In 2001, Khoury College of Computer Sciences at Northeastern University created their first combined majors with Cognitive Psychology, Mathematics and Physics. This type of degree has often been referred to as "CS+X" in the literature and is increasingly relevant as the need for interdisciplinary computer scientists grows. As of 2021, students at Northeastern can choose among three computing majors (Computer Science, Data Science or Cybersecurity) and 42 combined majors, which combine one of the three computing degrees with one of 29 distinct majors in other fields. Prior to 2014, combined majors were with the sciences, business and design. Over the last seven years, we created 29 new combined majors, explicitly creating combinations with fields where there has traditionally been greater gender diversity. The resulting increase in student interest and gender diversity over the last seven years is compelling. As of Fall 2020, 44.6% of the 2,800+ computing majors at Northeastern are pursuing combined majors, 39% of whom are women. This is substantially higher than the 21.5% reported in IPEDS for 2019 women computing graduates in the U.S. We did not observe any significant differences in racial and ethnic diversity between combined and computing only degrees. In this experience paper, we describe how we create and manage combined majors, and we present results on enrollments, admissions, graduation, internship placements, and how students discover combined majors.

We have created, piloted and are growing the Align program, a Master of Science in Computer Science (MS in CS) for post-secondary graduates who did not major in CS. Our goal is to create a pathway to CS for all students, with particular attention to women and underrepresented minorities. Indeed, women represent 57% and underrepresented minorities represent 25% of all bachelor's recipients in the U.S., but only 19.5% and 12.6% of CS graduates, respectively. If we can fill this opportunity gap, we will satisfy a major economic need and address an issue of social equity and inclusion. In this paper, we present our "Bridge'' curriculum, which is a two-semester preparation for students to then join the traditional MS in CS students in master's-level classes. We describe co-curricular activities designed to help students succeed in the program. We present our empirical findings around enrollment, demographics, retention and job outcomes. Among our findings is that Align students outperform our traditional MS in CS students in grade point average. To date we have graduated 137 students and 827 are enrolled.

In their "Generation: CS" report this past year, The Computing Research Association detailed the explosive growth of computer science majors across both large and small institutions calling it "larger today than any time previously". Using data from The Taulbee Survey they show a 500% increase in the number of majors in computer science, but only a 50% increase in full-time faculty. Unfortunately, this growth has major consequences outside the classroom setting. The ability for faculty to engage in meaningful advising relationships with students has been severely impacted by this record growth. One solution could be to hire staff to assist students in course selection and degree requirements, but meaningful mentorship is much more than checking boxes and exemptions. Can we be effective advisors when advising loads have skyrocketed? In 2004, The CRA, lists effective mentoring as one of the challenges faced by broadening participation in computer science. In particular, they advocate for better training for mentors. Now faced with extreme ratios the need for creative mentoring practices is paramount. This BOF will provide a platform to discuss and share solutions from a variety of institutions. Questions such as: "How can we identify students who would like to do research and continue to graduate school?", "How do we expose students to independent learning in large classes?", "Can we leverage peer mentoring?" "How can we make advising better?" "What processes can be automated?".

Recent work introduced Generalized First Order Decision Diagrams (GFODD) as a knowledge representation that is useful in mechanizing decision theoretic planning in relational domains. GFODDs generalize function-free first order logic and include numerical values and numerical generalizations of existential and universal quantification. Previous work presented heuristic inference algorithms for GFODDs. In this paper, we study the complexity of the evaluation problem, the satiability problem, and the equivalence problem for GFODDs under the assumption that the size of the intended model is given with the problem, a restriction that guarantees decidability. Our results provide a complete characterization. The same characterization applies to the corresponding restriction of problems in first order logic, giving an interesting new avenue for efficient inference when the number of objects is bounded. Our results show that for Σk formulas, and for corresponding GFODDs, evaluation and satisfiability are Σkp complete, and equivalence is Πk+1p complete. For Πk formulas evaluation is Πkp complete, satisfiability is one level higher and is Σk+1p complete, and equivalence is Πk+1p complete.

