1. Theoretical Machine Learning and Deep Learning

This area focuses on the fundamental theories of machine learning and deep learning, exploring new algorithms and models to enhance the performance and generalization ability of learning algorithms. Specific topics include the convergence analysis of optimization algorithms, model complexity control, and the interpretability of deep learning models. Distributed deep learning technologies are employed to improve model training speed and efficiency, supporting large-scale data processing and the efficient execution of complex tasks.

2. Big Data and Statistical Learning

This area concentrates on statistical learning problems in big data environments, studying high-dimensional data analysis, sparse models, and distributed learning algorithms. Through innovations in statistical learning theory, it aims to solve key issues in big data analysis, improving the efficiency and accuracy of data mining and knowledge discovery.

3. Optimization and Game Theory

This area explores the application of optimization theory and game theory in cloud computing and network environments, developing efficient optimization algorithms to solve complex problems such as resource allocation, task scheduling, and network design. Game theory analysis is used to study the competition and cooperation relationships within systems, optimizing resource utilization and system performance.