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

• Ryan Adams
• Sanjeev Arora
• Danqi Chen
• Jia Deng
• Barbara Engelhardt
• Tom Griffiths
• Elad Hazan
• Karthik Narasimhan
• Olga Russakovsky
• Sebastian Seung
• Yoram Singer

Associated Graduate Students

• Nataly Brukhim
• Farhan Damani
• Sinong Geng
• Gregory Gundersen
• Arushi Gupta
• Paul McMullen Krueger
• Zhou Lu
• Edgar Minasyan
• Geoffrey Roeder
• Ari Seff
• Zheng Shi
• Nicholas Turner
• Nikunj Umesh Saunshi
• Runzhe Yang
• Dingli Yu
• Cyril Zhang

Groups

• Artificial Intelligence & Machine Learning

Projects

• Computational Neuroscience
• Learning with partial feedback
• Online Convex Optimization
• Projection free learning
• Sublinear Optimization
• WordNet

Archived Projects (no longer active)

• Biological Process Inference from Experimental Interaction Evidence (BioPIXIE)
• Boosting
• Modeling Science
• Prediction of Protein Function and Regulation