معرفی
Rocco Servedio is a Professor in the Department of Computer Science at Columbia University, where he leads research in theoretical computer science with a focus on computational complexity theory, learning theory, and the role of randomness in computation. He previously served as Chair of the Computer Science Department from 2018 to 2021. He holds a Ph.D., MS, and AB in Mathematics from Harvard University.
His research interests include property testing, computational learning theory, and algorithmic lower bounds. He has contributed to foundational work in areas like junta testing, trace reconstruction, and convexity testing. Servedio has held leadership roles in major conferences such as STOC, CCC, and COLT, and has mentored students through courses like Unconditional Lower Bounds and Derandomization.
- Education:
- Ph.D. in Computer Science, Harvard University
- MS in Computer Science, Harvard University
- AB in Mathematics, Harvard University
His research bridges theoretical computer science and applied mathematics, with recent work exploring the intersection of Gaussian processes, convex geometry, and algorithmic efficiency. Servedio's contributions to the field are exemplified through his involvement in high-impact conferences and his leadership in advancing fundamental computational theories.
