Computational thinking class enables students to engage in Covid-19 response

Left to right: Department of Mathematics Professor Alan Edelman,
his co-instructor and family Corgi Philip, and visiting professor
and longtime Julia lab collaborator David Sanders have altered
their computational thinking course to encourage input on COVID-19
responses.

Nearly 300 students join an open course that applies data
science, artificial intelligence, and mathematical modeling using
the Julia language to study Covid-19.

When an introductory computational science class, which is open
to the general public, was repurposed to study the Covid-19
pandemic this spring, the instructors saw student registration rise
from 20 students to nearly 300.

Introduction to
Computational Thinking (6.S083/18.S190), which applies data
science, artificial intelligence, and mathematical models using the
Julia programming language developed at MIT, was introduced in the
fall as a pilot half-semester class. It was launched as part of the
MIT Stephen A. Schwarzman College of Computing’s computational
thinking program and spearheaded by Department of Mathematics
Professor Alan
Edelman and Visiting Professor David
P. Sanders. They very quickly were able to fast-track the
curriculum to focus on applications to Covid-19 responses; students
were equally fast in jumping on board.

“Everyone at MIT wants to contribute,” says Edelman.
“While we at the Julia
Lab are doing research in building tools for scientists, Dave
and I thought it would be valuable to teach the students about some
of the fundamentals related to computation for drug development,
disease models, and such.”