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Kun Zhang

Professor

Bio

Kun Zhang is a professor in the Â鶹¹ÙÍø philosophy department and an affiliate faculty member in the machine learning department. His research interests lie in machine learning and artificial intelligence, especially in causal discovery and causality-based learning. He develops methods for automated causal discovery from various kinds of data, investigates learning problems including transfer learning and deep learning from a causal view, and studies philosophical foundations of causation and machine learning. On the application side, he is interested in neuroscience, computational finance, and climate analysis.

Research

Causal discovery: Theory, algorithms, and applications

• Practical computational methods for causal discovery and inference
• Data analytics from a causal perspective
• Fundamental and testable principles to characterize causality
• Latent variable modeling

Statistical machine learning and applications (especially from a causal perspective)

• Domain adaptation/transfer learning
• Learning in nonstationary/heterogeneous environments
• Kernel distribution embedding
• Gaussian processes, semi-supervised learning
• Mixture models
• Model selection
• Independent component analysis
• Sparse coding

Neuroscience (especially fMRI, MEG, and EEG data analysis), climate analysis, and healthcare

Computational finance

• Volatility modeling and risk management
• Factor models in finance
• Causality in finance

Publications

Please visit for publication information.