, 2026
I received the FY2025 RIKEN Research and Technology Incentive Award (RIKEN OHBU Award).
Jan 15th, 2026
Press release: A 300-Billion-Particle Milky Way Simulation Achieved with AI x Fugaku.

    About

    I combine computational astrophysics, machine learning, and high-performance computing to study galaxy formation and evolution at star-by-star resolution. My work develops scalable simulation codes and AI surrogate models that replace computationally expensive, short-timescale physical calculations.

    My current project, PHEASANT, performs architecture-agnostic exascale simulations of a Milky Way-mass galaxy while resolving individual stars across multiple physical scales.

    Degrees

    Ph.D., Astronomy (sub-major: Information Science), Science, The University of Tokyo, Japan, 2025

    M.S., Astronomy, Science, The University of Tokyo, Japan, 2022

    B.E., Informatics and Mathematical Science, Engineering, Kyoto University, Japan, 2020

    Publications

    [ First-Authored, Refereed ]
  1. Hirashima, K., Harada, N., Saitoh, T. R., Nomura, K., Iwasawa, M., Bollweg, D., Yoshikawa, K., Miki, Y., Asano, T., Hirai, Y., Okamoto, T., Makino, J., Bettencourt, M., Morgenstern, L., Bode, M., & Fujii, M. S. (2026). Architecture-Agnostic Exascale Star-by-Star Simulation of Our Galaxy, ACM Gordon Bell Prize Finalist, SC26, [SC26] [Project]
  2. Hirashima, K., Fujii, M. S., Saitoh, T. R., Harada, N., Nomura, K., Yoshikawa, K., Hirai, Y., Asano, T., Moriwaki, K., Iwasawa, M., Okamoto, T., & Makino, J. (2025). The First Star-by-star N-body/Hydrodynamics Simulation of Our Galaxy Coupling with a Surrogate Model, Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC '25), 1859-1873, [DOI] [arXiv]
  3. Hirashima, K., Nozaki, S., & Harada, N. (2025). Self-supervised Synthetic Pretraining for Inference of Stellar Mass Embedded in Dense Gas, NeurIPS 2025 Machine Learning and the Physical Sciences Workshop, [OpenReview] [arXiv]
  4. Hirashima, K., Moriwaki, K., Fujii, M. S., Hirai, Y., Saitoh, T. R., Makino, J., Steinwandel, U. P., & Ho, S. (2025). ASURA-FDPS-ML: Star-by-star Galaxy Simulations Accelerated by Surrogate Modeling for Supernova Feedback, The Astrophysical Journal, 987(1), 86, [DOI] [ADS]
  5. Hirashima, K., Moriwaki, K., Fujii, M. S., Hirai, Y., Saitoh, T. R., Makino, J., Steinwandel, U. P., & Ho, S. (2024). First High-Resolution Galaxy Simulations Accelerated by a 3D Surrogate Model for Supernovae, NeurIPS 2024 Machine Learning and the Physical Sciences Workshop, [PDF]
  6. Hirashima, K., Moriwaki, K., Fujii, M. S., Hirai, Y., Saitoh, T. R., Makino, J., & Ho, S. (2023). Surrogate Modeling for Computationally Expensive Simulations of Supernovae in High-Resolution Galaxy Simulations, NeurIPS 2023 AI for Science Workshop, [OpenReview]
  7. Hirashima, K., Moriwaki, K., Fujii, M. S., Hirai, Y., Saitoh, T. R., & Makino, J. (2023). 3D-Spatiotemporal Forecasting the Expansion of Supernova Shells Using Deep Learning toward High-Resolution Galaxy Simulations, Monthly Notices of the Royal Astronomical Society, 526(3), 4054-4066, [DOI] [ADS]
  8. Hirashima, K., Moriwaki, K., Fujii, M., Hirai, Y., Saitoh, T., & Makino, J. (2023). Predicting the Expansion of Supernova Shells Using Deep Learning toward Highly Resolved Galaxy Simulations, Proceedings of the International Astronomical Union, 16(S362), 209-214, [DOI] [ADS]
  9. Hirashima, K., Moriwaki, K., Fujii, M. S., Hirai, Y., Saitoh, T., & Makino, J. (2022). Predicting the Expansion of Supernova Shells for High-Resolution Galaxy Simulations Using Deep Learning, Journal of Physics: Conference Series, 2207(1), 012050, [DOI] [ADS]
    10. [ Co-Authored, Refereed ]
  10. McCabe, M., Mukhopadhyay, P., Marwah, T., et al. (incl. Hirashima, K.) (2026). Walrus: A Cross-Domain Foundation Model for Continuum Dynamics, The Forty-Third International Conference on Machine Learning (ICML), [OpenReview]
  11. Parker, L. H., Lanusse, F., Shen, J., et al. (incl. Hirashima, K.) (2025). AION-1: Omnimodal Foundation Model for Astronomical Sciences, The Thirty-Ninth Annual Conference on Neural Information Processing Systems (NeurIPS), [OpenReview]
  12. Shen, J., Lanusse, F., Parker, L. H., et al. (incl. Hirashima, K.) (2025). Universal Spectral Tokenization via Self-Supervised Panchromatic Representation Learning, NeurIPS 2025 Machine Learning and the Physical Sciences Workshop, [OpenReview]
  13. Ohana, R., McCabe, M., et al. (incl. Hirashima, K.) (2024). The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning, Proceedings of the 38th International Conference on Neural Information Processing Systems, 44989-45037, [ADS]
  14. Ohana, R., McCabe, M., et al. (incl. Hirashima, K.) (2024). The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning, NeurIPS 2024 Workshop on Data-driven and Differentiable Simulations, [OpenReview]
    15. [ Co-Authored, Non-Refereed ]
  15. Golkar, S., Bietti, A., Pettee, M. et al. (incl. Hirashima, K.) (2024). Contextual Counting: A Mechanistic Study of Transformers on a Quantitative Task, arXiv:2406.02585, [DOI] [ADS]

