Ani Sridhar

Postdoc @ MIT Mathematics


MIT 2-246A

I am a postdoctoral associate at the Mathematics Department at MIT, working with Elchanan Mossel. Previously, I completed my PhD at Princeton’s Department of Electrical and Computer Engineering, where I was advised by Miklós Z. Rácz and Vince Poor. My research uses tools from probability, statistics and graph theory to tackle fundamental challenges in network analysis, causal inference and epidemiology. Recent and ongoing research directions include:

  • Graph matching. How can structural similarities be found across networks, and what can we learn from the commonalities? My research characterizes the precise information-theoretic thresholds for the possibility or impossibility of this question and related ones, such as community detection or clustering.
  • Modeling and mitigation of network cascades. How does a cascading process – such as misinformation in social networks, malware in computer networks, or a virus in a human population – evolve over time? And how can we learn about the ongoing spread before such cascades cause too much damage to society at large? My research develops the underlying theory behind new aspects of cascade models (e.g., viral mutations, mask-wearing) and designs data-driven statistical methods for quickly learning key information about cascades.

For additional details, you can check out my CV or my Google Scholar page.


Nov 16, 2023 Paper on mean-field approximations for stochastic processes on networks published in the SIAM Journal on Control and Optimization (SICON). Joint work with Soummya Kar.
Oct 17, 2023 I will be giving a talk and chairing a session at the Informs 2023 conference on Community Detection in Networks.
Sep 19, 2023 New paper on the average-case and smoothed complexity of graph isomorphism. Joint work with Miki Rácz and Julia Gaudio.
Jun 25, 2023 Presented work on matching inhomogeneous random graphs and quickly tracking network cascades at ISIT 2023.
Jun 8, 2023 Paper on spreading processes with mutations on multi-layer networks published in the Proceedings of the National Academy of Sciences (PNAS). Joint work with Mansi Sood, Rashad Eletreby, Chai Wah Wu, Simon A. Levin, Osman Yagan and Vince Poor.

selected publications


  1. SICON
    Mean-field Approximations for Stochastic Population Processes with Heterogeneous Interactions
    Anirudh Sridhar, and Soummya Kar
    To appear in the SIAM Journal on Control and Optimization, Aug 2023
  2. PNAS
    Spreading Processes with Mutations over Multi-Layer Networks
    Mansi Sood, Anirudh Sridhar, Rashad Eletreby, Chai Wah Wu, Simon A. Levin, H. Vincent Poor, and Osman Yagan
    Proceedings of the National Academy of Sciences, Jun 2023
  3. IEEE-IT
    Quickest Inference of Network Cascades with Noisy Information
    Anirudh Sridhar, and H. Vincent Poor
    IEEE Transactions on Information Theory, Apr 2023


  1. COLT 2022
    Exact Community Recovery in Correlated Stochastic Block Models
    Julia Gaudio, Miklós Z. Rácz, and Anirudh Sridhar
    In Proceedings of the 35th Annual Conference on Learning Theory, Jul 2022