معرفی
Prof. Niao He is an Associate Professor in the Department of Computer Science at ETH Zürich. His research focuses on optimization theory, reinforcement learning, stochastic systems, and their applications in machine learning and multi-agent systems. He holds an academic position at one of the world's leading technical universities, contributing to both theoretical advancements and practical algorithmic solutions.
His research interests span optimization theory (e.g., convex/non-convex optimization, stochastic optimization), reinforcement learning (policy gradient methods, multi-agent systems), and statistical learning (risk-averse methods, entropy regularization). He has also explored applications in data science, network revenue management, and deep reinforcement learning for complex systems.
Recent work emphasizes algorithmic reproducibility, robustness under model uncertainty, and efficient methods for large-scale problems. Notable contributions include novel approaches to minimax optimization, mean-field games, and adaptive learning frameworks. His articles consistently address theoretical guarantees while maintaining practical relevance for real-world computational challenges.
