Time and Date: 16:30 - 18:10 on 13th June 2018
Chair: Angela Shiflet
|43|| Resource for Undergraduate Research Projects in Mathematical and Computational Biology [abstract]
Abstract: A substantial hurdle faced by undergraduate mathematics faculty and students wishing to embark on collaborative research projects is not knowing quite where to begin. This is true of any field in mathematics, including the more applied areas of mathematical and computational biology, which is the focus of a new volume of the FURM (Foundations for Undergraduate Research in Mathematics) book series published by Birkhauser. Topics that might afford productive inroads for new student researchers are often not obvious, even to experts, and finding unanswered questions that are well-suited to student projects is time-consuming. The volume, which is the topic of this talk, aims to reduce the challenges in starting faculty-student collaborations by presenting self-contained, undergraduate-accessible articles, each of which provides directions for new research in mathematical and computational biology, enough background to get started, and recommendations for further reading. The content spans the breadth of mathematical and computational biology, including many topics that are appropriate for student-faculty exploration and are not normally addressed by the undergraduate curriculum (Hidden Markov Models, e.g.). Each article in this collection has been written with an eye toward generating new research collaborations between undergraduates and faculty. As such, each article presents background material sufficient for preparing readers to tackle specific open problems, which the material also includes. Moreover, authors have carefully cultivated lists of references intended to launch productive ongoing investigations for readers wanting to delve more deeply into a given field. The intended audience is broad: undergraduate mathematics faculty, with a particular emphasis on faculty interested in (but not necessarily experienced in) mathematical and computational biology, and students with sophomore-to junior-level coursework as a background. Undergraduate faculty wishing to direct research will benefit from the many project ideas suggested by the authors, as will faculty simply wishing to expand their own research repertoire in a new direction. Undergraduate mathematics students will appreciate the accessible, yet rigorous treatment of topics previously relegated to the graduate curriculum, some of these with fairly minimal prerequisite assumptions (e.g., calculus and linear algebra). The primary intended audience is undergraduate students, typically in STEM majors, with an interest in pursuing undergraduate research. A secondary audience is applied mathematics and computer science faculty interested in mentoring undergraduate research but unsure of how to get started. A further potential source of interest is among math faculty who are interested in learning a new area of mathematics. This talk will present information about the volume and some of the included material, such as the following: “Using Neural Networks to Identify Bird Species from Birdsong Samples,” “Using Regularized Singularities to Model Stokes Flow: A Study of Fluid Dynamics Induced by Metachronal Ciliary Waves,” “Network Structure and Stochastic Dynamics of Biological Systems,” “Simulating Bacterial Growth, Competition, and Resistance with Agent-Based Models and Laboratory Experiments,” “Phase Sensitivity in Ecological Oscillators,” “What Are the Chances? – Hidden Markov Models,” and “A Tour of the Basic Reproductive Number and the Next Generation of Researchers.”
|Hannah Highlander, Carrie Eaton, Alex Capaldi, Angela Shiflet and George Shiflet|
|151|| Numerical Analysis project in ODEs for undergraduate students [abstract]
Abstract: Designing good projects involving programming in numerical analysis for large groups of students with different backgrounds is a challenging task. The assignment has to be manageable for the average student, but to additionally inspire the better students it is preferable that it has some depth and leads to them to think about the subject. We describe a project that was assigned to the students of an introductory Numerical Analysis course at the University of Iceland. The assignment is to numerically compute the length of solution trajectories of a system of ordinary differential equations with a stable equilibrium point. While not difficult to do, the results are somewhat surprising and got the better students to get interested in what was happening. We describe the project, its solution using Matlab, and the underlying mathematics in some detail.
|514|| Computational Modeling at Rose-Hulman Institute of Technology [abstract]
Abstract: In 2008 Rose-Hulman Institute of Technology in Terre Haute, Indiana, USA introduced a new Computational Science minor built around two key junior-level courses: Introduction to Computational Science and a follow-on, Computational Modeling. The latter course adopted an innovative approach to teaching students to work effectively in teams to develop, implement, test, and refine nontrivial computational models and simulations in a lab-like setting, then use them to investigate a scientific phenomenon, with an emphasis on models based primarily around systems of ordinary differential equations solved in Matlab, but also including models that were discrete, stochastic, and so on. Students teams write several reports during the term, spending one to two weeks per project. This course sequence has led to a textbook by two of the faculty involved in the program (to be published in Spring 2019) which covers material for both courses. In 2012 the minor was expanded to an undergraduate major in Computational Science that requires additional coursework including a full course in parallel computing, separate courses in analytical and numerical (finite differences or finite elements, at the student’s choice) partial differential equations, and other areas. But the Computational Modeling course—popular enough that it is frequently offered off-schedule as an independent study for small groups of students—also served as the inspiration for the more recent Bioinformatics course that is part of the new undergraduate Biomathematics major, which also requires students to take the Introduction to Computational Science course. This course uses a similar approach but with a narrower focus; less emphasis is placed on learning to create and utilize simulations in teams, but lengthier and more detailed reports are expected. Again, there is a strong focus on performing computational science, that is, on using these models to perform computational experiments and to address scientific questions in a detailed and convincing manner. Meanwhile, an even more recent Data Science minor—expected to be expanded to a major in the near future—incorporates aspects of these same courses, and new courses continue to be created along these lines; for example, I will be offering a trial Computational Data Science course for the first time during Spring quarter of 2019. A prototype second course in computational modeling, with the main course as a prerequisite, will be given a soft trial the same term. In this talk I will briefly discuss the structure of the curricula of these majors and minors, emphasizing the Computational Science major and minor, then focus on how computational modeling skills are developed in Rose-Hulman’s Computational Science and Biomathematics students via the Computational Modeling and Bioinformatics courses: Philosophy, syllabi, materials, effective use of classroom time in a lab-like atmosphere, and so on.
|102|| Growing an inclusive scientific computing community at Boise State University [abstract]
Abstract: In this work we describe the results of campus-wide efforts to grow a campus computing community over a four-year period, strongly leveraging The Carpentries pedagogy. We discuss (1) Development of a required introductory programming course within a materials science curriculum, (2) Impact of regular Software Carpentry training events, and (3) Outcomes of an interdisciplinary Vertically Integrated Projects course entitled "Computing Across Campus" aimed at supporting student researchers. We find that pedagogical approaches focused on lowering cognitive load are effective for efficiently training competent computational science practitioners as evidenced by student research outputs (posters, papers, talks, allocations, awards). We also find that creating computing demand from students requires strategic infrastructure planning and describe obstacles, challenges, and solutions that arose over this four-year period.