Rocco Servedio is a professor in the Department of Computer Science at Columbia University, where he is currently serving as department chair. He graduated from Harvard University in 2001, where he was advised by Leslie Valiant, with a PhD thesis on efficient algorithms in computational learning theory. Prior to joining Columbia, Servedio was an NSF postdoc at Harvard University. He is a recipient of the Alfred P. Sloan Research Fellowship, the NSF CAREER Award, and the Columbia University Presidential Teaching Award, and he has received best paper or best student paper awards from the STOC, FOCS, COLT and CCC conferences.
Servedio’s research interests lie in theoretical computer science. His particular interests include computational complexity theory (concrete complexity, pseudorandomness, and analysis of Boolean functions), computational learning theory (learnability of Boolean functions and probability distributions), and sublinear time algorithms (property testing) and the connections among these and related topics.