In multiple sequence alignment (MSA), a set of nucleotide or amino-acid sequences are “aligned” through the addition of spaces or rearrangement of individual sequences. A gap in the alignment indicates a possible loss or gain of an element and rearrangements account for inversions or translocations (particularly important for genome alignments); thus evolutionary inference of the insertion and deletion, translocation and inversion processes is inherent in MSA. In addition, MSA estimation is closely tied to phylogenetic estimation – a mathematically rich area with connections to probability theory, geometry, algebra, and graph theory. MSA estimation also informs protein function and structure prediction, and thus has strong connections to structural biology. However, these disciplines approach MSA estimation very differently. As a result, a variety of techniques have been explored, including combinatorial optimization, biophysical models of protein structure, machine learning, and probabilistic models of evolution. Despite the importance of MSA estimation and active research, many challenges persist. The research community is addressing these through improved mathematical formalization of MSA estimation; development of sophisticated and biologically meaningful models of sequence evolution that include insertions, deletions, and rearrangements; and design of new methods that have good mathematical properties and empirical performance for large datasets. This workshop will engage researchers from different fields, including mathematicians, statisticians, evolutionary biologists, structural biologists, and computer scientists, with the aim of integrating diverse viewpoints, improving mathematical foundations, and developing new and more powerful methods for estimating MSAs. This workshop will include a poster session; a request for posters will be sent to registered participants in advance of the workshop.