My name is Alexander Goscinski, I am currently pursuing a PhD which is reaching its end at the École Polytechnique Fédérale de Lausanne (EPFL) in Materials Science and Engineering in the Laboratory of Computational Science and Modeling. My research focuses on studying features of machine learning models used for the prediction of atomistic properties. This allows us to better understand the internals of these kinds of models and thereby inspiring new developments. I am passionate about developing software that helps researchers to push the boundaries of materials science research. In my free time, I enjoy tennis and running outdoors. In addition to my research, I am also skilled in programming languages such as Python and C and am interested in diving more into Rust and Scala. I have experience managing high-performance computing systems and have contributed to several open-source software projects in the field of computational materials science. I am always looking for opportunities to collaborate with others and learn from their experiences.
We introduce scicode-widgets, a Python package designed for educational purposes that facilitates the creation of interactive exercises for students in interdisciplinary fields of computational sciences. The implementation of computational experiments often demands extensive coding, hindering students' ability to effectively learn the interwork of coding experiments and analyzing their results. To reduce this workload for students, instructors can already provide a codebase and demand educational contributions from students. These contributions have to be embedded into a general workflow that involves coding experiments and analyzing their results. For that task, scicode-widgets provides the tools to connect custom pre- and post-processing of students’ code, with the ability to verify the solution and to pass it to interactive visualizations driven by Jupyter widgets.