-
Miklos Racz
-
Princeton University
Correlated randomly growing graphs
Correlated random graph models have received much attention recently due to their relevance to various applications, for instance, understanding the graph matching problem in an average-case setting. In this talk, I will discuss models of correlated randomly growing graphs. I will focus on the fundamental statistical questions of detecting correlation and estimating aspects of the correlated structure. Our results highlight the influence of the seed graph in the underlying growth model and its connections with these detection and estimation questions. This is based on joint work with Anirudh Sridhar.
Zoom Link - https://princeton.zoom.us/j/91006932581?pwd=bjd6OGg3TXdGWXIybFB0Nk53TmdWQT09
Miklos Z. Racz is an assistant professor at Princeton University in the ORFE department, as well as an affiliated faculty member at the Center for Statistics and Machine Learning (CSML). Before coming to Princeton, he received his Ph.D. in Statistics from UC Berkeley and was then a postdoc in the Theory Group at Microsoft Research, Redmond. Miki’s research focuses on probability, statistics, and their applications. Miki's research and teaching have been recognized by Princeton's Howard B. Wentz, Jr. Junior Faculty Award, a Princeton SEAS Innovation Award, and an Excellence in Teaching Award.