Tomasz Arodz, Ph.D.

Title: cMEDA, Sparse Models, and Sparse Signal Recovery

Abstract. Sparse solutions, that is, vectors with few non-zero components, are often preferred over arbitrary solutions. In this talk, we will discuss two applications of sparsity in biomedical research conducted within VCU Center for Microbiome Engineering and Data Analysis (cMEDA). First, linear sparse classification models we used for predicting risk of pregnancy complications from human microbiome data. Second, combinatorial group testing, which can recover an unknown binary vector from a small number of measurements, provided that the unknown signal vector is sparse. We will also provide an overview of cMEDA.