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Kun Zhang

Acting Chair of Machine Learning, Professor of Machine Learning, and Director of Center for Integrative Artificial Intelligence (CIAI)

Research interests

Zhang’s research interests lie in machine learning and artificial intelligence, especially in causal discovery and inference, causal representation learning, and machine learning under data heterogeneity. He aims to make causal learning and reasoning transparent in science, AI systems, and human society.

On the application side, he is interested in biology, neuroscience, computer vision, computational finance, and climate analysis. His research has been motivated by real problems in healthcare, biology, neuroscience, computer vision, computational finance, and climate analysis.

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Zhang maintains an associate professorship at Carnegie Mellon University (CMU) in the USA to explore machine learning and AI, especially causal learning, and reasoning, at MBZUAI. Zhang formulates principles and develops methods for automated causal discovery or causal representation learning from various kinds of data; investigates learning problems including transfer learning, representation learning, and deep learning from a causal view; and studies the philosophical foundations of causation and various machine learning tasks.

Zhang is a general and program chair of the 1st Conference on Causal Learning and Reasoning (CleaR 2022) and a program chair of the 38th Conference on Uncertainty in Artificial Intelligence (UAI 2022).

Senior research scientist at the Max-Planck Institute for Intelligent Systems, Germany.
Postdoctoral fellow at the University of Helsinki, Finland.
Ph.D. in computer science from the Chinese University of Hong Kong.
Bachelor of Science in automation from the University of Science and Technology of China, China.

Co-authored a best paper finalist paper at CVPR 2019.
Co-authored best student paper at UAI 2010.
Best benchmark award of the causality challenge 2008.
Test of Time Award Honorable Mention at ICML 2022
Best Paper Award of the workshop on New Frontiers in Adversarial Machine Learning (AdvML 2022) at ICML 2022
General and program chair of the 1st Conference on Causal Learning and Reasoning (CleaR 2022)
Program chair of the 38th Conference on Uncertainty in Artificial Intelligence (UAI 2022)

Publication Kun Zhang

Zhang co-authored a best student paper at UAI 2010, received the best benchmark award of the causality challenge 2008, and co-authored a best paper finalist paper at CVPR 2019.

Biwei Huang, Fan Feng, Chaochao Lu, Sara Magliacane, Kun Zhang, “AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning,” International Conference on Learning Representations (ICLR) 2022 (spotlight).
Weiran Yao, Yuewen Sun, Alex Ho, Changyin Sun, Kun Zhang, “Learning Temporally Latent Causal Processes from General Temporal Data,” International Conference on Learning Representations (ICLR) 2022.
Petar Stojanov, Zijian Li, Mingming Gong, Ruichu Cai, Jaime G. Carbonell, Kun Zhang, “Domain Adaptation with Invariant Representation Learning: What Transformations to Learn?” Neural Information Processing Systems (NeurIPS) 2021.
Jeffrey Adams, Niels Richard Hansen, Kun Zhang, “Identification of Partially Observed Linear Causal Models: Graphical Conditions for the Non-Gaussian and Heterogeneous Cases,” Conference on Neural Information Processing Systems (NeurIPS) 2021.
K. Zhang*, M. Gong*, P. Stojanov, B. Huang, Qingsong Liu, and C. Glymour, “Domain Adaptation as a Problem of Inference on Graphical Models,” Conference on Neural Information Processing Systems (NeurIPS) 2020.
Feng Xie, Ruichu Cai, Biwei Huang, Clark Glymour, Zhifeng Hao, Kun Zhang, “Generalized Independent Noise Condition for Estimating Linear Non-Gaussian Latent Variable Causal Graphs,” Conference on Neural Information Processing Systems (NeurIPS) 2020 (spotlight).

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Selected works on MBZUAI eCommons

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