Born in Hefei, China, Sitan Chen was one year old when his family immigrated to Canada so his father could complete his doctorate at the University of Toronto. They moved to Suwanee, Georgia, in the early 2000s, and Sitan's experiences throughout middle and high school with math contests and programs like the Research Science Institute ultimately motivated him to study mathematics and computer science at Harvard University.
Among his mentors in college, he regards Salil Vadhan and Leslie Valiant, two professors in Harvard's Theory of Computing research group, as the ones who inspired him to pursue graduate studies in theoretical computer science. Sitan graduated summa cum laude in 2016, receiving the Thomas T. Hoopes and Captain Jonathan Fay Prizes for his thesis on geometric aspects of counting complexity and arithmetic complexity.
Starting in 2016, Sitan worked at MIT with his PhD advisor Ankur Moitra on algorithmic problems in machine learning and inference, with a focus on developing new mathematical frameworks to analyze techniques like the method of moments, Gibbs sampling, and local search that are popular in practice but poorly understood in theory. Sitan has presented his work at venues including the Symposium on Theory of Computing and the Simons Institute for the Theory of Computing.