Neuroendoscopy is a minimally invasive surgical approach with numerous benefits, but it presents technical challenges such as spatial constraints and the need for precise, repetitive movements. While surgical robots have the potential to address these challenges, their adoption in neuroendoscopy remains limited due to inadequate user interfaces and reliance on overly specific preprogrammed commands. This work introduces a robot learning algorithm that enables surgeons to kinesthetically train a robot arm to navigate and operate a neuroendoscope. Surgeons can manually guide the robot arm through task demonstrations, make real-time corrections, and teach the robot preferred surgical approaches, all within the surgical workspace constraints to ensure patient and surgeon safety. We aim to develop this machine learning-driven approach to create an intuitive shared control system between the surgeon and the robot, potentially reducing task completion times and enabling adaptation to anatomical variations.

Despite living in an increasingly connected world, social isolation is a prevalent issue today. While social robots have been explored as tools to enhance social connection through companionship, their potential as asynchronous social platforms for fostering connection towards humanity has received less attention. In this work, we introduce the design of a social support companion that facilitates the exchange of emotionally relevant stories and scaffolds reflection to enhance feelings of connection via five design dimensions. We investigate how social robots can serve as “social proxies” facilitating human stories, passing stories from other human narrators to the user. To this end, we conduct a real-world deployment of 40 robot stations in users' homes over the course of two weeks. Through thematic analysis of user interviews, we find that social proxy robots can foster connection towards other people's experiences via mechanisms such as identifying connections across stories or offering diverse perspectives. We present design guidelines from our study insights on the use of social robot systems that serve as social platforms to enhance human empathy and connection.

How do we design mentoring programs that effectively support CS learners? This workshop draws upon our expertise in mentoring and inclusive design as well as our experience in the academic and industry spaces to help participants maximize the impact of a mentoring program. Participants will work with field-tested open-source toolkits which share modules rooted in effective and inclusive pedagogy.

The MaGE Training curriculum prepares computer science students for the task of inclusive peer mentoring and teaching. The curriculum raises awareness of the role of social identity in learning, emphasizes active learning within computer science, and provides preparation for technical code review. This article presents an overview of the MaGE Training curriculum where it has been used to train six cohorts of near-peer mentors and its impact on more than 500 students at a liberal arts college. While rapid growth in course enrollments has presented many challenges, results suggest that the MaGE curriculum has helped to address some of these challenges by maintaining high quality feedback to, and close interaction with, introductory students. Effectiveness is evidenced through increases in mentor self-efficacy, positive impact on student belongingness and continued enrollment, and reports of buffering the instructor workload. The flexibility of the curriculum is supported through a set of modules that can be engaged with via in-person discussions or viewed remotely. This enables easier adoption of the curriculum for use at other institutions.

The Megas and Gigas Educate (MaGE) program is a peer mentoring program being developed at Mount Holyoke College, a liberal arts college for women, for the introductory CS curriculum. Consistent with national trends, interest in CS is rising rapidly; current resources cannot meet demand while maintaining quality feedback and pedagogy. Supported by a Google Computer Science Capacity Award, MaGE has three main objectives: (1) to triple enrollment capacity over 3 years in introductory courses; (2) to increase enrollment and retention for women and other underrepresented groups; (3) to train students to educate, mentor, and support others in inclusive ways. Trained undergraduate students act as peer mentors to beginner students, providing close interaction and assisting with feedback. MaGE is currently being piloted in the introductory CS1 course. Enrolled students bring varying interests, including Art, History, Biochemistry, Economics and Engineering; most students have no prior programming experience. Each CS1 student is assigned a peer Giga Education Mentor (or GEM) in a 9:1 ratio. GEMs have undergone a rigorous training course that raises awareness of the role of social identity in learning, emphasizes active learning within computer science, and provides preparation for being technical peer mentors. While research supports the need for culturally-sensitive, inclusive training as part of the curriculum, we know of few peer-based models in CS that explicitly include this education. By building a diverse set of peer role models and connecting with the pre-existing co-curricular Megas and Gigas mentoring program, MaGE seeks to effectively engage underrepresented students in computing.

BACKGROUND: A protein's function is determined by the wide range of motions exhibited by its 3D structure. However, current experimental techniques are not able to reliably provide the level of detail required for elucidating the exact mechanisms of protein motion essential for effective drug screening and design. Computational tools are instrumental in the study of the underlying structure-function relationship. We focus on a special type of proteins called "hinge proteins" which exhibit a motion that can be interpreted as a rotation of one domain relative to another. RESULTS: This work proposes a computational approach that uses the geometric structure of a single conformation to predict the feasible motions of the protein and is founded in recent work from rigidity theory, an area of mathematics that studies flexibility properties of general structures. Given a single conformational state, our analysis predicts a relative axis of motion between two specified domains. We analyze a dataset of 19 structures known to exhibit this hinge-like behavior. For 15, the predicted axis is consistent with a motion to a second, known conformation. We present a detailed case study for three proteins whose dynamics have been well-studied in the literature: calmodulin, the LAO binding protein and the Bence-Jones protein. CONCLUSIONS: Our results show that incorporating rigidity-theoretic analyses can lead to effective computational methods for understanding hinge motions in macromolecules. This initial investigation is the first step towards a new tool for probing the structure-dynamics relationship in proteins.

How many pair-wise distances must be prescribed between an unknown set of points, and how should they be distributed, to determine only a discrete set of possible solutions? These questions, and related generalizations, are central in a variety of applications. Combinatorial rigidity shows that in two-dimensions one can get the answer, generically, via an efficiently testable sparse graph property.