In this paper I analyze the story of Alma, a Mexican migrant woman deported after being imprisoned for five years, and Isabel, Alma’s fifteen year old daughter. Immediately after her deportation, Alma became active in the movement for immigrant rights. When Isabel joined her in Tijuana during her summer vacation, she suddenly became the voice for children of undocumented parents. Isabel was interviewed by the media and was scheduled to speak at the Democratic National Convention in Denver, Colorado. However, the story that they were asked to tell was very different from their actual experience. Rather than speaking of Alma’s imprisonment, she was represented as a dedicated working mother who was detained by ICE while at work. Through an analysis of the media’s narrative, I argue that the representation of their story reinforces gendered racial ideas about who deserves protection and who deserves punishment.

Contemporary learning research has provided multiple paradigms that have benefited not only researchers in the field, but also applied theorists and practitioners. However, the emphasis on theory development has made the learning literature almost impenetrable to nonexperts. In the present paper, we attempt to summarize not the different theoretical perspectives that have been proposed to explain different instances of learning, but the empirical relationships that testing of such theories has uncovered. Because the empirical relationships we summarize here hold across preparations and species, we suggest that such relationships should be understood as the empirical laws of basic learning . The focus of our review is the Pavlovian conditioning tradition, but most of these relationships also apply to instrumental learning and causality learning. We hope that the relatively novel organization we present here helps researchers and practitioners to directly incorporate these empirical principles into their current theoretical framework, whatever it may be.

A shape can be defined as the representation of an object or its external boundary so as to characterize the information remaining when describing an object absent manipulations such as translation, rotation, reflection, or scaling. This includes combinatorial shapes, such as for structures defined in terms of relationships such as "parent-child", "sibling", or "predecessor-successor", as well as geometric shapes, such as those defined by polygons or polytopes. In this thesis, we provide novel methods for constructing, counting, and matching combinatorial and geometric shapes. For example, we study learning and constructing phylogenetic trees, which describe evolutionary relationships among a group of objects. Reconstructing phylogenetic trees is an important problem in computational biology, data protection and computer security. With respect to counting, we focus on characterizing the growth in the number of polycubes and families of polycubes, where a polycube is the shape defined by a connected set of $n$ copies of $d$-dimensional hypercubes. Counting polycubes of a given size has been long-studied in statistical physics, given their applications to percolation processes. We also consider constructing knight’s tour shapes, for chess board grids, optimizing two new metrics of ``simplicity'' in knight’s tours: the number of turns and the number of crossings. Finally, we focus on matching geometric shapes to real-world objects. In particular, motivated by the tracing of ethically-sourced diamonds, we develop approximation algorithms for geometric polyhedral point-set pattern matching as a minimum width polyhedral annulus problem under translations and rotations.