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There is currently an unprecedented demand for efficient, quantitative and interpretable methods to study large-scale (often multi-modal) data. One key area of interest is that of tensor decomposition, which seeks to automatically learn latent trends or topics of complex data sets, providing practitioners a view of what is “going on” inside their data. Math professor Jamie Haddock will survey tools for topic modeling on matrix and tensor data. These tools are of interest across the many fields and industries producing, capturing and analyzing big data, but are of particular interest in applications where expert supervision is available and often essential (e.g., medicine). We will describe an applications of these methods to medical data; an ongoing application to cardiovascular imaging data.

Jamie Haddock, assistant professor of mathematics, works in optimization, applied convex geometry and mathematical data science. Haddock was previously a Computational and Applied Mathematics Assistant Professor (postdoc) in the UCLA Mathematics Department.