Kun Zhang
Professor of Philosophy, Dietrich College of Humanities and Social Sciences
Affiliated faculty, Machine Learning
Kun Zhang is a professor of philosophy and an affiliated faculty member of machine learning at Carnegie Mellon University. He has been advancing the machine learning perspective on causality, focusing on causal discovery from tabular data and causal representation learning from multimodal sources such as text, video, and images. His contributions address long-standing challenges, including uncovering causal structures with hidden variables, distinguishing cause from effect using distributional information, developing reliable nonparametric conditional independence tests, handling measurement error and missing data, and showing how causal perspectives can benefit generative AI. He has been frequently serving as a senior area chair, area chair, or senior program committee member for major conferences in machine learning or artificial intelligence, including UAI, NeurIPS, ICML, IJCAI, AISTATS, and ICLR. He was a general and program co-chair of the first Conference on Causal Learning and Reasoning (CLeaR 2022), a program co-chair of the 38th Conference on Uncertainty in Artificial Intelligence (UAI 2022) and International Conference on Data Mining (ICDM) 2024, and is a general co-chair of UAI 2023. He currently serves as an associate editor of JASA, JMLR, IEEE TPAMI, and ACM Computing Surveys.