Machine Learning

I study patterns in large, complex data sets, and make quantitative predictions and inferences about those patterns. Problems I've worked on include classification, anomaly detection, active and semi-supervised learning, transfer learning, and density estimation. I am primarily interested in developing new algorithms and proving performance guarantees for new and existing algorithms.

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Applications are great for inspiring new problems and validating algorithms. Many applications also have the potential to impact society in a positive way. Here are some of the applications that have motivated my research (more recent projects listed earlier).

Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.