    16. Awards

  16. ACM Gordon Bell Prize Finalist, Architecture-Agnostic Exascale Star-by-Star Simulation of Our Galaxy, SC26, 2026 [LINK].
  17. Selected for the HPCI Excellent Achievement Award, The 13th Project Report Meeting of the HPCI System Including Fugaku, Development of High-Resolution Galaxy Formation Simulations Using AI (hp250186), 2026 [LINK].
  18. The 13th IRMAIL Science Grant HIRESO Inc. Award, High-resolution simulation of the Milky Way galaxy using AI surrogate models, Aug. 2026 [LINK].
  19. Excellence Award in the Development Division of the HPCI Software Award, ASURA-FDPS-ML, The HPCI Consortium, May 2026 [LINK].
  20. FY2025 RIKEN Research and Technology Incentive Award (RIKEN OHBU Award), Establishment of galaxy simulations resolving individual stars through the integration of AI surrogate models and large-scale parallel computing, Apr. 2026 [LINK].
  21. The Next Generation Researcher Award, The 4th Research Workshop of ”Program for Promoting Research on the Supercomputer Fugaku", First High-Resolution Galaxy Simulations Accelerated by an AI-based Surrogate Model for Supernovae, Feb. 2025 [LINK].

    22. Media, Interviews, and Videos

    [ Interviews and Features ]
  22. High-Resolution Award Winner Interview, The 13th IRMAIL Science Grant, Aug. 2026 (in Japanese) [INTERVIEW].
  23. The Forefront of AI and HPC: Galaxy Evolution Simulations Using AI, Information Technology Center, The University of Tokyo, Mar. 2026 [INTERVIEW].
  24. Reproducing the Milky Way in Fine Detail and at High Speed by Combining Simulations and AI, HPCI Magazine “Fugaku Hyakkei” Vol. 19, 2025 (in Japanese) [FEATURE].
  25. Self-introduction: Keiya Hirashima, RIKEN iTHEMS “Person of the Week”, Jun. 2025 [PROFILE].
    26. [ Press Releases and Research News ]
  26. Teaching AI Patterns to Achieve High-Resolution and High-Speed Galaxy Simulations, NAOJ News No. 349, Feb. 2026 (in Japanese) [ARTICLE (PDF)].
  27. The Simulated Milky Way: 100 Billion Stars Using 7 Million CPU Cores, RIKEN, Nov. 2025 [PRESS RELEASE].
  28. AI vs Supercomputers Round 1: Galaxy Simulation Goes to AI, RIKEN, Jul. 2025 [PRESS RELEASE].
  29. Deep Learning Speeds Up Galactic Calculations, The University of Tokyo, Oct. 2023 [PRESS RELEASE].
    30. [ Video Features ]
  30. Ask a STEM Professional: Keiya Hirashima (Astronomy and AI), HPCI Outreach “Fugaku Hyakkei”, Mar. 2026 (in Japanese) [PROFILE] [YOUTUBE].
  31. Next-Generation Researcher Award 2024: Accelerating High-Resolution Galaxy Formation Simulations Using an AI Surrogate Model, HPCI Outreach “Fugaku Hyakkei”, 2026 (in Japanese) [AWARD PAGE] [YOUTUBE].
  32. HPCI Magazine “Fugaku Hyakkei” Vol. 19: Reproducing the Milky Way by Combining Simulations and AI, 2025 (in Japanese) [FEATURE] [YOUTUBE].

