The 10th International Symposium on Big Data and Applied Statistics (ISBDAS 2027) will be held from March 12 to 14, 2027, in Nanjing, 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.

ISBDAS has built a strong legacy with past editions held in: Guangzhou (2025, 2018), Beijing (2024), Shanghai (2023), Xining (2022), Dali (2021), Kunming (2020) and Dalian (2019). We cordially invite experts, scholars, industry representatives, and professionals worldwide to participate in this grand event, collectively propelling innovation and advancement in Big Data and Applied Statistics!

Important Dates:
Full Paper Submission Date: December 28, 2026
Notification of Acceptance Date: January 26, 2027
Final Paper Submission Date: February 12, 2027
Conference Dates: March 12-14, 2027

Topics:

1. Big Data
Big Data Analytics; Models, Architecture, and Algorithms of Big Data; Big Data Search and Information Retrieval; Big Data Acquisition, Integration, and Cleaning; Scalable Computing Models and Algorithms; Big Data and Deep Learning; Big Data and High Performance Computing; Cyber-Infrastructure for Big Data; Resource Management in Big Data Systems; IoT Applications of Big Data; Smart City Applications of Big Data; Big Data Privacy and Security; Distributed Big Data Storage Architectures; Cloud-Native Big Data Computing Models; Edge-Cloud Collaborative Big Data Computing

2. Applied Statistics and Intelligent Computing
Statistical Computing in Big Data Environments; Statistical Methods for High-Dimensional Data; Nonparametric Statistical Methods in Data Mining; Statistical Learning Theory and Algorithms; Multivariate Statistical Methods; Time Series Forecasting and Modeling; Advanced Cluster Analysis Algorithms; Statistical Data Fusion in Sensor Networks; Statistical Classification in Pattern Recognition; Statistical Analysis and Prediction in Power Systems; Statistical Modeling and Optimization in Communication Networks; Statistical Reliability Prediction Algorithms; Applied Mathematics and Optimization; Statistical Applications in Engineering; Statistical Software and Tool Development