I’m Lingxiao Wang(王 凌霄), now a Research Scientist(PI) in RIKEN-iTHEMS (理化学研究所 数理創造プログラム). My research interest includes Machine Learning in Physics (especially high energy nuclear physics, e.g., QCD Matter, Lattice QCD, etc.), Medical AI and Human Behavior. See my latest CV here.

Now, I’m running a working group of “DEEP-IN” in RIKEN-iTHEMS, which aims to develop deep learning models for inverse problems in physical sciences.

I have organized many “machine learning physics” seminars for physicists online, find the previous activities in our page MLP club. If you are seeking any form of academic cooperation, please feel free to contact me via lwang[at]fias.uni-frankfurt.de.

🔥 News

• 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.

• 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.

• 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.

🔍 Researches

• Generative Models in Lattice Calculations

1. Zhu, Q., Aarts, G., Wang, W., Zhou, K. & Wang, L. Physics-Conditioned Diffusion Models for Lattice Gauge Theory. arXiv:2502.05504 [hep-lat] (2025).
2. Aarts, G., Habibi, D. E., Wang, L. & Zhou, K. On learning higher-order cumulants in diffusion models. arXiv:2410.21212 [hep-lat] (2024).
3. Xu, T., Wang, L., He, L., Zhou, K. & Jiang, Y. Building imaginary-time thermal field theory with artificial neural networks. Chin. Phys. C 48, 103101 (2024).
4. Wang, L., Aarts, G. & Zhou, K. Diffusion models as stochastic quantization in lattice field theory. JHEP 05, 060 (2024).
5. Chen, S. et al. Fourier-flow model generating Feynman paths. Phys. Rev. D 107, 056001 (2023).
6. Wang, L., Jiang, Y., He, L. & Zhou, K. Continuous-mixture autoregressive networks learning the Kosterlitz-Thouless transition. Chin. Phys. Lett. 39, 120502 (2022).

• Inverse Problems

1. Aarts, G. et al. Physics-Driven Learning for Inverse Problems in Quantum Chromodynamics. Nat Rev Phys 7, 154–163 (2025). 2.Wang, L. Deep learning for exploring hadron–hadron interactions. J. Subatomic Part. Cosmol. 3, 100024 (2025).
2. Wang, L. & Zhao, J. Learning Hadron Emitting Sources with Deep Neural Networks. arXiv:2411.16343 [nucl-th] (2024).
3. Soma, S., Wang, L., Shi, S., Stöcker, H. & Zhou, K. Reconstructing the neutron star equation of state from observational data via automatic differentiation. Phys. Rev. D 107, 083028 (2023).
4. Shi, S., Wang, L. & Zhou, K. Rethinking the ill-posedness of the spectral function reconstruction — Why is it fundamentally hard and how Artificial Neural Networks can help. Comput. Phys. Commun. 282, 108547 (2023).
5. Wang, L., Shi, S. & Zhou, K. Reconstructing spectral functions via automatic differentiation. Phys. Rev. D 106, L051502 (2022).

• AI for Science

1. Xiao, H. et al. CloudDiff: Super-resolution ensemble retrieval of cloud properties for all day using the generative diffusion model. Preprint (2024).
2. Zhou, S., Shi, R. & Wang, L. Extracting macroscopic quantities in crowd behaviour with deep learning. Phys. Scr. 99, 065213 (2024).
3. Xiang, M., Yuan, H., Wang, L., Zhou, K. & Roskos, H. G. Amplitude/Phase Retrieval for Terahertz Holography with Supervised and Unsupervised Physics-Informed Deep Learning. IEEE Transactions on Terahertz Science and Technology, 1–9 (2024).
4. Wang, L., Hare, B. M., Zhou, K., Stöcker, H. & Scholten, O. Identifying lightning structures via machine learning. Chaos Solitons and Fractals: the interdisciplinary journal of Nonlinear Science and Nonequilibrium and Complex Phenomena 170, 113346 (2023).
5. Zhong, Y.-W. et al. Tumor radiomics signature for artificial neural network-assisted detection of neck metastasis in patient with tongue cancer. Journal of Neuroradiology 49, 213–218 (2022).
6. Wang, L. et al. Machine learning spatio-temporal epidemiological model to evaluate Germany-county-level COVID-19 risk. Mach. Learn.: Sci. Technol. 2, 035031 (2021).

