I am currently a graduate student pursuing my PhD in the Electrical and Computer Engineering department at Princeton University. I am advised by Miklós Rácz and H. Vincent Poor. Before starting grad school, I completed my Bachelor’s degree in Electrical and Computer Engineering (ECE) at Carnegie Mellon University, where I worked with Jeremiah Blocki and Soummya Kar.

Broadly, my research interests are in network science, applied probability and statistics. My current work focuses on inference in random graphs and network cascades.

For more details, check out my CV.

Recent news:

  • (Dec 2021) I received the Yan Huo *94 Graduate Fellowship in Electrical Engineering. [Press release]
  • (Dec 2021) I gave a spotlight presentation talk about my work on graph matching and community recovery in correlated Stochastic Block Models at NeurIPS 2021.
  • (Summer 2021) I was an intern with the Machine Learning University team at Amazon, where I taught a course on Natural Language Processing to Amazon employees (mainly software developers) and also designed a course on Graphical Models.
  • (July 2021) I presented my work on modeling mutations and mask-wearing in viral spread on networks at the Networks 2021 conference.
  • (June 2021) Two of my papers were presented at ICASSP. One was on the effect of viral mutations in network epidemics, with implications for the COVID-19 pandemic. The other derived optimal algorithms for tracking a cascade under noisy observations.

Awards and Honors:

  • Yan Huo *94 Fellowship in Electrical Engineering from Princeton, 2022. [Press release]
  • Spotlight presentation (top 3% of submissions), NeurIPS 2021.
  • Interdisciplinary Fellowship from Princeton’s ECE Department, Spring 2021.
  • Finalist for the INFORMS-APS Best Student Paper Award, 2020.

Selected Papers:

  1. M. Z. Rácz, A. Sridhar. Correlated Stochastic Block Models: Exact Graph Matching with Applications to Recovering Communities. In the Proceedings of NeurIPS 2021 (spotlight presentation), July 2021. [Paper link]
  2. O. Yagan, A. Sridhar, R. Eletreby, S. A. Levin, J. B. Plotkin, H. V. Poor. Modeling and Analysis of the Spread of COVID-19 Under a Multiple-Strain Model with Mutations. Harvard Data Science Review, April 2021. [Paper link]
  3. M. Z. Rácz, A. Sridhar. Correlated Randomly Growing Graphs. To appear in the Annals of Applied Probability, 2022+. [arXiv link]

Last updated: March 2022