Carnegie Mellon University

Kun Zhang

Kun Zhang

Associate Professor


Kun Zhang is an associate professor in the CMU 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.


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


Please visit Professor Zhang’s personal website for publication information.