Mansi Sood

Research

I am excited about tackling fundamental problems at the interface of stochastic modeling, algorithm design, and inference, particularly in the context of networked socio-technical systems that have an increasing grip on our everyday lives.

M. Sood, Structural Heterogeneity and Performance in Stochastic Networks: From Distributed Inference to Epidemics and Beyond, PhD Thesis, Carnegie Mellon University, Aug, 2024. [A. G. Jordan Award for Outstanding Thesis] [ITA Graduation Day Award]

Selected

M. Sood, D. Shah, Learning Mixtures of Exponential Family, IEEE International Symposium on Information Theory, 2026.
M. Sood, D. Shah, Causal Inference with Unobserved Confounding: a Mixture Learning Perspective, IEEE Signal Processing Magazine: Methodological Advances in Causal Inference (to appear), 2026.
M. Sood, R. Eletreby, S. Kumar, C. W. Wu, O. Yagan, On the Interplay of Clustering and Evolution in the Emergence of Epidemic Outbreaks, under review with IEEE Transactions on Networking 2026.
M. Sood, E. C. Elumar, O. Yagan, On Balancing Sparsity with Reliable Connectivity in Distributed Network Design with Random K-out Graphs, 2025.
M. Sood, A. Sridhar, R. Eletreby, C. W. Wu, S. A. Levin, H.V. Poor, O. Yagan, Spreading Processes with Mutations over Multi-layer Networks, Proceedings of the National Academy of Sciences, 2023.
M. Sood, O. Yagan, Existence and Size of the Giant Component in Inhomogeneous Random K-out Graphs, IEEE Transactions on Information Theory 2023.
M. Sood, O. Yagan, Tight Bounds for the Probability of Connectivity in Random K-out Graphs, IEEE International Conference on Communications (ICC) 2021. [Best Paper Award]
M. Sood, O. Yagan, On the Minimum Node Degree and k-connectivity in Inhomogeneous Random K-out Graphs, IEEE Transactions on Information Theory 2021.

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