When a groundbreaking computational science course adapted its curriculum to tackle the Covid-19 pandemic this spring, enrollment skyrocketed from just 20 participants to nearly 300 eager learners, demonstrating unprecedented demand for AI-driven solutions to global health challenges.
Introduction to Computational Thinking (6.S083/18.S190) leverages cutting-edge data science, artificial intelligence algorithms, and sophisticated mathematical models using the innovative Julia programming language developed at MIT. Initially launched as a pilot half-semester class in the fall, this course represents a cornerstone of the MIT Stephen A. Schwarzman College of Computing's computational thinking initiative, spearheaded by Mathematics Professor Alan Edelman and Visiting Professor David P. Sanders. The instructors rapidly redesigned the curriculum to focus on practical applications for Covid-19 response strategies, with students enthusiastically embracing this timely pivot.
"Everyone at MIT is driven to contribute during this crisis," explains Edelman. "While our Julia Lab focuses on developing computational tools for scientists, Dave and I recognized the importance of equipping students with fundamental skills in computational approaches to drug development, epidemiological modeling, and pandemic response."
Offered through MIT's Department of Electronic Engineering and Computer Science in collaboration with the Department of Mathematics, this course unlocks unprecedented opportunities to harness computational power in understanding and combating the Covid-19 pandemic. "This initiative opens pathways for applying advanced computing techniques to address one of the most pressing global health challenges of our time," notes Daniela Rus, Director of MIT Computer Science and Artificial Intelligence Laboratory.
The fall iteration of this course capped enrollment at 20 students, but the spring version attracted nearly 300 participants within a single weekend, predominantly from the MIT community. "The response has been absolutely phenomenal," Edelman remarks. "This surge in interest placed unprecedented demands on MIT's registration systems, exceeding all our expectations."
Shinjini Ghosh, a sophomore studying computer science and linguistics, initially enrolled to master the Julia programming language but quickly recognized the broader significance. "I was motivated to develop computational modeling skills essential for researching Covid-19 transmission patterns and evaluating potential containment strategies," she explains.
"The proliferation of misinformation regarding coronavirus epidemiology and statistical modeling has been concerning," adds Raj Movva, a sophomore majoring in computer science and biology. "This course provides crucial clarity on these complex topics while offering hands-on experience in developing predictive models for pandemic trajectories."
Edelman has long envisioned an interdisciplinary educational experience that seamlessly integrates machine learning and AI from our data-driven world with the advanced computational capabilities enabled by Julia, alongside physical models, differential equations, and scientific machine learning approaches representing our physical reality.
He describes this course as "a natural evolution of Julia Lab's research endeavors and the collaborative open-source Julia community's collective innovations." For years, this global online community has collaborated to develop tools accelerating drug approval processes, enhancing scientific machine learning and differential equation solving, and predicting infectious disease transmission patterns. "True to MIT's tradition of open education, all course materials are freely accessible to learners worldwide," Edelman emphasizes.
When MIT transitioned to virtual learning to reduce campus density, adapting this course for online instruction proved relatively straightforward for Edelman and Sanders, who were already prepared for digital delivery.
"Despite our experience with remote learning formats, nothing quite compares to the energy of a live classroom setting," Edelman reflects. "However, MIT students continue to ask insightful questions through Zoom chat, maintaining the course's intellectual vibrancy and stimulating academic discourse."
Sanders, a Marcos Moshinsky research fellow currently on leave from his professorship at the National University of Mexico, specializes in techniques for accelerating global optimization. Since joining the Julia Lab in 2014, Sanders has collaborated with Edelman on various teaching, research, and outreach initiatives related to Julia. His YouTube tutorials have amassed over 100,000 views, with Edelman noting that "his video series is widely regarded as the premier resource for learning the Julia programming language."
Edelman also receives assistance from an unexpected teaching assistant—Philip, his family's Corgi, who previously roamed MIT's halls and classrooms. "Philip has become something of a Julia celebrity, with his image frequently processed by Julia's AI systems for machine learning demonstrations," Edelman shares. "Students always brighten up when Philip makes appearances during our online sessions."
