Mukobara Yuta / 向原 悠太
| Affiliation: | Institute of Science Tokyo / School of Environment and Society / Transdisciplinary Science and Engineering / Nuclear Engineering Course |
| Grade: | Doctoral Course, 2nd Year |
| Laboratory: | Katabuchi Laboratory |
| E-mail: | mukobara.y.636f[at]m.isct.ac.jp |
I conduct theoretical calculations in nuclear physics, focusing on nuclear data evaluation and improving the accuracy of heavy-ion therapy simulations. Utilizing antisymmetrized molecular dynamics and machine learning, I aim to contribute to the applications of nuclear physics, such as radiation medicine and nuclear reactors.
Keywords: Nuclear Physics, NUclear Data, Antisymmetrized Molecular Dynamics, Machine Learning
Background
| 2017/03 | Graduated from Yamaguchi Prefectural Hofu High School |
| 2021/03 | Graduated from Chiba Institute of Technology, Department of Computer Science, Faculty of Information Science and Technology |
| 2023/03 | M.S., Nuclear Engineering Course, Department of Interdisciplinary Science and Engineering, School of Environmental and Social Science and Engineering, Tokyo Institute of Technology |
| 2023/04 | Doctoral Course in Nuclear Engineering, Department of Interdisciplinary Science and Engineering, Graduate School of Environmental and Social Science and Engineering, Tokyo Institute of Technology |
| 2024/10~ | Doctoral Course in Nuclear Engineering, Department of Interdisciplinary Science and Engineering, Graduate School of Environmental and Social Science and Engineering, Institute of Science Tokyo |
News
- 2024/09: Proceedings "Fission Trajectory Analysis Using ML Techniques" Published in EPJ Web of Conferences
- 2024/09: Article Explaining EACL 2024 Presentation Published in Journal of Natural Language Processing
- 2024/03: Presented "Rethinking Loss Functions for Fact Verification" at EACL 2024
- 2024/01: Paper "Mean-field dependence of fragment-production cross sections in heavy-ion induced reactions calculated by antisymmetrized molecular dynamics" Published in Journal of Nuclear Science and Technology
- 2024/01: Paper "Bayesian approach to energy dependence of fission product yields of 235U by data augmentation" Published in Journal of Nuclear Science and Technology