(734) 615-3656

Methodologies:
Machine Learning
Relevant Projects:

NSF


Clayton Scott

Professor

EECS, College of Engineering
Statistics, LSA

Professor of Electrical Engineering and Computer Science, College of Engineering and Professor of Statistics, College of Literature, Science, and the Arts

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.

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.