SUBMIT ONLINE: https://www.easychair.org/conferences/?conf=prai2026  (Please choose Special Session 9)

In the new era of data-driven astronomy, telescopes are continuously generating massive volumes of observational data, placing us in an unprecedented deluge of information. With these vast, complex, and high-dimensional datasets, artificial intelligence is becoming the golden key to unlock the new cosmic door. From using convolutional neural networks to automatically identify gravitational lenses, to employing generative adversarial networks to reconstruct the large-scale structure of the universe; from accurately estimating galaxy redshifts based on machine learning, to real-time classification of transient sources in time-domain astronomy through deep learning models--artificial intelligence has gradually transformed from an "auxiliary tool" in astrophysical research into a "core engine." It not only dramatically improves the efficiency of data processing but also helps us discover novel physical laws and reshapes the way we perceive the cosmos. Through this conference, we aim to bring together top-tier expertise from around the world to explore the wide-ranging applications of artificial intelligence in astrophysical research and to promote deep interdisciplinary integration.

Chair:	Chair:	Chair:
Prof. Yong-Feng Huang School of Astronomy and Space Science, Nanjing University, China hyf@nju.edu.cn	Prof. Maria Giovanna Dainotti National Astronomical Observatory, Japan mariagiovannadainotti@yahoo.it	Prof. Pei Wang National Astronomical Observatories, Chinese Academy of Sciences, China wangpei@nao.cas.cn

RELATED TOPICS
Topics of interest include, but are not limited to:

• AI-driven large-scale observational data processing and analysis methods: applications in multi-wavelength, multi-messenger facilities
• Applications of AI in time-domain astronomy: autonomous search and identification of various transient sources
• Intelligent analysis of astronomical spectra and images: deep learning-based parameter measurement, classification, and anomaly detection
• AI for astrophysics theory: accelerating numerical simulations, constructing models, and testing theories
• Applications of large language models in astronomical research: scientific literature mining, automated observation and programming

Invited Speakers
	Yong-Liang Ma Professor of Physics, School of Frontier Sciences, Nanjing University, China
Ma Yongliang is a professor and doctoral supervisor at Nanjing University. He received his Ph.D. in 2006 from the Institute of Theoretical Physics, Chinese Academy of Sciences. He subsequently conducted postdoctoral and research work at the University of Tübingen in Germany and Nagoya University in Japan. After returning to China in 2014, he served as a professor at Jilin University (including as Associate Dean of the School of Physics and Director of the Center for Theoretical Physics) and at the Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences (as Professor and Associate Dean of the School of Fundamental Physics and Mathematical Sciences). He currently works at Nanjing University. His research focuses primarily on microscopic structure of compact stars, gravitational waves of binary neutron star mergers, application of AI in compact star matter and compact star properties. Title: TBA Abstract: TBA