    33. Grants, Fellowships, and Scholarships

    Research Grants
  33. The 13th IRMAIL Science Grant, High-resolution simulation of the Milky Way galaxy using AI surrogate models, PI, FY2026 [LINK]
  34. Grant-in-Aid for JSPS Fellows, Co-evolution of Galaxies and the Interstellar Medium Revealed by AI Surrogate Modeling and Simulations (25KJ0234), FY2025 - FY2027, PI, 3.51M JPY (declined) [KAKEN]
  35. Grant-in-Aid for JSPS Fellows, Forecasting SN shells using deep learning for video predictions toward higher-resolution galaxy simulations (22J23077, 22KJ1153), FY2022 - FY2024, (2.5M JPY)
  36. JSPS Overseas Challenge Program for Young Researchers (CCA/Flatiron Institute, USA), FY2022, (11,700)
  37. The University of Tokyo Computational Science Alliance Travel Support (For NeurIPS 2023 workshop), 2023
  38. International Astronomical Union Grants 2024 (for XXXII IAU General Assembly in Cape Town, South Africa)
    39. Computing Time
  39. FY2026 Period-A Junior Researchers Project, Supercomputer Fugaku, RIKEN Center for Computational Science, PI, 168M CPU-hours
  40. Oak Ridge Leadership Computing Facility Director's Discretion Project, Frontier, PI, 80,000 GPU-hours, 2026
  41. Argonne Leadership Computing Facility Director's Discretionary Allocation Program, Aurora, PI, 420,000 GPU-hours, 2025
  42. FY2025 Period-A Junior Researchers Project, Supercomputer Fugaku, RIKEN Center for Computational Science, PI, 85.3M CPU-hours
  43. Recommendation Program for Young Researchers and Woman Researchers, Miyabi-G, The University of Tokyo, PI, 8,640 GPU-hours, 2025
  44. Recommendation Program for Young Researchers and Woman Researchers, Wisteria/BDEC-01, The University of Tokyo, PI, 8,640 GPU-hours, 2023
    45. Fellowships
  45. Special Postdoctoral Researcher of RIKEN, FY2025 - FY2027, (45,000/yr)
  46. Overseas Research Fellowships of the Japan Society for the Promotion of Science for Young Scientists, FY2025 - FY2027, (50,000/yr; declined)
  47. Research Fellowships of the Japan Society for the Promotion of Science for Young Scientists PD, FY2025 - FY2027, (30,000/yr; declined)
  48. Special allowance for research grant in the final year of JSPS fellow DC (特別研究員－ＤＣの採用最終年次における研究奨励金特別手当), FY2024, (2,230)
  49. Research Fellowships of the Japan Society for the Promotion of Science for Young Scientists DC1, FY2022 - FY2024, (20,000/yr)
  50. The University of Tokyo Doctoral Fellowship for Creation of Intelligent World, WINGS-IIW (The University of Tokyo World Leading Innovative Graduate Study Program - Innovation for Intelligent World), FY2022 - FY 2024, (5,000/yr)
    51. Scholarships
  51. JEES-Mitsubishi Corporation Science Technology Student Scholarship, FY2022, (11,000/yr)
  52. Repayment Exemption for Students with Excellent Grades (Exemption of all of loan), Japan Student Services Organization (JASSO) Type I (Interest-free loan) scholarship, FY2021
  53. Japan Student Services Organization (JASSO) Type I (Interest-Free loan) scholarship, FY2020 - FY2021, (5,000/yr)

    54. Research Visits and Appointments

  54. Visiting Researcher, Research Center for the Early Universe, The University of Tokyo, Tokyo, Japan, 2026 - present.
  55. Invited Guest Researcher, Center for Computational Astrophysics, Flatiron Institute, Simons Foundation, NY, USA, 2025 - present.
  56. Invited Guest Researcher, Center for Computational Astrophysics, Flatiron Institute, Simons Foundation, NY, USA, 2023 - 2024.
  57. Invited Guest Researcher, Center for Astrophysics | Harvard-Smithsonian, MA, USA, (supported by JSPS and MIT), 2023/11.
  58. Research Intern, RIKEN R-CCS International HPC Computational Science Internship Program, Kobe, Japan, 2020/08.

