In 2016, we launched an interdisciplinary, undergraduate Data Analytics major that extends the definition of "interdisciplinary" beyond computer science, mathematics, and statistics to the natural and social sciences, humanities, and fine arts. Our program was conceived, and continues to be administered, as an independent academic unit by a Committee of faculty representing ten disciplines. Students majoring in Data Analytics complete four or more mathematics and computer science courses, four project-oriented Data Analytics courses, three to four courses in one of seven applied domains, and a required summer internship. Data Analytics courses are taught by both dedicated Data Analytics faculty and other faculty from the Committee. Partnerships with campus offices, alumni, businesses, and nonprofits have enhanced both coursework and internship opportunities. The major's popularity has exceeded our expectations, and has succeeded in attracting students with a variety of academic interests, many of whom would not have otherwise pursued a computational or quantitative major.

In this special session, we share a variety of ways in which CS 1 courses can broaden their focus beyond programming. These courses strive to effectively introduce the fundamental ideas of computer science, and the importance of computing in broader contexts, without sacrificing programming rigor.

MOTIVATION: Riboswitches are short sequences of messenger RNA that can change their structural conformation to regulate the expression of adjacent genes. Computational prediction of putative riboswitches can provide direction to molecular biologists studying riboswitch-mediated gene expression. RESULTS: The Denison Riboswitch Detector (DRD) is a new computational tool with a Web interface that can quickly identify putative riboswitches in DNA sequences on the scale of bacterial genomes. Riboswitch descriptions are easily modifiable and new ones are easily created. The underlying algorithm converts the problem to a 'heaviest path' problem on a multipartite graph, which is then solved using efficient dynamic programming. We show that DRD can achieve ∼ 88-99% sensitivity and >99.99% specificity on 13 riboswitch families. AVAILABILITY AND IMPLEMENTATION: DRD is available at http://drd.denison.edu.

We study how to route online splittable flows in bidirectional ring networks to minimize maximum load. We show that the competitive ratio of any deterministic online algorithm for this problem is at least , where is the size of the ring, and that the online algorithm that splits each flow inversely proportionally to the length of the flow's shortest path achieves this competitive ratio for all integers © 2010 Wiley Periodicals, Inc. NETWORKS, Vol. 57(2), 187–197 2011.

The Department of Mathematics and Computer Science at Denison University has introduced a significant new oral communication component early in both majors. The sophomore computer science and mathematics majors meet together each week for a "lab" taught jointly by a computer scientist and a mathematician. There were three goals in this endeavor: (1) to prepare students for the workforce and graduate school by improving their oral communication skills, (2) to nurture future researchers in both fields by exposing them to research early in their undergraduate training, and (3) to increase computer science students' exposure to mathematics. In the following, we establish the need for such a course, describe our approach, how it satisfies our three goals, and additional outcomes.

We analyze the competitive ratio of two greedy online algorithms for routing permanent virtual circuits in a network with arbitrary topology and uniform capacity links. We show that the competitive ratio of the first algorithm, with respect to network congestion, is in and where m is the number of links in the network, 𝒟 is the maximum ratio, over all requests, of the length of the longest path for the request to the length of the shortest path for the request, and ℒ︁ is the ratio of the maximum-to-minimum bandwidth requirement. We show that the competitive ratio of the second greedy algorithm is in Ω(d + log(n −d)) and min{O(d log n), when the optimal route assignment is pairwise edge disjoint, where n is the number of network nodes and d is the length of the longest path that can be assigned to a request. It is known that the optimal competitive ratio for this problem is Θ(log n). Aspnes et al. designed a Θ(log n) competitive online algorithm that computes an exponential function of current congestion to make each decision. The greedy online algorithms, although not optimal, make each decision more quickly and still have good competitive ratios in many nontrivial situations. © 1999 John Wiley & Sons, Inc. Networks 34: 136–153, 1999