Miklos Z Racz

Miklos Z Racz is an Assistant Professor of Statistics and Data Science and Computer Science at the Department of Statistics and Data Science, Northwestern University, within the Weinberg College of Arts & Sciences. He holds a PhD in Statistics from UC Berkeley (2015). His research focuses on the intersection of probability, statistics, computer science, and information theory, with an emphasis on combinatorial statistics, discrete probability, and applied probability. He studies statistical inference on large random discrete structures such as random graphs, including topics like inferring latent geometry in networks, community detection, and dynamics on networks. He also explores applications in social networks, voting systems, sequence reconstruction, and DNA data storage.

His educational background includes a PhD in Statistics from UC Berkeley (2015), reflecting expertise in theoretical and applied statistical methods. His work often bridges theoretical foundations with practical challenges in data science and network analysis.

Racz's research interests span a broad range of topics, including the analysis of random graphs, community detection, graph matching, and the development of algorithms for high-dimensional data. His contributions to DNA data storage and sequence reconstruction highlight his interdisciplinary approach to solving real-world problems through statistical and computational methods.

His recent publications explore themes such as graph isomorphism analysis, efficient polymer synthesis, and reducing polarization in social networks. These works demonstrate a focus on advancing both foundational theory and applied technologies. He has not explicitly mentioned any scientific awards, but his prolific publication record reflects significant scholarly activity.

While no specific grants or advising roles are detailed in the provided text, his research on DNA data storage and network dynamics suggests involvement in cutting-edge projects at the intersection of computer science and biotechnology. His work contributes to advancing methodologies for handling complex data structures and improving computational efficiency in network analysis.