    59. International Conferences

    [ Invited ]
  59. FAIRS Japan 2026: Future of Artificial Intelligence for Science in Japan, Nagoya, Japan, 2026/10
  60. What We Do in the Aurora, iTHEMS NOW & NEXT 2026, RIKEN, Wako, Japan, 2026/04 [LINK]
  61. Star-by-star Simulations of the Milky Way Accelerated by AI Surrogate Modeling, The 13th FugakuNEXT Application Seminar, RIST, Tokyo, Japan, 2026/02 [LINK]
  62. Star-by-star Galaxy Simulations Accelerated by Surrogate Modeling for Supernova Feedback, IAU Commission B1 - Challenges and Innovations in Computational Astrophysics VI, IISER Mohali, India, 2025/10 [LINK]
  63. Applications of AI Surrogate Models and Large-Scale Simulations to Astronomy, Big Data Astronomy 2025, University of Tsukuba, Tsukuba, Japan, 2025/05 [LINK]
  64. Accelerating Multi-Scale Fluid Dynamics and Galaxy Simulations with Machine Learning Surrogate Modeling, International Conference on Scientific Computing and Machine Learning 2025, 2025/03 [LINK]
    65. [ Refereed ]
  65. Architecture-Agnostic Exascale Star-by-Star Simulation of Our Galaxy, ACM Gordon Bell Finalists Presentations, SC26, Chicago, USA, 2026/11 [LINK]
  66. The First Star-by-star N-body/Hydrodynamics Simulation of Our Galaxy Coupling with a Surrogate Model, SC25, St. Louis, USA, 2025/11 [DOI]
  67. Surrogate Modeling for Supernova Feedback toward Star-by-Star Simulations of Milky-Way-sized Galaxies, XXXII IAU General Assembly 2024, FM7 NEW HORIZONS AT THE INTERFACE BETWEEN COMPUTATIONAL ASTROPHYSICS AND BIG DATA, Cape Town, South Africa, 2024/08 [LINK]
  68. Surrogate Modeling for Computationally Expensive Simulations of Supernovae in High-Resolution Galaxy Simulations, NeurIPS 2023 AI for Science Workshop, New Orleans, USA, 2023/12 [LINK]
    69. [ Non-Refereed ]
  69. Surrogate Modeling for Supernova Feedback toward Star-by-Star Simulations of Milky-Way-sized Galaxies, The 2nd edition of the International Conference on Machine Learning for Astrophysics (ML4ASTRO2), Sicily, Italy, 2024/07 [LINK]
  70. Surrogate Modeling for Supernova Feedback toward High-Resolution Galaxy Simulations, AI-driven discovery in physics and astrophysics, Kavli IPMU, The University of Tokyo, Chiba, Japan, 2024/01 [LINK]
  71. Surrogate modeling for supernova feedback in galaxy simulations, The 8th Japan-US Science Forum in Boston, Consulate-General of Japan, Boston, USA, 2023/11 [LINK]
  72. Forecasting the expansion of SN shells toward high resolution galaxy simulations, Cosmic Connections: A ML X Astrophysics Symposium, poster, Flatiron Institute, NY, USA, 2023/05 [LINK]
  73. Forecasting the expansion of SN shells using deep learning toward high-resolution galaxy simulations, Challenges and Innovations in Computational Astrophysics IV, 10, Live Zoom, 2022/11 [LINK]
  74. Forecasting SN explosions Using Deep Learning toward High-Resolution Galaxy Simulations, IAUS 368: Machine Learning in Astronomy: Possibilities and Pitfalls, 3322, e-talk, 2022/8 [LINK]
  75. Predicting the expansion of supernova shell for high-resolution galaxy simulations using deep learning, IAUS 362: Predictive Power of Computational Astrophysics as a Discovery Tool, Live Zoom, 2021/11 [LINK]
  76. Predicting the expansion of supernova shell for high-resolution galaxy simulations using deep learning, XXXII IUPAP Conference on Computational Physics, Live Zoom, 2021/7 [LINK]
  77. Predicting the expansion of supernova shell for high-resolution galaxy simulations using deep learning, IAU CB1 ChaICA-III2021, Live Zoom, 2021/6 [LINK]

