The 1st Workshop on AI for Data Editing aims to bring together researchers, practitioners, and policymakers to explore innovative AI-driven solutions for the multifaceted challenges in data editing. As data grows exponentially, there is an urgent demand for advanced strategies in data preprocessing, cleaning, transformation, quality control, as well as better understanding of complex interdependencies in large-scale data workflows. This workshop will delve into the latest AI technologies to facilitate efficient, accurate, and human-centric data editing processes.

The workshop is held in conjunction with the 31th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD-2025), one of the world's premier conferences in data science, data mining, and big data analytics. Organized by ACM SIGKDD, KDD is a key platform where researchers, practitioners, and industry experts present groundbreaking advancements and explore emerging trends in data mining, machine learning, and AI. This partnership provides the workshop with a vital forum for interdisciplinary knowledge exchange, fostering collaboration among data scientists, domain experts, and policymakers.

Submission Guidelines

We invite the submission of regular research papers, which cannot exceed 9 pages, including an appendix, plus unlimited references (paper content is limited to 9 pages, that means that if you have an appendix, then it should be included within that page limit. It is also ok if you do not have an appendix and instead 9 pages of content). Submissions must be in PDF format, and formatted according to the new Standard ACM sigconf template. Submitted papers will be assessed based on their novelty, technical quality, potential impact, insightfulness, depth, clarity, and reproducibility. All the papers are required to be submitted via the easychair system. 

Committees

Program Committee

• Wangyang Ying
• Nanxu Gong
• Tao Zhe
• Rui Liu
• Jinhan Zhang
• Xinyuan Wang
• Meng Xiao
• Ziyue Qiao
• Haoyue Bai
• Lu Jiang
• Xiaosa Zhao

Organizing committee

• Yanjie Fu 
• Kunpeng Liu
• Dongjie Wang
• Xiangliang Zhang
• Khalid Osman
• Charu Aggarwal
• Suzanne M. Shortz
• Huan Liu
• Jian Pei

Contact

All questions about submissions should be emailed to yanjie.fu@asu.edu