Machine Learning

Machine Learning

Machine learning uses computational, information-theoretic, and statistical principles to develop algorithms that using experimental and simulation-based data of real-world phenomena to make accurate predictions about these phenomena.

The CICS is investigating machine learning algorithms that operate in supervised, unsupervised, and semi-supervised settings to perform classification, regression, visualization, clustering, dimensionality reduction, generative modeling, surrogate modeling, network modeling, probabilistic graphical modeling, deep learning, inference, and structured prediction.

Faculty involved in this research area include Lizhen LinNicholas Zabaras, Fang Liu, Walter Scheirer and others.