    78. Domestic Conferences

    [ Invited ]
  78. Galaxy (Formation) Simulations and Applications of AI, CfCA Users' Meeting 2025, National Astronomical Observatory of Japan, Mitaka, Japan, 2026/01 [LINK]
  79. AI-Enhanced High-Resolution Galaxy Simulations and Prospects for Foundation Models, The 38th Rironkon Annual Symposium 2025, University of Tsukuba, Tsukuba, Japan, 2025/12 [LINK]
  80. 超新星フィードバックのサロゲートモデルを用いた銀河形成シミュレーションの高速化 (Accelerating Galaxy Simulations using Surrogate Modeling for Supernova Feedback), The 2024 Spring Annual Meeting of the Astronomical Society of Japan, Z223r, Tokyo, 2024/3 [LINK]
    81. [ Contributed ]
  81. 大規模並列計算とAIで実現するStar-by-star銀河シミュレーションの高速化 (Accelerating Star-by-star Galaxy Simulations with Large-Scale Parallel Computing and AI), The 2025 Spring Annual Meeting of the Astronomical Society of Japan, X39a, Tokyo, 2025/3 [LINK]
  82. First High-Resolution Galaxy Simulations Accelerated by an AI-based Surrogate Model for Supernovae, The 4th Research Workshop of ”Program for Promoting Research on the Supercomputer Fugaku", Tokyo, 2025/2 [LINK]
  83. AIサロゲートモデルを用いたstar-by-star銀河形成シミュレーションの高速化 (Accelerating Star-by-star Galaxy Simulations using AI Surrogate Modeling), The 2024 Autumn Annual Meeting of the Astronomical Society of Japan, X14a, Hyogo, 2024/9 [LINK]
  84. Surrogate Modeling for Hydrodynamics and Fluid dynamics, Nuclear Fusion and its Interdisciplinary Fields, Tokyo, 2024/05 [LINK]
  85. Surrogate Modeling for Supernova Feedback toward Star-by-star Galaxy Simulations, 第3回「富岳」成果創出加速プログラム研究交流会, Poster, Tokyo, 2024/03 [LINK]
  86. 高解像度銀河形成シミュレーションに向けた超新星フィードバックのサロゲートモデリング, 「成果創出加速」基礎科学合同シンポジウム, Tokyo, 2023/12 [LINK]
  87. Star-by-star銀河形成シミュレーションに向けた超新星フィードバックのサロゲートモデリング (Surrogate Modeling for Supernovae Feedback in toward star-by-star Galaxy Simulations), The 2023 Autumn Annual Meeting of the Astronomical Society of Japan, X46a, 2023/09 [LINK]
  88. Accelerating SN simulations using deep learning toward star-by-star galaxy simulations, Astro AI with Fugaku workshop, Tokyo, 2023/09 [LINK]
  89. 超新星フィードバックのためのサロゲートモデルの開発, シミュレーション天文学のこれまでとこれから -ハードウェア・アプリケーション・サイエンス-, Kobe, 2023/09 [LINK]
  90. 機械学習を用いた高解像度銀河形成シミュレーションの高解像度化, 第２回 スーパーコンピュータ「富岳」成果創出加速プログラム 研究交流会, 2023/03, Online [LINK]
  91. 機械学習を用いた超新星爆発シェル膨張の予測, 「富岳で加速する素粒子・原子核・宇宙・惑星」シンポジウム, Kobe, 2022/12 [LINK]
  92. 深層学習による超新星シェル膨張予測を用いた高解像度銀河形成シミュレー ションの高速化 (Forecasting the expansion of Supernova shells toward accelerating high-resolution galaxy simulations), Data Science in Astronomy 2022, b03, The Institute of Statistical Mathematics, 2022/10 [LINK]
  93. Accelerating high-resolution galaxy simulations using deep learning for predicting the expansion of SN shells, The 2022 Autumn Annual Meeting of the Astronomical Society of Japan, X52a, Live Zoom, 2022/9 [LINK]
  94. Predicting the expansion of supernova shells using deep learning and computer vision toward high-resolution galaxy simulations, The 2022 Spring Annual Meeting of the Astronomical Society of Japan, X61a, Live Zoom, 2022/3 [LINK]
  95. 深層学習を用いた超新星爆発によるシェル膨張の予測, 天体形成研究会2021, 筑波大学, オンライン, 2021/10 [LINK]
  96. Predicting the Expansion of Supernova Shell Using Deep Learning, The 2021 Autumn Annual Meeting of the Astronomical Society of Japan, X44a, Live Zoom, 2021/9 [LINK]
  97. 深層学習を用いた超新星爆発によるシェル膨張の予測, 2021年度 第51回 天文・天体物理若手夏の学校, オンライン, 2021/8 [LINK]
  98. Predicting the expansion of supernova shell for high-resolution galaxy simulations using deep learning, 新学術A03班 夏の会合プログラム, 九州大学・オンライン, 2021/7
  99. 星団の高速・高精度シミュレーション用アルゴ リズムBRIDGEとその応用, 2020年度 第50回 天文・天体物理若手夏の学校, オンライン, 2020/8 [LINK]

