Interdisciplinary learning is a reality in the future of any student pursuing applied mathematics and statistics. Whether in academia or industry, researchers must rely on their communication skills collaborating with experts from other fields. To better prepare students academically for their futures, I believe that group work, experimental and case study learning are essential teaching methods to include in the classroom. As a supplement to lectures, non-traditional classroom experiences deepen students’ understanding, allowing them to apply theoretical concepts to problems which require more than the repetition of a method.
DAPPER is a package for learning the implementation, and benchmarking the performance, of data assimilation (DA) methods. DAPPER reproduces numerical results (benchmarks) reported in the literature, and facilitates comparative studies, thus promoting the reliability and relevance of the results. DAPPER is open source, written in Python, and focuses on readability; this promotes the reproduction and dissemination of the underlying science, and makes it easy to adapt and extend. I am an active contributer of the DAPPER library.
This repository is to document and share my teaching workflow. This workflow has been designed to meet as best as possible ADA web accessibility requirements for science and mathematics. Particularly, the templates included provide examples of my work in creating slides and handouts with rich mathematics that can be read by screen readers.