Machine learning for materials design
I am a first-year Ph.D. student in the Materials Design Group at Imperial College London, supervised by Professor Aron Walsh. My research interests lie in developing and applying modern machine learning techniques, such as generative models and reinforcement learning, to the inverse design of materials at the atomic level and to uncovering the scientific principles underlying their behavior.
2025.10 - present
London, United Kingdom
Researching evaluation metrics and new algorithms for generative modeling of inorganic crystals.
Advisor: Aron Walsh
2024.02-2024.07
Vienna, Austria
Developed an algorithm for interpreting the behavior of graph neural networks.
Advisor: Thomas Gartner
2023.11 - 2024.08
Tokyo, Japan
Developed a state-of-the-art coefficient-estimation algorithm for symbolic regression for scientific discovery.
Advisor: Yoshitaka Ushiku and Ryo Igarashi
2023.04 - 2025.03
Tokyo, Japan
Studied advanced machine learning while conducting research on weakly supervised disentanglement.
Advisor: Masashi Sugiyama
2022.10 - 2023.08
Tokyo, Japan
Scaled up generative models for autonomous driving and robotics.
Advisor: Yutaka Matsuo
2019.04 - 2023.03
Tokyo, Japan
Studied the foundations of computer science while conducting research on weakly supervised classification.
Advisor: Masashi Sugiyama
Full funding of tuition fees, stipend, and research consumables support.
Support for postgraduate study abroad, including living allowance and medical coverage.
Stipend and airfare support for research exchange study.
Ranked first in the Department of Information Science based on thesis and coursework evaluation.
Supporting Japanese students who want to study abroad through Japan's largest study-abroad community.
