Miklos Z Racz

About

Miklos Z. Racz is a professor whose research focuses on statistics, with a particular emphasis on the analysis of socioeconomic systems, random graphs, and network inference. His academic background includes advising PhD students in fields such as Operations Research and Financial Engineering at Princeton University, where he has contributed to the understanding of collective good, optimization, and the geometry of random graphs. Racz has also mentored numerous students and postdoctoral researchers across top institutions, demonstrating a strong commitment to advancing research in statistical inference, network analysis, and applied mathematics.

Research topics

• Computer Science
• Discrete mathematics
• Artificial Intelligence
• Mathematics
• Computer Security
• Physics
• Algorithm
• Mathematical economics
• Geometry
• Combinatorics

Selected publications

• Matching Correlated Inhomogeneous Random Graphs using the k-core Estimator

  2023-06-25 · 8 citations

  article1st authorCorresponding

  We consider the task of estimating the latent vertex correspondence between two edge-correlated random graphs with generic, inhomogeneous structure. We study the so-called k-core estimator, which outputs a vertex correspondence that induces a large, common subgraph of both graphs which has minimum degree at least k. We derive sufficient conditions under which the k-core estimator exactly or partially recovers the latent vertex correspondence. Finally, we specialize our general framework to deriv…

  PublisherDOI

• Exact Community Recovery in Correlated Stochastic Block Models

  arXiv (Cornell University) · 2022-03-29 · 3 citations

  preprintOpen access

  We consider the problem of learning latent community structure from multiple correlated networks. We study edge-correlated stochastic block models with two balanced communities, focusing on the regime where the average degree is logarithmic in the number of vertices. Our main result derives the precise information-theoretic threshold for exact community recovery using multiple correlated graphs. This threshold captures the interplay between the community recovery and graph matching tasks. In par…

  PublisherOA PDFDOI

• Towards Consensus: Reducing Polarization by Perturbing Social Networks

  arXiv (Cornell University) · 2022-06-17 · 3 citations

  preprintOpen access1st authorCorresponding

  This paper studies how a centralized planner can modify the structure of a social or information network to reduce polarization. First, polarization is found to be highly dependent on degree and structural properties of the network -- including the well-known isoperimetric number (i.e., Cheeger constant). We then formulate the planner's problem under full information, and motivate disagreement-seeking and coordinate descent heuristics. A novel setting for the planner in which the population's in…

  PublisherOA PDFDOI

• Efficient Graph Matching for Correlated Stochastic Block Models

  2024-01-01 · 2 citations

  articleSenior author
  PublisherDOI

• Efficient Graph Matching for Correlated Stochastic Block Models

  arXiv (Cornell University) · 2024-12-03 · 1 citations

  preprintOpen accessSenior author

  We study learning problems on correlated stochastic block models with two balanced communities. Our main result gives the first efficient algorithm for graph matching in this setting. In the most interesting regime where the average degree is logarithmic in the number of vertices, this algorithm correctly matches all but a vanishing fraction of vertices with high probability, whenever the edge correlation parameter $s$ satisfies $s^2 > α\approx 0.338$, where $α$ is Otter's tree-counting const…

  PublisherOA PDFDOI

Recent grants

• Dynamic Networks: Probabilistic Models and Inference Problems

  NSF · $180k · 2018–2021

Frequent coauthors

• Elchanan Mossel

  31 shared

• Sébastien Bubeck

  18 shared

• Cyrus Rashtchian

  15 shared

• M. Kun

  Research Centre for Astronomy and Earth Sciences

  13 shared

• Ronen Eldan

  10 shared

• Á. Kóspál

  Eötvös Loránd University

  10 shared

• Anirudh Sridhar

  8 shared

• Yuval Peres

  Beijing Institute of Mathematical Sciences and Applications

  8 shared

Labs

• Miklos Z. Racz LabPI

  Not provided

Education

• Ph.D., Statistics

  University of Chicago

  1996

• M.S., Statistics

  University of Chicago

  1993

• B.S., Mathematics

  University of Chicago

  1991

Similar researchers at Northwestern University

• Elizabeth Tiptonh-33
• Thomas Severinih-23
• Kaize Dingh-22