I’m Lingxiao Wang (王 凌霄), currently a Research Scientist（研究員） at RIKEN iTHEMS (理化学研究所 数理創造研究センター). Starting from November 2025, I will also serve as an Assistant Professor at the Institute for Physics of Intelligence (iPi), the University of Tokyo.
My research interests span Machine Learning in Physics—particularly in Quantum Chromodynamics (QCD) (e.g., QCD matter, Lattice QCD, etc.)—as well as Collective Behavior and Atmospheric Sciences.
At RIKEN iTHEMS, I am the main facilitator of the “DEEP-IN” working group, which focuses on developing deep learning models to tackle inverse problems in science. I have also organized numerous Machine Learning in Physics seminars for the community; past activities can be found on our MLP club page.
If you are interested in academic collaboration, please feel free to contact me via lingxiaowang[at]foxmail.com.
🔥 News
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2025.09.24 -25: 🚆🚆 Attend the R7年度学術変革A「学習物理学」領域会議プログラム at the University of Tokyo.
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2025.08.25 - 09.05: ✈️✈️ I was invited to attend the “Build Big or Build Smart: Examining Scale and Domain Knowledge in Machine Learning for Fundamental Physics” Workshop at Munich Institute for Astro-, Particle and BioPhysics(MIAPbP), Germany.
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2025.08.17 - 08.20: 🚆🚆 I was invited to give a lecture in 第三届量子场论前沿研讨会(the 3rd Workshop on Frontiers of Quantum Field Theory) at Guiyang, China.
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2025.07: 🎉🎉 I was approved to start a new position as Assistant Professor in the Instite of Physics Intelligence(iPI) at the UTokyo from November 2025 under the support of “JST-BOOST Program 次世代AI人材育成プログラム（若手研究者支援））”.
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2025.02 - 2025.05: 👶 I was in Parental Leave for my child.
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2025.03: 🎉🎉 I was awarded a new grant “QCD物理の逆問題を解くための物理駆動型深層学習” in 学術変革領域研究（Ａ）- 公募研究 from 文部科学省科学研究費補助金.
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2025.01: 🎉🎉 Our review paper “Physics-Driven Learning for Inverse Problems in Quantum Chromodynamics” got publihsed on “ Nature Reviews Physics”. It provides a structured and concise overview of how incorporating prior knowledge such as symmetry, continuity and equations into deep learning designs can address diverse inverse problems across different physical sciences.
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2024.12: 🎉🎉 Our work “Higher-order cumulants in diffusion models” got the Best ‘Physics for AI’ Paper Award (Sponsored by Apple) in “Machine Learning and the Physical Sciences” Workshop at the 38th conference on Neural Information Processing Systems (NeurIPS), December 15, 2024.