Clayton Scott
Associate Professor
EECS, College of Engineering
Statistics, LSA
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.
Examples of pulses generated from a neutron and a gamma ray interacting with an organic liquid scintillation detector used to detect and classify nuclear sources. Machine learning methods take several such examples and train a classifier to predict the label associated to future observations.
