Teaching

Courses

Courses where I served as teaching assistant at UC Berkeley

• [Fall 2023] STAT 201A: Introduction to Probability at an Advanced Level
• [Spring 2023] STAT 159/259: Collaborative and Reproducible Data Science
• [Spring 2022] STAT 159/259: Collaborative and Reproducible Data Science

I also intructed in the University of Buenos Aires

• [Fall 2017] Topics of Physics for Math majors

Developing STAT 159 at UC Berkeley

With Fernando Pérez and building on top of the contributions of past intructors of this course, we have developed a new curriculum for undergraduate and graduate students that focuses in good computational practices that encourage collaboration and reproducibility. The contents of STAT 159/259: Collaborative and Reproducible Data Science include

• Working with a team in GitHub
• Good programing practices
• Tools in the Jupyter ecosystem (Notebook, Lab, Binder, JupyterBook)
• Packaging and testing of scientific software in Python
• Working in sharable virtual environments
• Automation with GitHub actions and Make

The courses focuses in applicatins to climate sciences using Python, but most of these ideas are applicable to other domains and programming languages.

We believe there is a need in introducing these contents to students in data science programs. It is our expirience that students benefit from this course and they start applying the workflows they learned in their research projects and future works.

If you are interested in the contents of the course, you can check the course website where you can see notes and labs.

Workshops

I am a member of the organizing team and instructor of the Machine Learning in Glaciology workshop. There had been already two realizations of the workshop in 2022 and 2023 in the Finse research station in Norway. The goal of the workshop is to introduce machine learning tools to both students and reseachers working in glacier modelling and encourage new collaborations.