Y. Feng

Y. Feng's publication record centers on particle physics, including high-energy particle collisions, quantum chromodynamics, and detector development, with some work applying machine learning methods like graph neural networks to problems such as noise removal in particle physics data. The presence of a source-separation paper suggests some cross-disciplinary interest in applying data-driven and machine learning techniques beyond pure particle physics. Note that the OpenAlex topic relevance scores provided are all listed as 0.00, so the strength of association with these topics cannot be confirmed from the given data.

Publication output has grown substantially over the last decade, rising from single digits annually before 2021 to over 40-50 papers per year in 2023-2025, indicating a strong upward trend in research activity.

Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026