Full name: 2025 The 9th International Conference on Computing and Data Analysis Abbreviation: ICCDA 2025 November 14-16, 2025 - Chengdu, China More details, please visit: www.iccda.org International Conference on Computing and Data Analysis (ICCDA) is an annual conference held each year. It is an international forum for academia and industries to exchange visions and ideas in the state of the art and practice of computing and data analysis. ICCDA 2025 will be located in Chengdu, China during November 14-16, 2025. It is hosted by Sichuan Tourism University, China and Chengdu University of Technology, China, and co-sponsored by Wenzhou University of Technology, China, Wenzhou University, China, Wenzhou Business College, China, and Concordia University, Canada, supported by Wenzhou Computer Federation, Wenzhou Software Industry Association. The previous editions of ICCDA were held in Wenzhou, China; Guizhou, China; Shanghai, China (Virtual); Sanya, China (Virtual); Silicon Valley, USA; University of Hawaii Maui College, Kahului; Northern Illinois University (NIU) DeKalb Florida Polytechnic University, Lakeland. *Conference Awards Outstanding Organization Award Outstanding Contribution Award Best Paper Award Best Oral Presentation Award Best Poster Presentation Award *Proceedings Full Paper submitted and accepted after successful registration will be published by ICCDA Conference Proceedings, which will be indexed by Scopus & Ei Compendex. *Previous ICCDA Past ICCDA papers were all published in the prestigious ACM proceedings: ICCDA 2024, ISBN: 979-8-4007-1044-5, index soon ICCDA 2023, ISBN: 979-8-4007-0057-6 EI, Scopus indexed ICCDA 2022, ISBN: 978-1-4503-9547-2, EI, Scopus indexed ICCDA 2021, ISBN: 978-1-4503-8911-2, EI, Scopus indexed ICCDA 2020, ISBN: 978-1-4503-7644-0, EI, Scopus indexed ICCDA 2019, ISBN: 978-1-4503-6634-2, EI, Scopus indexed ICCDA 2018, ISBN: 978-1-4503-6359-4, EI, Scopus indexed ICCDA 2017, ISBN: 978-1-4503-5241-3, EI, Scopus indexed *Submission Link https://www.zmeeting.org/submission/iccda2025 *Topics Mathematical, probabilistic and statistical models and theories Machine learning theories, models and systems Knowledge discovery theories, models and systems Manifold and metric learning Deep learning Scalable analysis and learning Non-iidness learning Heterogeneous data/information integration Data pre-processing, sampling and reduction Dimensionality reduction Feature selection, transformation and construction Large scale optimization High performance computing for data analytics Architecture, management and process for data science More topics: http://iccda.org/cfp.html *Contact Ms. Danica Cong Email: iccda_info@163.com Wechat: iconf-cs-2