Making the methodologies and results of computational projects transparent and accessible is an ever-present challenge, but computational notebooks have revolutionized this work, allowing one to seamlessly combine code, visualizations, and documentation in a single interactive environment. 

This workshop will provide you with a comprehensive understanding of how to leverage the full potential of MATLAB and Python using two incredible options for computational notebooks: MATLAB Live Scripts and Jupyter Notebooks. With practical exercises, you will have the opportunity to use the MATLAB kernel in Jupyter, as well as to practice some basic MATLAB and Python workflows, allowing you to enhance your analysis, modeling, and data visualization workflows. 

Bring your laptop and don't forget to also have your MathWorks account and GitHub account ready for this workshop!

Registration is required.  

Presenters

María Elena Gavilán

Gavilán is a Technical Program Manager at MathWorks, supporting researchers and educators in engineering and science. Given her technical expertise with several engineering tools and languages like C++, Python and MATLAB, Maria supports projects that seek to increase the use of MATLAB alongside Open Source in research projects, particularly in applications involving AI and physical modeling.  María has extensive industry experience in numerical simulation projects (CFD and FEA) in the automotive and aerospace industries. María holds a BSc in Physics from the National University of Colombia, a MSc in Aeronautics and Astronautics from Purdue, and an MBA from UIUC.

Jon Loftin 

Loftin is a Customer Success Engineer at MathWorks Jon’s background is in mathematics. More specifically, implementing mathematics in a computer. He holds degrees in mathematics: a BS from Southern Arkansas University, a MS from the University of Arkansas, and a Ph.D. from Texas Tech University. He has had years of teaching experience, from teaching at the Naval Nuclear Power School to teaching as an Assistant Professor. Jon’s research focus is building efficient integration techniques in finite element methods.