College students rely on increasingly data-rich environments when making learning-relevant decisions about the courses they take and their expected time commitments. However, we know little about how their exposure to such data may influence student course choice, effort regulation, and performance. We conducted a large-scale field experiment in which all the undergraduates at a large, selective university were randomized to an encouragement to use a course-planning web application that integrates information from official transcripts from the past fifteen years with detailed end-of-course evaluation surveys. We found that use of the platform lowered students' GPA by 0.28 standard deviations on average. In a subsequent field experiment, we varied access to information about course grades and time commitment on the platform and found that access to grade information in particular lowered students' overall GPA. Our exploratory analysis suggests these effects are not due to changes in the portfolio of courses that students choose, but rather by changes to their behavior within courses.

Many recent algorithms for approximate model counting are based on a reduction to combinatorial searches over random subsets of the space defined by parity or XOR constraints. Long parity constraints (involving many variables) provide strong theoretical guarantees but are computationally difficult. Short parity constraints are easier to solve but have weaker statistical properties. It is currently not known how long these parity constraints need to be. We close the gap by providing matching necessary and sufficient conditions on the required asymptotic length of the parity constraints. Further, we provide a new family of lower bounds and the first non-trivial upper bounds on the model count that are valid for arbitrarily short XORs. We empirically demonstrate the effectiveness of these bounds on model counting benchmarks and in a Satisfiability Modulo Theory (SMT) application motivated by the analysis of contingency tables in statistics.

The field of computer science suffers from a lack of diversity. The Stanford Artificial Intelligence Laboratory's Outreach Summer (SAILORS), a two-week non-residential free summer program, recruits high school girls to computer science, specifically to Artificial Intelligence (AI). The program was organized by graduate student and professor volunteers. The goals of the pilot program are to increase interest in AI, contextualize technically rigorous AI concepts through societal impact, and address barriers that could discourage 10th grade girls from pursuing computer science. In this paper we describe the curriculum designed to achieve these goals. Survey results show students had a statistically significant increase in technical knowledge, interest in pursuing careers in AI, and confidence in succeeding in AI and computer science. Additionally, survey results show that the majority of the students found new role models, faculty support, and a sense of community in AI and computer science.

We show that asymptotically, completely asynchronous stochastic gradient procedures achieve optimal (even to constant factors) convergence rates for the solution of convex optimization problems under nearly the same conditions required for asymptotic optimality of standard stochastic gradient procedures. Roughly, the noise inherent to the stochastic approximation scheme dominates any noise from asynchrony. We also give empirical evidence demonstrating the strong performance of asynchronous, parallel stochastic optimization schemes, demonstrating that the robustness inherent to stochastic approximation problems allows substantially faster parallel and asynchronous solution methods. In short, we show that for many stochastic approximation problems, as Freddie Mercury sings in Queen's \emph{Bohemian Rhapsody}, ``Nothing really matters.''

In this poster we present a CS curriculum aimed at "middle-years" students. In the U.S., this corresponds roughly to students aged 11-15. This MyCS curriculum provides a hands-on introduction to computer science through six distinct modules. Three programming modules use the Scratch environment to build computational sophistication and procedural intuition within an inviting development environment. Alternating with Scratch are three problem-solving modules whose activities deepen awareness and facility with computation through novel exercises. More than 25 classrooms and 900 students have participated in MyCS's pilot years within the Pomona, CA and Lihue, HI school districts. 2013-15 will see an expansion of its workshops, academic-year classes, and program evaluation. Course materials will be served by the OpenEdX platform both at HMC and on Amazon EC2. This poster shares our development and deployment of MyCS to date, and invites feedback from other institutions in their efforts to foster adolescents' computational identities.