MOTIVATION: It has long been hypothesized that incorporating models of network noise as well as edge directions and known pathway information into the representation of protein-protein interaction (PPI) networks might improve their utility for functional inference. However, a simple way to do this has not been obvious. We find that diffusion state distance (DSD), our recent diffusion-based metric for measuring dissimilarity in PPI networks, has natural extensions that incorporate confidence, directions and can even express coherent pathways by calculating DSD on an augmented graph. RESULTS: We define three incremental versions of DSD which we term cDSD, caDSD and capDSD, where the capDSD matrix incorporates confidence, known directed edges, and pathways into the measure of how similar each pair of nodes is according to the structure of the PPI network. We test four popular function prediction methods (majority vote, weighted majority vote, multi-way cut and functional flow) using these different matrices on the Baker's yeast PPI network in cross-validation. The best performing method is weighted majority vote using capDSD. We then test the performance of our augmented DSD methods on an integrated heterogeneous set of protein association edges from the STRING database. The superior performance of capDSD in this context confirms that treating the pathways as probabilistic units is more powerful than simply incorporating pathway edges independently into the network. AVAILABILITY: All source code for calculating the confidences, for extracting pathway information from KEGG XML files, and for calculating the cDSD, caDSD and capDSD matrices are available from http://dsd.cs.tufts.edu/capdsd

BACKGROUND: New technology has resulted in high-throughput screens for pairwise genetic interactions in yeast and other model organisms. For each pair in a collection of non-essential genes, an epistasis score is obtained, representing how much sicker (or healthier) the double-knockout organism will be compared to what would be expected from the sickness of the component single knockouts. Recent algorithmic work has identified graph-theoretic patterns in this data that can indicate functional modules, and even sets of genes that may occur in compensatory pathways, such as a BPM-type schema first introduced by Kelley and Ideker. However, to date, any algorithms for finding such patterns in the data were implemented internally, with no software being made publically available. RESULTS: Genecentric is a new package that implements a parallelized version of the Leiserson et al. algorithm (J Comput Biol 18:1399-1409, 2011) for generating generalized BPMs from high-throughput genetic interaction data. Given a matrix of weighted epistasis values for a set of double knock-outs, Genecentric returns a list of generalized BPMs that may represent compensatory pathways. Genecentric also has an extension, GenecentricGO, to query FuncAssociate (Bioinformatics 25:3043-3044, 2009) to retrieve GO enrichment statistics on generated BPMs. Python is the only dependency, and our web site provides working examples and documentation. CONCLUSION: We find that Genecentric can be used to find coherent functional and perhaps compensatory gene sets from high throughput genetic interaction data. Genecentric is made freely available for download under the GPLv2 from http://bcb.cs.tufts.edu/genecentric.

MOTIVATION: With the growing availability of high-throughput protein-protein interaction (PPI) data, it has become possible to consider how a protein's local or global network characteristics predict its function. RESULTS: We introduce a graph-theoretic approach that identifies key regulatory proteins in an organism by analyzing proteins' local PPI network structure. We apply the method to the yeast genome and describe several properties of the resulting set of regulatory hubs. Finally, we demonstrate how the identified hubs and putative target gene sets can be used to identify causative, functional regulators of differential gene expression linked to human disease. AVAILABILITY: Code is available at http://bcb.cs.tufts.edu/hubcomps. CONTACT: [email protected]; [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

We study properties of non-uniform reductions and related completeness notions. We strengthen several results of Hitchcock and Pavan (ICALP (1), Lecture Notes in Computer Science, vol. 4051, pp. 465–476, Springer, 2006 ) and give a trade-off between the amount of advice needed for a reduction and its honesty on NEXP. We construct an oracle relative to which this trade-off is optimal. We show, in a more systematic study of non-uniform reductions, among other things that non-uniformity can be removed at the cost of more queries. In line with Post’s program for complexity theory (Buhrman and Torenvliet in Bulletin of the EATCS 85, pp. 41–51, 2005 ) we connect such ‘uniformization’ properties to the separation of complexity classes.