Hongning Wang

Hongning Wang is an Associate Professor in the Department of Computer Science at Tsinghua University. His research focuses on machine learning, information retrieval, and data mining, with an emphasis on developing theoretical and practical methods for real-world decision-making problems. His work includes interactive online learning, recommendation systems, and large-scale information retrieval. He has contributed to the development of techniques such as online learning to rank and user latent intent modeling, which have been applied in systems like Google, Bing, KuaiShou, and Instagram. He has published extensively in top-tier conferences such as NeurIPS, ICML, KDD, and SIGIR, and has received awards including the Best Paper Award at SIGIR 2019. He is also actively involved in academic leadership roles, including serving as Co-General Chair for ACM SIGIR 2024.

Hongning Wang is an Associate Professor in the Department of Computer Science and Technology at Tsinghua University, specializing in Machine Learning, Information Retrieval, and Large Language Models. His research focuses on advancing large language models through techniques such as jailbreaking defense, agent safety evaluation, and instruction-following benchmarking. He also explores explainable recommendation systems, personalized search enhancement, and contextual document ranking. His work integrates multi-task learning, reinforcement learning, and adversarial training to improve recommendation and search relevance. Wang's research emphasizes the development of robust, interpretable, and efficient models for real-world applications.