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3Blue1Brown

Math YouTube channel

  • 3 min read
  • 4 chapters

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Through Convolutional neural network, Digital image processing and Data and information visualization

Grant Sanderson

Early life and education

Sanderson graduated from Stanford University in 2015 with a bachelor's degree in mathematics. He worked for Khan Academy from 2015 to 2016 as part of their content fellowship program, producing videos and articles about multivariable calculus, after which he started focusing his full attention on 3Blue1Brown.

Career

In 2020 Grant Sanderson became one of the creators and lecturers of the MIT course Introduction to Computational Thinking, together with Alan Edelman, David Sanders, James Schloss, and Benoit Forget. The course uses the Julia programming language and Grant Sanderson's animations to explain various topics: convolutions, image processing, COVID-19 data visualization, epidemic modelling, ray tracing, introduction to climate modelling, ocean modelling, and the algorithms that lie behind these topics.

In January 2020 Sanderson delivered a talk in An Evening with Grant Sanderson, hosted by the Stanford Speakers Bureau. Sanderson offered his perspective on engaging with math: instead of prioritizing usefulness, he emphasizes emotion, wonder and imagination. He aims to "bring life to math" with visuals, graphics, and animations. In August 2021, Sanderson was one of several featured speakers at SIGGRAPH 2021.

In February 2022 Sanderson determined that the best starting word in the game Wordle was CRANE using information theory. Later, he stated that the code he wrote to determine the best starting word had a bug in it, and the actual best starting word that gives the lowest average score is SALET.

In November 2022 Sanderson delivered a keynote speech, "What can algorithms teach us about education?", at the 17th Dutch National Informatics Congress CelerIT hosted by Stichting Nationaal Informatica Congres (SNiC). Sanderson offered his perspective on how mathematics education should evolve in the future and related his findings with the way neural networks learn, he emphasizes the need for students to grasp concepts and understand them.

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