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(Valpo) in Indiana. He is housed in the Mathematics and Statistics Department with an affiliate appointment to the Computing and Information Sciences Department. He's run the Masters in Analytics and Modeling program since 2014, and is the founding director of Valpo's Bachelor's in Science in Data Science. Karl specializes in data science as applied to networks and graphs. He's done work with applying network algorithms to improve genome assembly and published fundamental work in understanding K-Dense graphs. He's also very interested in finding ways to connect data science with social good, especially through the classroom and experiential learning. Karl's teaching includes Optimization, Data Mining, Multivariable Calculus and Differential Equations. He's also designed and implemented an Introduction to Data Science course targeted at students with minimal programming experience that centers around a data-driven service learning project.

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In the past several years, the problem of genome assembly has received considerable attention from both biologists and computer scientists. An important component of current assembly methods is the scaffolding process. This process involves building ordered and oriented linear collections of contigs (continuous overlapping sequence reads) called scaffolds and relies on the use of mate pair data. A mate pair is a set of two reads that are sequenced from the ends of a single fragment of DNA, and therefore have opposite mutual orientations. When two reads of a mate-pair are placed into two different contigs, one can infer the mutual orientation of these contigs. While several orientation algorithms exist as part of assembly programs, all encounter challenges while solving the orientation problem due to errors from mis-assemblies in contigs or errors in read placements. In this paper we present an algorithm based on hierarchical clustering that independently solves the orientation problem and is robust to errors. We show that our algorithm can correctly solve the orientation problem for both faux (generated) assembly data and real assembly data for R. sphaeroides bacteria. We demonstrate that our algorithm is stable to both changes in the initial orientations as well as noise in the data, making it advantageous compared to traditional approaches.

<p style='text-indent:20px;'>During the emergence of Data Science as a distinct discipline, discussions of what exactly constitutes Data Science have been a source of contention, with no clear resolution. These disagreements have been exacerbated by the lack of a clear single disciplinary 'parent.' Many early efforts at defining curricula and courses exist, with the EDISON Project's Data Science Framework (EDISON-DSF) from the European Union being the most complete. The EDISON-DSF includes both a Data Science Body of Knowledge (DS-BoK) and Competency Framework (CF-DS). This paper takes a critical look at how EDISON's CF-DS compares to recent work and other published curricular or course materials. We identify areas of strong agreement and disagreement with the framework. Results from the literature analysis provide strong insights into what topics the broader community see as belonging in (or not in) Data Science, both at curricular and course levels. This analysis can provide important guidance for groups working to formalize the discipline and any college or university looking to build their own undergraduate Data Science degree or programs.</p>