The 9th International Symposium on Big Data and Applied Statistics (ISBDAS 2026) will be held from March 6 to 8, 2026, in Guangzhou, China. This conference aims to establish a high-level platform for global experts, engineers, researchers, and industry professionals in "Big Data" and "Applied Statistics" to share cutting-edge research and technological innovations, track academic trends, broaden research perspectives, foster in-depth scholarly collaboration, and accelerate industrial partnerships for academic achievements. 

The topics of interest for submission include, but are not limited to:

◕ Big Data

· Big Data Analytics 
· Models, Architecture, and algorithms of Big Data 
· Big Data Search and Information Retrieval Techniques
· Big Data Acquisition, Integration, Cleaning
· Scalable Computing Models, Theories, and Algorithms
· Big Data and Deep Learning 
· Big Data and High Performance Computing
· Cyber-Infrastructure for Big Data
· Resource Management Approaches for Big Data Systems
· Big Data Applications for Internet of Things
· Big Data Applications for Smart City
· Scalability of Big Data Systems 
· Big Data Privacy and Security
· Big Data Archival and Preservation
· Big Data Transformation, and Presentation
· Distributed Big Data Storage Architectures
· High-Performance Big Data Processing Frameworks
· Cloud Native Big Data Computing Models
· Lossless Big Data Compression Algorithms
· Edge - Cloud Collaborative Big Data Computing

◕ The applied mathematics theory
· Statistical Computing in Big Data Environments
· Statistical Methods for High-Dimensional Data Analysis
· Applications of Nonparametric Statistical Methods in Data Mining 
· Statistical Learning Theory and Algorithms
· Statistical Software & Tool Development
· Advanced Cluster Analysis Algorithms 
· Data Multivariate Statistical Methods 
· Statistical Data Fusion in Sensor Networks
· Statistical Classification Algorithms in Pattern Recognition
· Time Series Forecasting & Modeling
· Statistical Analysis and Prediction in Power Systems 
· Statistical Modeling and Optimization in Communication Networks 
· Statistical Reliability Prediction Algorithms