Property testing algorithms seek to determine whether an unknown massive object has some particular property of interest, or is "far" from having the property, while inspecting only a tiny portion of the object. Recent years have witnessed significant progress on both classic property testing problems and the development of several new property testing problems and frameworks, motivated by connections to machine learning theory and high-dimensional data analysis. In this talk, Rocco Servedio will survey several of these new property testing problems, models, and results.

Rocco Servedio received his undergraduate degree in mathematics at Harvard and his PhD in computer science at Harvard, where his thesis was advised by Leslie Valiant. Servedio is a professor of computer science at Columbia University, where he has received a Presidential Teaching Award and served as department chair. His research interests within theoretical computer science include computational learning theory, property testing, computational complexity theory, lower bounds, pseudorandomness, and the study of randomness in computing. He has served as PC chair of conferences and workshops including STOC, CCC, COLT, and RANDOM, and has received best paper and best student paper awards from STOC, FOCS, COLT, ALT, and CCC.


The Richard M. Karp Distinguished Lectures were created in Fall 2019 to celebrate the role of Simons Institute Founding Director Dick Karp in establishing the field of theoretical computer science, formulating its central problems, and contributing stunning results in the areas of computational complexity and algorithms. Formerly known as the Simons Institute Open Lectures, the series features visionary leaders in the field of theoretical computer science and is geared toward a broad scientific audience.

Light refreshments will be available at 3 p.m., prior to the start of the lecture. 

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If you require special accommodation, please contact our access coordinator at simonsevents [at] with as much advance notice as possible.