New Papers can be found in the iNSPIRE page, and the latest news and events are listed below.
2026.03.13: 🚆🚆 Present at the BOOST Young Researchers Conference(BOOST若手研究会議) at 日本科学未来館.
2026.02.10 – 02.12: 🚆🚆 Invited to give a talk “Detecting Phase Transitions by Intrinsic Dimensions” in Topology in Lattice Systems 2026 at the University of Tokyo, Japan.
2026.02.07 – 02.09: 🎉🎉 Organised the JST‑Sakura Science Exchange Program: RIKEN–Fudan University joint workshop on atmospheric science, DA and ML at RIKEN‑R‑CCS, Kobe, Japan.
2026.01.19 – 01.23: 🎉🎉 Organised the 2nd “AI+HEP in East Asia” workshop at KEK, Tsukuba, Japan.
2026.01.13 – 01.15: 🚆🚆 Attended the 「富岳成果創出加速プログラム」基礎科学合同シンポジウム 2025 and presented a talk on “Physics‑Driven Generative Models : From Lattice Fields to Atmospheric Simulations”.
2025
- 2025.09.24 – 09.25: 🚆🚆 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.
- 2025.08.25 – 09.05: ✈️✈️ 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.
- 2025.08.17 – 08.20: 🚆🚆 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.
- 2025.07: 🎉🎉 Approved to start a new position as Assistant Professor in the Institute 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））”.
- 2025.02 – 2025.05: 👶 Parental leave.
- 2025.03: 🎉🎉 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 published in 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
- 2024.12: 🎉🎉 Our work “Higher‑order cumulants in diffusion models” received the Best “Physics for AI” Paper Award (Sponsored by Apple) in the “Machine Learning and the Physical Sciences” workshop at the 38th conference on Neural Information Processing Systems (NeurIPS), December 15, 2024.
- 2024.07: 🚆🚆 Invited to attend the “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: 🚆🚆 Attended the “Machine Learning and the Renormalization Group” workshop in ECT*, Italy from 27th to 31st May, and gave a talk on “Action estimation with continuous‑mixture autoregressive networks”.
- 2024.05: 🎉🎉 Our new work “Diffusion models as stochastic quantization in lattice field theory” has been published in the Journal of High Energy Physics.
- 2024.05: 🚆🚆 Attended the “Spicy Gluons (胶麻) 2024” workshop in Hefei from 15th to 18th May, and gave a plenary talk on “Deep Learning for Exploring QCD Matter”.
- 2024.04: 🎉🎉 The DEEP‑IN working group has been established at RIKEN‑iTHEMS; the kick‑off meeting was held on 23 April.
- 2024.03: 🎉🎉 Started working as a Research Scientist (PI) at RIKEN‑iTHEMS (理化学研究所 数理創造プログラム) from March 2024.
2023
- 2023.12: 🎉🎉 Started working as a visiting scholar at Institute of Modern Physics (IMP) in Fudan University for two months.
- 2023.12: 🚆🚆 Attended “The 15th Workshop on QCD Phase Transition and Relativistic Heavy‑Ion Physics (QPT 2023)” and gave a plenary talk on “Machine Learning for QCD Matter”.
- 2023.10: 🚆🚆 Visited many institutions in China (SCNU, Tsinghua University and CCNU), and attended the 第三届中国格点量子色动力学研讨会.
- 2023.07: 🚆🚆 Attended “XQCD 2023” at University of Coimbra from 26th to 28th July, and gave a talk on “Rebuilding Neutron Star EoSs from Observations with Deep Learning”.
- 2023.04: 🎉🎉 Our work “Reconstructing dense matter equation of state from neutron star observations” has been published in Phys. Rev. D.
- 2023.03: 🎉🎉 Our review paper “Exploring QCD matter in extreme conditions with Machine Learning” was posted on arXiv. It provides a comprehensive introduction of machine learning approaches to our community.
- 2023.03: 🎉🎉 Our work “Identifying lightning structures via machine learning” has been published in Chaos, Solitons & Fractals. 📢📢 It was also featured on the FIAS homepage, and reported by German media, e.g., HR TV, FAZ and Main Riedberg.
- 2023.03: 🎉🎉 Our work “Fourier‑Flow Model” has been published in Phys. Rev. D.
- 2023.02: 🚆🚆 Attended the “Machine Learning approaches in Lattice QCD” workshop at TUM‑IAS from 27 February to 3 March.