💼 Experiences

• 2024.03 - present, Research Scientist, RIKEN-iTHEMS, Japan
• 2023.12 - 2024.02, Visiting Scholar, Institute of Modern Physics(IMP) in Fudan University, China
• 2020.09 - 2023.12, Postdoctoral Researcher, Frankfurt Institute for Advanced Studies, Germany
• 2021.10 - 2023.10, Postdoctoral Fellow, Xidian-FIAS Joint Research Center, FIAS, Germany
• 2021.03 - 2023.03, Research Assistant, Institute of Physics, Goethe University, Germnay
• 2018.09 - 2020.09, Research Assistant, Department of Physics, Tsinghua University , China

📖 Educations

• 2015.09 - 2020.06, Ph.D., Department of Physics, Tsinghua University , Beijing, China
• 2018.10 - 2019.10, Joint Ph.D., Physics Department, University of Tokyo , Tokyo, Japan
• 2012.09 - 2015.06, B.S., School of Physics, Dalian University of Technology, Dalian, China
• 2011.09 - 2012.06, School of Chemistry, Dalian University of Technology, Dalian, China

📰 Archive

2024

• 2024.07:  🚆🚆 I was invited to attend the workshop of “EMMI Workshop at the University of Wrocław - Aspects of Criticality II” in Wrocław, Poland from 2nd to 4th July, and gave a plenary talk on “Exploring properties of extreme matter with machine learning”.
• 2024.05:  🚆🚆 I attended the workshop of “Machine Learning and the Renormalization Group” in ECT*, Italy from 27th to 31th May, and gave a talk on “Action estimation with continuous-mixture autoregressive networks”.
• 2024.05: 🎉🎉 Our new work of “Diffusion models as stochastic quantization in lattice field theory” has been published on Journal of High Energy Physics .
• 2024.05:  🚆🚆 I attended the workshop of “Spicy Gluons (胶麻) 2024” in Hefei from 15th to 18th May, and gave a plenary talk on “Deep Learning for Exploring QCD Matter”.
• 2024.04:  🎉🎉 “DEEP-IN” working group has been established at RIKEN-iTHEMS, the kick-off meeting was held on 23 of April.
• 2024.03:  🎉🎉 I start working as a Research Scientist(PI) in RIKEN-iTHEMS (理化学研究所 数理創造プログラム) from Mar. 2024.

2023

• 2023.12:  🎉🎉 I start working as a visiting scholar at Institute of Modern Physics(IMP) in Fudan University for two months.
• 2023.12:  🚆🚆 I attended the conference of “The 15th Workshop on QCD Phase Transition and Relativistic Heavy-Ion Physics (QPT 2023)” and gave a plenary talk of “Machine Learning for QCD Matter” .
• 2023.10:  🚆🚆 I visited many intitutions in China (SCNU, Tsinghua Uni. and CCNU), and attended the conference of “第三届中国格点量子色动力学研讨会” .
• 2023.07:  🚆🚆 I attended the conference of “XQCD 2023” at University of Coimbra from 26th to 28th Jul., and gave a talk on “Rebuilding Neutron Star EoSs from Observations with Deep Learning”.
• 2023.04:  🎉🎉 Our new work of “Reconstructing dense matter equation of state from neutron star observations” has been published on Phys. Rev. D .
• 2023.03:  🎉🎉 Our new review paper “Exploring QCD matter in extreme conditions with Machine Learning” was posted on arXiv:2303.15136. It aims to introduce machine learning approaches to our community comprehensively.
• 2023.03:  🎉🎉 Our new work of “Identifying lightning structures via machine learning” has been published on Chaos, Solitons & Fractals . 📢📢 It was also featured on the FIAS’s homepage , and reported by the media of Germany, e.g., HR TV , FAZ and Main Riedberg .
• 2023.03:  🎉🎉 Our new work of “Fourier-Flow Model” has been published on Phys. Rev. D .
• 2023.02:  🚆🚆 I attended the workshop “Machine Learning approaches in Lattice QCD” at TUM-IAS from 27th of Feb. to 3rd of Mar..