Abstract: [No abstract available]

Abstract: Obtaining meaningful computational science project assignments for students is always a challenge in a variety of courses from modeling and simulation to high performance computing to many other mathematics and computer science subjects. Often, the instructor desires projects that involve interesting applications, are challenging enough for teams of students while not being too difficult, have a suitable time commitment, are new, are instructive, and cover appropriate concepts. The Shiflets coauthored Introduction to Computational Science: Modeling and Simulation for the Sciences, 2nd edition (Princeton U. Press, 2014), the only textbook of its kind designed specifically for an introductory course in the computational science and engineering curriculum. The textbook, which a variety of courses on the undergraduate and graduate level use, includes numerous such projects. Additional projects are now publically available on the textbook’s website (https://ics.wofford-ecs.org/additional-projects) for anyone to use; and instructors can obtain solutions to a selection of the assignments from the authors. The problems involve a number of important computational science concepts and approaches, such as system dynamics modeling, empirical modeling, random walks, cellular automaton simulations, sensitivity analysis, agent-based simulations, age- and stage-structured modeling, Markov chains, and matrix modeling of social networks. Applications include invasive species, rumor spreading, juvenile delinquency, fur patterns, condor populations, rice viruses, toxin-producing micro-organisms, coral bleaching, brown bears, neuron signals, and succession. The talk will discuss some of these applications and approaches and present representative models and simulations to solve the problems.

Abstract: The Institute for Advanced Computational Science (IACS) is Stony Brook University’s flagship organization for teaching and promoting computational and data science inside and outside of campus. It is an interdisciplinary organization that brings together faculty and students from applied mathematics, statistics, physics, chemistry, marine and atmospheric sciences, engineering, ecology and evolution, sociology, linguistics, music, medicine, and many others. We currently offer a Certificate in Data & Computing for Scientists and Engineers and a NSF-sponsored training program (STRIDE: Science Training and Research to Inform DEcisions) for doctoral students. Masters-level and undergraduate programs are being developed. This poster overviews and summarizes the pedagogical strategies for computational science education at IACS and Stony Brook University. We focus on training domain experts who are able and skilled at using high-performance computational resources to generate, analyze, and interpret data to solve research problems. We stress interdisciplinary communication and work closely with the Alan Alda Center for Communicating Science to help our researchers bridge these gulfs. Other IACS initiatives, such as computational science “bootcamps” for medical researchers, primary school teachers, and high-school students will also be discussed.

Abstract: We are midway through the development of writing four textbooks and four open-content computer lab manuals with supporting materials for a new upper-division undergraduate curriculum in Applied and Computational Mathematics that will modernize the mathematics major and better integrate it with the broader STEM community. The new curriculum is being piloted at Brigham Young University as a new degree program. The new curriculum brings mathematical analysis, algorithm design, optimization, data science and mathematical modeling into the forefront of interdisciplinary study in the pure and applied sciences. Scientific computing is taught in the context of big data and high performance computing. The textbooks are being published by the Society of Industrial and Applied Mathematics (SIAM), and both the lab manuals and supporting materials will be made freely available online. The project creates a modern undergraduate curriculum which provides the foundation that students need to become world-class problem solvers and interdisciplinary innovators. This should increase student interest and participation in applied and computational mathematics, and expand the pipeline of young scholars in the mathematical sciences who are well equipped to face the challenges of the 21st Century and become leaders in the globally competitive STEM workforce.

Abstract: Over the past decade, a variety of free computational modeling tools have become available for use in secondary school and introductory college courses. These tools provide an excellent introduction to computation for students who have yet to develop skill at or interest in programming. Once exposed to these tools through modeling projects, students have reported that they understand the value of computation in solving problems, and also the limitations of the tools -- which highlights the need to delve further into computational techniques. In this session, an overview of several such tools will be provided, along with examples of modeling and simulation projects that have been used mathematics courses at the secondary and college level. The tools that have been used successfully with students at these levels include InsightMaker (web-based systems and agent modeling); VensimPLE (systems modeling); NetLogo (agent modeling); Gephi (network and graph analysis); WolfraAlpha (web-based symbolic computation); DataFlyer and Desmos (web-based graphing and data fitting).

Abstract: We will present our experiences in teaching science courses with computational perspective. In 2011, when our efforts have started, the only available solution which would provide web based mathematical experimentation environment for students was SageMath notebook (sagenb). We have decided to provide at our faculty for all students a central installation of sagenb and to prepare teaching materials for most of courses. Now, the system is being displaced by a modern Jupyter notebook which provided SageMath kernel but also pure scientific Python ecosystem. In this talk we present benefits and technical challenges of creating such infrastructure both for universities as well as for schools.