About

Kun Zhang is a professor in the Department of Philosophy at Carnegie Mellon University and an affiliate faculty member in the machine learning department. His research interests lie in machine learning and artificial intelligence, with a particular focus on 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 perspective, and studies the philosophical foundations of causation and machine learning. His work encompasses theoretical, algorithmic, and application-oriented aspects of causal discovery, data analytics from a causal perspective, and fundamental principles to characterize causality. In addition to his theoretical contributions, Kun Zhang's research extends to practical computational methods for causal inference, latent variable modeling, and statistical machine learning. His applied interests include neuroscience, computational finance, climate analysis, and healthcare, where he applies his causal learning frameworks to real-world data such as fMRI, MEG, EEG, and financial data. His work also involves domain adaptation, transfer learning, and learning in nonstationary environments, utilizing techniques such as kernel distribution embedding, Gaussian processes, and mixture models. Overall, his research integrates philosophical insights with computational approaches to advance understanding in causality, machine learning, and their applications across various scientific domains.

Research topics

• Materials science
• Biology
• Optoelectronics
• Genetics
• Chemistry
• Chemical engineering
• Internal medicine
• Composite material
• Nanotechnology
• Organic chemistry
• Engineering
• Physiology
• Psychology
• Metallurgy
• Medicine

Selected publications

• Bidirectional two-sample Mendelian randomization analysis identifies causal associations between relative carbohydrate intake and depression

  Nature Human Behaviour · 2022 · 144 citations

  • Internal medicine
  • Medicine
  • Psychology
  DOI

• Ultrastrong nanotwinned titanium alloys through additive manufacturing

  Nature Materials · 2022 · 213 citations

  • Materials science
  • Metallurgy
  • Composite material
  DOI

• Ultra-sensitive ethanol gas sensors based on nanosheet-assembled hierarchical ZnO-In2O3 heterostructures

  Journal of Hazardous Materials · 2020 · 281 citations

  1st authorCorresponding
  • Materials science
  • Chemistry
  • Nanotechnology
  DOI

Frequent coauthors

• Mo Yang

  University of Shanghai for Science and Technology

  92 shared

• Amir Faghri

  55 shared

• Gery P. Guy

  National Center for Injury Prevention and Control

  51 shared

• Giuseppe Giaccone

  Jacobi Medical Center

  49 shared

• Jianhua Zhou

  Shaoyang University

  41 shared

• J. K. Chen

  University of Missouri

  39 shared

• Yijin Mao

  35 shared

• George F. Vande Woude

  34 shared

Labs

• Kun Zhang LabPI

Education

• Ph.D., Mechanical Engineering

  University of Connecticut

  1998

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• Ignacio Grossmannh-117
• Neil Donahueh-116