Special Session 1: Data Driven Techniques (CFP Flyer)

Data-driven techniques use machine learning and AI to extract insights and optimize systems based on large datasets. These techniques encompass scalable optimization for deep learning, predictive models for weather, climate, and life sciences, and decision-making in energy systems. They are crucial for high-performance computing (HPC), big data analytics, cloud computing, and edge computing, enabling real-time processing in IoT networks. Additionally, data-driven techniques enhance distributed systems, networking, and blockchain technologies, driving efficiency and innovation across various domains.

● Session Co-chairs:

Dr. Jian Wang
Faculty of Electric Power Engineering, Kunming University of Science and Technology

Dr. Lei Kou
Institute of Oceanographic Instrumentation, Shandong Academy of Sciences

Dr. Zhenming Zhang
State Key Laboratory of Low-carbon Smart Coal-fired Power Generation and Ultra-clean Emission, China Energy Science and Technology Research Institute Co.,Ltd.

● Submission Method:

 Please submit your manuscript to http://www.zmeeting.org/submission/hp3c2025 and select Track "Special Session 1: Data Driven Techniques"

● Topics of interest include (but are not limited to:
Scalable Optimization Methods for Deep Learning
Machine Learning Systems and Tools
Machine Learning for Weather and Climate
Machine Learning in Life Sciences
Data-Driven Approaches in HPC
Big Data Analytics in HPC Environments
Data-Driven Decision Making for Energy Systems
AI-Driven HPC for Exascale Systems
High Performance Data-Driven Models for Scientific Computing
Data-Driven Techniques in Distributed Systems
Cloud and Grid Computing for Big Data Applications
Edge Computing with Data Analytics
Data-Intensive Applications in IoT Networks
AI and Data-Driven Optimization in Networking
Data-Driven Blockchain Technologies