    100. Seminars

  100. CPS Seminar, Center for Planetary Science, Kobe University, 2025/5
  101. Galaxy Formation Seminar, Center for Computational Astrophysics / Flatiron Institute, 2024/09
  102. Surrogate Modeling for Supernova Feedback toward Star-by-Star Simulations of Milky-Way-sized Galaxies, ABBL-iTHEMS joint astro seminar, RIKEN Interdisciplinary Theoretical and Mathematical Sciences Program, 2024/05 [LINK]
  103. Douglas Finkbeiner's group meeting, Center for Astrophysics | Harvard University, 2023/11
  104. Astro-AI group meeting, Center for Astrophysics | Harvard University, 2023/11
  105. Sony Group Corporation, Online, 2023/11
  106. CPS Seminar, Center for Planetary Science, Kobe University, 2023/10
  107. Ken Nagamine's group meeting, Osaka University, 2023/10
  108. The University of Tokyo GCL/IIW Monthly meeting, online, 2023/9
  109. Evan Schneider's group meeting, University of Pittsburgh, 2023/6
  110. Coffee talk, Institute for Advanced Study, 2023/6
  111. Tri-state Cosmology X data Science tag up, Center for Computational Astrophysics / Flatiron Institute, 2023/4

    112. Teaching Experiences

  112. The 16th International High Performance Computing Summer School 2026
    • July 2026, Returning Mentor, Perth, Australia.
  113. Galaxy School 2022 [LINK]
    • 2022, Teaching Assistant.
  114. Computational Astronomy
    • Summer 2022, Teaching Assistant for Assoc. Prof. Michiko Fujii.
    • Summer 2021, Teaching Assistant for Assoc. Prof. Michiko Fujii.
    • Summer 2020, Teaching Assistant for Assoc. Prof. Michiko Fujii.

    Professional Services

  115. LOC, AI-driven discovery in physics and astrophysics, Kavli IPMU, The University of Tokyo, Chiba, Japan, 2024/01 [LINK]
  116. President of the Scientific Organizing Committee, The 53rd Summer School on Astronomy and Astrophysics, The University of Tokyo, Tokyo, Japan, 2022 - 2023, [LINK]

    117. Technical Skills

  117. Parallel and GPU Computing: C/C++, OpenMP, MPI, CUDA, SYCL, NVIDIA Nsight Systems, AMD ROCm/ROCprofiler, Intel ITT, exascale supercomputers, and GPU clusters.
  118. Machine Learning and Scientific Computing: PyTorch, TensorFlow, Keras, OpenCV, NumPy, SciPy, mpi4py, pandas, scikit-learn, and Matplotlib.
  119. Research Software: Git, Docker, Singularity, Globus, shell scripting, and vim.
  120. Working Knowledge: OpenACC, R, MATLAB, Java, and C#.

    121. Other Experiences

  121. Jun. 2022, The 12th International HPC Summer School 2022, Greek. [LINK]
  122. 2021・2022, 筑紫丘高校難関大講座, 講師, 福岡県立筑紫丘高等学校.
  123. 2020・2021, 第1回・第2回全国高校AIアスリート選手権大会, 問題作成・講師.
  124. Dec. 2020, 理学部ガイダンス＠駒場－なぜ私は理学を選んだか－, ジュニアスタッフ.
  125. Oct. 2020, 学問研究ワークショップ, 講師, 兵庫県立洲本高等学校.
  126. Aug. 2020, RIKEN R-CCS International HPC Computational Science Internship Program 2020, Intern, RIKEN R-CCS. [LINK]

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