Dr. Zhi-Hua Zhou is a Professor of Computer Science and Artificial Intelligence at Nanjing University. He was awarded the IEEE Computer Society Edward J. McCluskey Technical Achievement Award and is a Fellow of the IEEE, ACM, AAAI, and AAAS. His research interests include machine learning and data mining.
In his 2008 IEEE ICDM paper, Isolation Forest, he proposed the "iForest" algorithm, which is now widely used for anomaly detection in industry. The impact of this work is demonstrated by its over 11,300 citations: the original conference paper has been cited more than 8,800 times, and its subsequent journal version in ACM TKDD (2012) has received over 2,500 citations.
Dr. Zhou has made seminal and foundational contributions to ensemble learning, often called the "key to winning" in data mining tasks. His influential work spans virtually all aspects of the field, encompassing theory, algorithms, and applications, as comprehensively detailed in his well-known book, Ensemble Methods: Foundations and Algorithms (1st ed., 2012; 2nd ed., 2025). His contributions have helped to define the research scope of ensemble learning and deepened our understanding of the field. For example, his 2013 paper in the Artificial Intelligence journal, On the Doubt about Margin Explanation of Boosting, solved the long-standing mystery of why the "top-ten" algorithm AdaBoost appears resistant to overfitting. This work inspired the development of new algorithms that optimize margin distributions rather than single margins.
It is worth mentioning that as early as 2004, Dr. Zhou published a paper in IEEE TKDE titled NeC4.5: Neural Ensemble based C4.5. In this work, he proposed an approach where one model is used to generate pseudo-data to train a second, simpler, yet more powerful model. This represents a pioneering contribution to what is now known as "knowledge distillation," a technique that is fundamental to the development of large language models and other big models today.