Zhou Zhihua

Overview

Zhou Zhihua is a computer scientist at Nanjing University whose work centres on machine learning. A 2025 local news report on his election to the national academy gives his birth year as 1973 ^[2]. He serves as vice president of Nanjing University ^[1][2] and, from 2023, as president of the IJCAI Board of Trustees ^[1]. On 21 November 2025 the Chinese Academy of Sciences announced its election results, and he was elected a member in the Department of Information Technology Sciences ^[2].

Education and early career

His entire university education took place at Nanjing University, where he earned a B.Sc. in computer science in 1996, an M.Sc. in 1998 and a Ph.D. in 2000 ^[1]. In 2001 he joined the university's Department of Computer Science and Technology as an assistant professor ^[1].

LAMDA and research directions

In 2004 he founded LAMDA, the Institute of Machine Learning and Data Mining at Nanjing University, a group that became the base for his later research and teaching ^[1]. His research covers ensemble learning, weakly supervised learning and multi-label learning, and he has written books on machine learning and on ensemble methods ^[3].

The three directions address different practical difficulties. Ensemble learning builds several models and combines their outputs, so that mistakes made by one model can be offset by others, in the way a panel of judges is usually more reliable than a single judge. Weakly supervised learning tries to train models when labels are missing, noisy or imprecise, a common situation because accurately labelled data is expensive to produce. Multi-label learning deals with cases where one object belongs to several categories at once, rather than exactly one.

Recognition

His resume lists the National Natural Science Award of China for 2013 and 2020, the IEEE Computer Society Edward J. McCluskey Technical Achievement Award in 2019, the CCF-ACM Artificial Intelligence Award in 2020 and the ACML Distinguished Contribution Award in 2019 ^[1]. Local press reporting on his academy election also mentions two National Natural Science Second Prizes and a National Teaching Achievement First Prize, without giving the years ^[2]. His homepage lists him as a Fellow of ACM, AAAI, AAAS and IEEE ^[3].

Place in the field

The problems his research addresses are basic ones for applied machine learning: most real-world data does not arrive with clean, complete labels, and single models are rarely the most reliable option. Work on weakly supervised and multi-label learning is aimed at that gap between clean benchmark data and messy real data, while ensemble methods are a standard way of making predictions more stable. Building an institute around these topics at Nanjing University, and writing textbooks on machine learning and ensemble methods, also made the material available to students beyond his own group ^[1][3].