Zhaoran Wang

Zhaoran Wang is an Associate Professor at Northwestern University's McCormick School of Engineering, holding a joint appointment in Industrial Engineering and Management Sciences and Computer Science. His research focuses on developing data-driven decision-making methods that integrate machine learning, optimization, and game theory to address societal challenges. Key areas include autonomous agents, multi-agent systems, and provably efficient reinforcement learning algorithms.

His work emphasizes computational and statistical efficiency, particularly in scenarios with partial observability or confounded data. Notable contributions include frameworks for mitigating overoptimization in reinforcement learning and scalable solutions for partially observed systems. He has published widely in top venues like ICML, NeurIPS, and ICLR.

Research interests span machine learning theory, reinforcement learning, optimization, and their applications in control systems and multi-agent coordination. Recent publications emphasize theoretical guarantees for algorithms and practical scalability in real-world domains like autonomous driving and economic systems.