Emerging Frontiers of AI/HPC for Science

Emerging Frontiers of AI/HPC for Science

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講義科目

Graduate School of Information Science

1Year

KIIDD7MCA1

2.00Credits

講義 (Lecture)

Fall semester 2026

(Fall semester)

芝　隼人,Rashed Essam

Graduate School of Information Science

English

日本語によるリアルタイム翻訳字幕の提供を検討中です。ただし、正確性は保証しません。

3／9

the classroom after the lecture, for 30 mins.

Course Coordinators:
    Hayato Shiba (shiba@gsis.u-hyogo.ac.jp)

    Essam Rashed (rashed@gsis.u-hyogo.ac.jp)           

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2◎／1〇

N/a

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[Course Objectives]
With the rapid advancement of AI, scientific simulation and research are undergoing a profound transformation. Across virtually all scientific domains, data from simulations and experiments are increasingly becoming targets of AI-driven analysis, and the very nature of how simulations and experiments are conducted is expected to change.   University of Hyogo will provide a comprehensive curriculum in AI/HPC for Science in the coming years. This course serves as the first step in that initiative and provides an overview of the emerging field of “AI-HPC for Science.”  

[Learning Outcomes]

• To master the fundamentals and applications of cutting-edge AI and HPC technologies recently introduced to the sciences.

• To acquire the skills necessary to explain these technological trends and integrate AI-HPC tools into individual research projects.

Keyword:  AI for Science, Big Data, Quantum Computing, Fugaku & Fugaku NEXT

In this lecture series, experts relevant to AI/HPC for Science are invited to give lectures on advanced topics listed below:

1. An Invitation to AI-HPC for Science  (Satoshi Matsuoka, RIKEN R-CCS)
2. Integration of Simulation/Data/Learning and Beyond (Kengo Nakajima, RIKEN R-CCS)
3. What is essential to the Ozaki scheme emulating FP64 format?  (Toshiyuki Imamura, RIKEN R-CCS)
4. Scaling Artificial Intelligence on Supercomputers (Mohamed Wahib, RIKEN R-CCS)
5. Advancing System Software for AI4S, FugakuNEXT, and Future Supercomputing (Kento Sato, RIKEN R-CCS)
6. AI for Medical Imaging Science: From Image Acquisition to Clinical Reporting (Essam Rashed, Univ. of Hyogo)
7. Quantum Computing and Quantum HPC Hybrid Platform (Tamiya Onodera, RIKEN R-CCS & Miwako Tsuji, Tsukuba Univ.)
8. AI for Electrochemical Materials Research (Tomohiro Fukushima, Hokkaido University)
9. A large-scale HPC operation powered by AI (Fumiyoshi Shoji, RIKEN R-CCS)

In-person

This course is delivered via simultaneous broadcast from the Kobe Information Science Campus to the Kobe Campus for Commerce. Therefore, although the "Class Format" column indicates "In-person," students attending from the  Kobe Campus for Commerce will be participating in a "Remote" (live-streamed) format.

Students who are not affiliated with the Kobe Information Science Campus or the Kobe Campus for Commerce.as well  as those with special circumstances, may be permitted to attend classes online via Zoom(application required). Please check the announcements on Universal Passport for further details.

Fully permitted

When using generative AI, please pay attention to the contents described in “Guidelines on the Use of Generative AI in Education at the University of Hyogo (For Students)”.

In this course, the use of generative AI is fully permitted for all activities, including in-class work, preparation and review, and the creation of reports or other deliverables. No restrictions are imposed on its use. However, submitting AI generated output directly as assignments or reports is strictly prohibited. It is important to verify factual accuracy and check/add sources and references for any content generated by AI. Whether students must indicate the use of AI in reports or other submissions will be determined by the instructor’s instructions.

Not specified

As this course focuses on a new field of research, no references are specified in the syllabus. Up-to-date literature will be introduced by the instructor during the course as needed.

Review & Research Integration: 30 hours
　　　Reviewing lecture content and applying AI-HPC methods to individual research projects.

Report Writing: 15 hours
　　　Preparing and finalizing the required reports.

Active learning is not adopted.

Students who demonstrate understanding of the assigned tasks and complete the required reports will receive grades based on their level of proficiency in achieving the course objectives and learning outcomes. Grades will be assigned as follows: S (90 points or higher), A (80 points or higher), B (70 points or higher), and C (60 points or higher). Evaluation will be based on the assessment of the task reports. However, students who fail to submit assignments on three or more occasions will not be eligible for course credit.

Feedback on reports will be provided through the "Class Profile" feature of Universal Passport.

This course is available to both Master’s and Doctoral students.
Please note that credits obtained for this course in the Master's program cannot be duplicated in the Doctoral program.
Eligible students should pay close attention during course registration.

The reports should be submitted in Microsoft Office formats (docx) or PDF files through the Universal Passport.
In the event of any discrepancy between the English and Japanese versions, the English version shall prevail.