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., Nuclear 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. The future activities will be integrated into the “DEEP-IN” working group. If you are seeking any form of academic cooperation, please feel free to contact me via lwang[at]fias.uni-frankfurt.de.

🔥 News

• 2024.07:  🚆🚆 I am 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”.
• 2024.06:  🚆🚆 I visited FIAS, GSI and Goethe University in Frankfurt.
• 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.

🔍 Researches

• Generative Models in Lattice Calculations
  • Generative Diffusion Models for Lattice Field Theory[13,12];
  • Continuous Autoregressive Networks (CANs) for recognizing the KT phase transition [11];
  • Fourier-Flow Model (FFM) for generating Feynman paths in multimode systems [10].
• Inverse Problems
  • Reconstructing spectral functions from Lattice calculated propagators [9, 8];
  • Rebuilding Equation of States(EoSs) from neutron star observations [7, 6].
• AI for Science
  • Machine learning for epidemiology and human behaviours [5, 4];
  • Machine learning for lightning data [3] and medical imaging [2, 1].

💼 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