Rocco Servedio

Rocco Servedio is a Professor in the Department of Computer Science at Columbia University.

He earned his PhD from Harvard University under the supervision of Leslie Valiant, with a thesis on efficient algorithms in computational learning theory. Prior to joining Columbia, he was an NSF postdoctoral fellow at Harvard University.

His research spans computational complexity theory, computational learning theory, property testing, analysis of Boolean functions, and randomness in computing. He has made significant contributions to the theoretical foundations of computer science, particularly in understanding the power and limitations of efficient algorithms for learning and testing. His work bridges theoretical computer science with practical applications in algorithm design and analysis.

Professor Servedio has been recognized with numerous awards including:

• Alfred P. Sloan Research Fellowship
• NSF CAREER Award
• Columbia University Presidential Teaching Award
• Best paper or best student paper awards from STOC, FOCS, COLT, and CCC conferences

His research has been supported by grants such as the NSF CAREER award, and he has served as a visiting scientist for programs including Sublinear Algorithms (Summer 2024), Analysis and TCS: New Frontiers (Summer 2023), and Real Analysis in Computer Science (Fall 2013). These appointments reflect his active engagement with the broader theoretical computer science community and ongoing contributions to cutting-edge research initiatives.