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Machine Learning

Machine learning and computational perception research at Princeton is focused on the theoretical foundations of machine learning, the experimental study of machine learning algorithms, and the interdisciplinary application of machine learning to other domains, such as biology and information retrieval. Some of the techniques that we are studying include boosting, probabilistic graphical models, support-vector machines, and nonparametric Bayesian techniques. We are especially interested in learning from large and complex data sets. Example applications include habitat modeling of species distributions, topic models of large collections of scientific articles, classification of brain images, protein function classification, and extensions of the Wordnet semantic network.

Associated Faculty

Associated Graduate Students

  • Nataly Brukhim
  • Sinong Geng
  • Arushi Gupta
  • Paul McMullen Krueger
  • Zhou Lu
  • Edgar Minasyan
  • Geoffrey Roeder
  • Zheng Shi
  • Ahmed Shuaibi
  • Yikai Wu
  • Tianhan Xu
  • Runzhe Yang
  • Dingli Yu



Archived Projects (no longer active)

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