Zhi-Hua Zhou is a renowned computer scientist and a professor at Nanjing University in China. He is particularly known for his work in artificial intelligence, machine learning, and pattern recognition. With a D-index of 114, over 67,534 citations, and 626 publications, he ranks 105th globally and 12th nationally in computer science.

Zhou has made significant contributions to artificial intelligence, machine learning, pattern recognition, mathematical optimization, and data mining throughout his career. His work in artificial intelligence involves semi-supervised learning, ensemble learning, artificial neural networks, training sets, and supervised learning. Zhou has explored various aspects in machine learning, including task analysis and pattern recognition. His data mining research often intersects with cluster analysis.

Zhou's recent work from 2017 to 2021 has focused on artificial intelligence, machine learning, mathematical optimization, deep learning, and regret. Some of his most famous works during this period include "A brief introduction to weakly supervised learning," "Multi-Label Learning with Global and Local Label Correlation," and "Abductive Learning: towards bridging machine learning and logical reasoning."

Zhi-Hua Zhou has received numerous awards and recognitions throughout his career. Among these are the Research.com Computer Science in China Leader Award in 2023 and 2022, the Edward J. McCluskey Technical Achievement Award from IEEE Computer Society in 2019, and becoming a Fellow of the American Association for the Advancement of Science (AAAS) in 2016. He is also a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) and a member of Academia Europaea.