Academic Interests
Keywords: Nuclear Astrophysics, Astrophysics, Cosmology, Big Bang Nucleosynthesis, Machine Learning, Meta-Analysis
How did the universe form? Where did our planet come from? How were our bodies created? These questions have interested human beings for a long time.
The terminal goal of my research project is to comprehensively describe the nucleosynthesis process during and after the Big Bang. Through this research, we may gain insights into the origins of the primordial universe, stars, and our bodies. One major problem in nucleosynthesis is that theoretical models are not able to reproduce the observed abundance of elements. One possible approach to solving these discrepancies is to integrate observed and measured data into theoretical models to enable a multifaceted and self-consistent analysis of the nucleosynthesis process. Machine learning is a promising tool that can identify the most sensitive reactions in nucleosynthesis to resolve the discrepancies between theoretical models and observational data.
Background
I am working in the field of nuclear astrophysics. My main work has been measuring cross sections relevant to Big Bang nucleosynthesis, particularly focusing on the cosmological lithium problem.
Awards
- 21st CNS International Summer School 2022 (A3F-CNSSS22) Young Scientist Award (2022)
- Asian Nuclear Physics Association (ANPhA) & AAPPS-DNP Award for Young Scientist (2022)
Positions held
- Specially Appointed Researcher, Research Center for Nuclear Physics, Osaka University, Japan