I am currently a Schmidt Science Fellow at MIT Laboratory for Information and Decision Systems hosted by Devavrat Shah. My research focuses on foundations for reliable learning and inference in complex systems.

My doctoral thesis received the A.G. Jordan Award at CMU ('25). I was named a Schmidt Science Fellow ('24), among 32 outstanding early-career scientists worldwide, and an EECS Rising Star (MIT, '21; Georgia Tech, '23). My work has received recognition across communication networks (Best Paper Award, IEEE ICC, '21), ecology and epidemiology (Proceedings of the National Academy of Sciences, '23), information theory (Graduation Day Award, ITA Workshop, '24), mathematics of networks (Invited Lecturer, International Centre for Theoretical Sciences, '26), cybersecurity (CyLab Presidential Fellowship, '20, '23), societal systems (Knight Fellowship, '20–'23), and operations research (Student Spotlight, Stochastic Networks & Applied Probability Seminar, '23).

My work is motivated by operational demands in emerging computational paradigms, such as learning efficiently from limited, distributed, or dependent data, and by societal-scale challenges, such as evaluating policy interventions during epidemics. An underlying theme of my work is understanding how structural heterogeneity, modeled through graphs, shapes system performance, as reflected in emergent phenomena and the efficiency of learning algorithms, and how this understanding can be leveraged to design interventions that improve system performance.

Note: I am on the academic job market! Research statement available by request, please reach out to msood@mit.edu if you think my research is a good fit for your department.

Salient Publications

PhD Dissertation

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]

Learning and Intervening under Latent Heterogeneity

M. Sood, D. Shah, Causal Inference with Unobserved Confounding: a Mixture Learning Perspective, IEEE Signal Processing Magazine: Special Issue on Methodological Advances in Causal Inference (under final review), 2026.
M. Sood, D. Shah, Learning Mixtures of Exponential Family, IEEE International Symposium on Information Theory, 2026.

Modeling and Mitigating Spreading Phenomena

M. Sood, H. Gu, R. Eletreby, S. Kumar, C. W. Wu, O. Yagan, On the Interplay of Clustering and Evolution in the Emergence of Epidemic Outbreaks, IEEE/ACM Transactions on Networking, 2026.
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.

Random Graph Foundations for Distributed Inference

M. Sood, E. C. Elumar, O. Yagan, On Balancing Sparsity with Reliable Connectivity in Distributed Network Design with Random K-out Graphs, 2025, preliminary results in IEEE International Conference on Communications (ICC) 2021. [Best Paper Award]
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, On the Minimum Node Degree and k-connectivity in Inhomogeneous Random K-out Graphs, IEEE Transactions on Information Theory 2021.