Elena Balashova will present her FPO, "Shape Synthesis Using Structure-Aware Reasoning" on Monday, July 8, 2019 at 1pm in CS 105. The members of her committee are as follows: Thomas Funkhouser (adviser), Readers: Szymon Rusinkiewicz and Vivek Singh (Siemens Healthcare); Examiners: Olga Russakovsky, Adam Finkelstein, and Thomas Funkhouser (adviser). A copy of her thesis is available upon request. Everyone is invited to attend her talk. The talk abstract follows below. Shape synthesis is an important area of computer vision and graphics that concerns creation of new shapes and reconstruction from partial data. Its goal is to learn a model that can generate shapes within an object category suitable for novel shape creation, interpolation, completion, editing, and other geometric modeling applications. Existing tools learn shape properties from large collections of shapes. Although these methods have been very successful at learning how to synthesize the coarse shapes of objects in categories with highly diverse shapes, they have not always produced examples that reconstruct important structural elements of a shape. In this thesis, I describe how structure can be incorporated into the synthesis process, and how it can be used to improve generative models. First, I introduce a template-defined skeleton structure for learning a part-aware generative model in typography, where the shapes have a known structure and can be explained by a small number of templates. Next, I present a scenario of noisy archaeological wall painting (fresco) reconstruction from eroded fragments, where there is no well-defined structure and exponentially many arrangement possibilities in this case, I present a cluster evaluation function that guides the assembly process and encourages selection of good clusters. Finally, I describe a semantic landmark-based structure and how it can be used to improve a generative model of examples with extremely varied topology by means of a geometric shape-structure consistency loss. Through exploration of each type of structure, I show how reasoning with proposed structures helps synthesize more accurate and realistic shapes. I also propose a fully automatic framework for font completion. Finally, I design a genetic algorithm for wall painting reconstruction and propose an iterative outlier detection technique based on the eigenvector method.