Miklos Z. Racz
Assistant Professor ORFE
Associated Faculty CSML
Princeton University

204 Sherrerd Hall
Princeton, NJ 08544
(609) 258-8281
mracz at princeton dot edu

[ CV | Google Scholar profile ]

Brief bio

I am an Assistant Professor in the ORFE Department at Princeton University. I am also an associated faculty member at the Center for Statistics and Machine Learning (CSML). My research focuses on probability, statistics, and their applications. I co-organize the Probability Seminar and the Wilks Memorial Seminar in Statistics at Princeton.

Before coming to Princeton I spent two years as a postdoc in the Theory Group at Microsoft Research, Redmond. I received my PhD in Statistics from UC Berkeley in 2015, where I was advised by Elchanan Mossel. I also obtained an MS in Computer Science from Berkeley. Previously, I received an MS in Mathematics from the Budapest University of Technology and Economics, under the supervision of Marton Balazs and Balint Toth.

Research interests

My research interests lie broadly at the interface of probability, statistics, information theory, and computer science. My work focuses on large random discrete structures, and my goal is to understand the interplay between underlying structural properties and statistical inference questions. I am also interested in applications, in particular understanding the spread of information in social networks, and sequence reconstruction problems in computational biology.

My research in statistical network analysis focuses on statistical inference questions in random graph models. These include inferring the past in randomly growing graphs, inferring latent geometry in high-dimensional random geometric graphs, and graph matching problems in correlated random graphs.

I also work on statistical error correction algorithms for DNA storage, part of a larger group effort with Microsoft and University of Washington researchers to make DNA storage a reality. We recently stored --- and then successfully retrieved --- a record 200 MB of data in DNA; a proof of concept for this exciting emerging technology.


In Spring 2022 I am teaching ORF 387: Networks, an undergraduate course that showcases how networks are widespread in society, technology, and nature, via a mix of theory and applications.


One of the best parts of being an academic is the opportunity to interact with and to mentor talented students. It is a pleasure to currently advise:

(PhD) Daniel Rigobon, Anirudh Sridhar
(Senior thesis) Grant Lu, Karena Yan

For a list of former students and more, see the Students tab.