This is a place to foster a growing community of computing educators around teaching in prison – for those who are interested in possibly doing this in the future, and those with plans or experience doing so. Higher education in prison (HEP) has expanded rapidly in the U.S. over the past several years after a policy change that re-instated pell grant eligibility to incarcerated adults. This follows a global shift toward more rehabilitative, as opposed to punitive, strategies toward criminal justice as more countries recognize the wide-ranging benefits to all members of society. However, computing education, as well as basic digital literacy, remain a challenge as various factors like technology infrastructure, perceived threats to security, and instructor willingness remain challenges to offering CS courses in prison education programs. Despite these barriers, there are several models of CS education happening in prison today including in-person and virtual instruction, for-credit college courses and informal workshops. In this session, we will talk about different ways of getting started with teaching in prison, as well as practical strategies for navigating challenges from our own personal experience of teaching CS in prison settings.

Incarcerated students enrolled in education programs in prisons and jails experience a range of benefits, from reduced recidivism to improved psychosocial well-being. With respect to computer science education, little is still known about how courses impact incarcerated students' experiences, though recent work has explored fears and confidence of incarcerated students enrolled in computer science courses. Our work investigates incarcerated students' changes in self-efficacy over multiple iterations of four different classes. Our findings showed that all subscales of computer programming self-efficacy (algorithm, control, cooperation, debugging, and logic), but not generalized self-efficacy, were statistically significantly increased at the end of the courses relative to the beginning (p < 0.001, n = 36). A similar pattern of results across the full sample (n = 188) adds additional support for the veracity of the effects found in the subset of paired data. Additionally, we share students' qualitative data to add nuance to our findings and emphasize the importance of these educational experiences for incarcerated students' personal and professional development.

Educational programs for incarcerated individuals, often called "behind bars" initiatives, have been shown to improve participants' social and economic outcomes upon release. Since its founding in 2018, MIT's Education Justice Institute (TEJI) has offered accredited classes for incarcerated students, with an increasing focus on computer education. Our courses have been delivered both in person and remotely (e.g., via Zoom). In this poster, we share insights into the challenges present in the incarcerated education environment, and highlight how remote learning offers unique advantages to incarcerated students. We also present preliminary findings from two years of data collected across four recurring computer science courses. This poster aims to foster a dialogue with the broader computer science education community, focusing on: (i) qualitative insights gained from extensive interactions with incarcerated education systems, (ii) preliminary empirical results obtained through IRB-approved surveys, (iii) common challenges faced during data collection, and (iv) an opportunity to seek feedback and pose questions to computer science education experts.

Self-efficacy and digital literacy are key predictors to incarcerated people’s success in the modern workplace. While digitization in correctional facilities is expanding, few templates exist for how to design computing curricula that foster self-efficacy and digital literacy in carceral environments. As a result, formerly incarcerated people face increasing social and professional exclusion post-release. We report on a 12-week college-accredited web design class, taught virtually and synchronously, across 5 correctional facilities across the United States. The program brought together men and women from gender-segregated facilities into one classroom to learn fundamentals in HTML, CSS and Javascript, and create websites addressing social issues of their choosing. We conducted surveys with participating students, using dichotomous and open-ended questions, and performed thematic and quantitative analyses of their responses that suggest students’ increased self-efficacy. Our study discusses key design choices, needs, and recommendations for furthering computing curricula that foster self-efficacy and digital literacy in carceral settings.

We present the first algorithm to efficiently compute certifiably optimal solutions to range-aided simultaneous localization and mapping (RA-SLAM) problems. Robotic navigation systems increasingly incorporate point-to-point ranging sensors, leading to state estimation problems in the form of RA-SLAM. However, the RA-SLAM problem is significantly more difficult to solve than traditional pose-graph SLAM: Ranging sensor models introduce nonconvexity and single range measurements do not uniquely determine the transform between the involved sensors. As a result, RA-SLAM inference is sensitive to initial estimates yet lacks reliable initialization techniques. Our approach, certifiably correct RA-SLAM (CORA), leverages a novel quadratically constrained quadratic programming formulation of RA-SLAM to relax the RA-SLAM problem to a semidefinite program (SDP). CORA solves the SDP efficiently using the Riemannian Staircase methodology; the SDP solution provides both: 1) a lower bound on the RA-SLAM problem's optimal value and 2) an approximate solution of the RA-SLAM problem, which can be subsequently refined using local optimization. CORA applies to problems with arbitrary pose-pose, pose-landmark, and ranging measurements and, due to using convex relaxation, is insensitive to initialization. We evaluate CORA on several real-world problems. In contrast to state-of-the-art approaches, CORA is able to obtain high-quality solutions on all problems despite being initialized with random values. In addition, we study the tightness of the SDP relaxation with respect to important problem parameters: The number of: 1) robots; 2) landmarks; and 3) range measurements. These experiments demonstrate that the SDP relaxation is often tight and reveal relationships between graph connectivity and the tightness of the SDP relaxation.

Inter-agent relative localization is critical for any multi-robot system operating in the absence of external positioning infrastructure or prior environmental knowledge. We propose a novel inter-agent relative 2D pose estimation system where each participating agent is equipped with several ultra-wideband (UWB) ranging tags. Prior work typically supplements noisy UWB range measurements with additional continuously transmitted data, such as odometry, making these approaches scale poorly with increased swarm size or decreased communication throughput. This approach addresses these concerns by using only locally collected UWB measurements with no additionally transmitted data. By modeling observed ranging biases and systematic antenna obstructions in our proposed optimization solution, our experimental results demonstrate an improved mean position error (while remaining competitive in other metrics) over a similar state-of-the-art approach that additionally relies on continuously transmitted odometry.

BACKGROUND: In phylogenetics, we often seek to reconcile gene trees with species trees within the framework of an evolutionary model. While the most popular models for eukaryotic species allow for only gene duplication and gene loss or only multispecies coalescence, recent work has combined these phenomena through a reconciliation structure, the labeled coalescent tree (LCT), that simultaneously describes the duplication-loss and coalescent history of a gene family. However, the LCT makes the simplifying assumption that only one individual is sampled per species whereas, with advances in gene sequencing, we now have access to multiple samples per species. RESULTS: We demonstrate that with these additional samples, there exist gene tree topologies that are impossible to reconcile with any species tree. In particular, the multiple samples enforce new constraints on the placement of duplications within a valid reconciliation. To model these constraints, we extend the LCT to a new structure, the partially labeled coalescent tree (PLCT) and demonstrate how to use the PLCT to evaluate the feasibility of a gene tree topology. We apply our algorithm to two clades of apes and flies to characterize possible sources of infeasibility. CONCLUSION: Going forward, we believe that this model represents a first step towards understanding reconciliations in duplication-loss-coalescence models with multiple samples per species.