Abstract: Prof. Angela Shiflet in computer science and mathematics and Prof. George Shiflet in biology are Fulbright Specialists. In January, 2015, they participated in a three-week collaborative project at University “Magna Græcia” of Catanzaro in Italy, in the Department of Medical and Surgical Sciences, hosted by Prof. Mario Cannataro. While there, the three along with Prof. Pietro Hiram Guzzi started a project to develop educational module(s) on one or more high-performance-computing bioinformatics algorithms. Drs. Cannataro and Guzzi have written a book, Data Management of Protein Interaction Networks (Wiley, 2011), and regularly teach bioinformatics and HPC. Upon returning to the United States, the Drs. Shiflet applied to have undergraduate Daniel Couch be a Blue Waters Intern for one year working on the project. The NSF-funded Blue Waters Project, which provides a stipend for the intern, supports “experiences involving the application of high-performance computing to problems in the sciences, engineering, or mathematics” (http://computationalscience.org/bwsip/). Besides having had an HPC course, the student participated in a two-week workshop at the National Center for Supercomputing Applications (NCSA) facilities on the University of Illinois Urbana-Champaign campus. In the project, he has written sequential and HPC programs and performed timings to accompany an educational module on “Aligning SequencesSequentially and Concurrently,” available at http://www.wofford.edu/ecs/, and is working with the professors on developing other modules and programs. After covering the necessary biological background, the named module develops the sequential Needleman-Wunsch Algorithm (NWA) to determine the similarity and the alignment(s) that yield a highest similarity score. Employing timings developed by the intern, the module illustrates that the algorithm’s runtime is proportional to the square of the number of nucleotides. Having motivated the need for HPC, the module discusses HPC pipeline versions of NWA along with timings. To aid students, the module contains fifteen Quick Review Questions, many with multiple parts; nine exercises; and five projects. Completed sequential and parallel C with MPI programs are available upon request by instructors. The materials are current being used by students and faculty members in a bioinformatics course at University “Magna Græcia” of Catanzaro.

Abstract: This paper presents an experience of an introductory course on Parallel Programming. The course is dedicated to parallel programming tools and environments, and in particular to the analysis, development and optimization of parallel algorithms. It has a practical orientation and is guided with the use of problems from the Spanish Parallel Programming Contest. The different units are presented in the traditional lecture format, and a practical session accompanies each unit, with problems to work with in the tools or algorithmic paradigms presented in the previous lecture. The students work in the practical sessions on problems and using the system of the contest, which facilitates online and real time validation of their implementations. The practical approach of the course and the continuous evaluation used led to an important increase in the marks.

Abstract: In this talk we discuss designing, implementing, and researching an Introduction to Computational Modeling course for university undergraduates. The course is part of the brand-new Computational Mathematics, Science, and Engineering (CMSE) department at Michigan State University (MSU). It was specifically designed to be interdisciplinary; the course is open to any major at the university. The course was also built to address the growing need for a workforce that can analyze, model, and interpret real-world data. Our talk will cover three strands of developing the course: 1. Curriculum Design: how we worked backwards from the professional disciplinary practices of modeling to arrive at well-defined learning outcomes, assessments, and course content. 2. Instructional Environment: The key decisions we made and technologies we chose to bring the experience of modeling to the classroom. 3. Educational Research: How we’re using methods from the learning sciences—including clinical interviews and ethnographic classroom observation—to understand students’ experiences in the course and continually integrate findings into the course design

Abstract: Each scientific project or publication can be attributed to several fields of study with different degrees. Call interdisciplinary distribution the set of the degrees. If we have fixed number of fields of study, the set can be written in vector form. Each component of the vector corresponds to one of the fields of study. Call the vector the interdisciplinary vector. If we consider a scientist to be a set of his or her publications we can get the vector for a scientist as a weighted sum of a vector of his or her publications. This paper is devoted to an approach to the evaluation of the interdisciplinary distribution of professional or research objects (RO), and the transdisciplinary effects of their changes. RO and professionals can be evaluated on the basis of keywords in relevant scientific papers, reports, surveys, proposals, CVs, and so on. The transdisciplinary effect is apparent when the interdisciplinarity distribution has been changed. We propose formulas to evaluate this transdisciplinary effect. This approach was implemented using participants in group projects at the fourth Young Scientists Conference (YSC) on High-Performance Computing and Computer Simulation. The accuracy of the interdisciplinary vector of several participants was examined by the survey about their involvement in the team projects. This approach can be used to evaluate the compliance of a scientific team with the transdisciplinary research project (problem), as well as to assess the students' skills in transdisciplinary environments.