Kuang Kun

Kun Kuang is an Associate Professor in the College of Artificial Intelligence at Zhejiang University. He received his Ph.D. in Computer Science and Technology from Tsinghua University in 2019, coadvised by Prof. Shiqiang Yang and Prof. Peng Cui. From 2017 to 2018, he visited Prof. Susan Athey's group at Stanford University as a visiting student. His research interests include causal inference, machine learning, and data fusion. Kuang has published extensively in top-tier conferences and journals, focusing on topics such as instrumental variables, treatment effect estimation, and large language models. He is also involved in mentoring and recruiting students for research projects.

Kun Kuang is a researcher at Zhejiang University with expertise in Causal Inference, Data Mining, and Machine Learning. His work explores advanced techniques in graph convolutional networks, federated learning, and stable prediction across varying environments. Kuang has published on topics such as causal inference, legal judgment prediction, and AI for retrosynthesis prediction. His research emphasizes the integration of GNNs with BERT for text classification, as well as the development of methods to mitigate biases and improve model robustness. Additionally, he has contributed to the evaluation of AI agents in data analysis tasks and the application of causality in legal and medical domains.