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Scalable causal discovery
Continuous-optimization formulations of structure learning that replace combinatorial search with differentiable acyclicity constraints, enabling GPU-accelerated batching across very high-dimensional graphs.
Research
My research lives at the intersection of causal inference, probabilistic graphical models, and continuous optimization. The unifying question: how do we recover causal graphs that remain trustworthy when data are scarce, partially observed, or drawn from a different distribution than the one we’ll deploy on?
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Continuous-optimization formulations of structure learning that replace combinatorial search with differentiable acyclicity constraints, enabling GPU-accelerated batching across very high-dimensional graphs.
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Designing intervention selection policies that maximize identifiability gain per experiment — lowering the cost of causal discovery in regimes where interventions are expensive or risky.
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Studying when and how causal graphs can be recovered with finite samples, latent confounders, and distribution shift between training and deployment — the conditions under which structure can be trusted.
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Earlier work on SER-optimized multi-level ASK modulations and energy/sign-based receivers for reconfigurable intelligent surface (RIS)–assisted wireless systems, with closed-form error analysis.
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Peer-reviewed work in causal inference and wireless communications. For the most up-to-date list see my Google Scholar profile.
ICASSP 2026
Accepted
C. Peng, S. Mishra, U. Mitra
Proposes a learning-based framework for selecting soft interventions that improves causal-discovery efficiency and reduces experimental cost.
IEEE Transactions on Green Communications and Networking
Vol. 10, pp. 1433–1445, 2026
S. Mishra, S. P. Dash, G. C. Alexandropoulos
Investigates one- and two-sided ASK modulations in noncoherent SISO systems assisted by an RIS, proposing novel energy- and sign-based receiver structures.
doi: 10.1109/TGCN.2025.3633182IEEE Wireless Communications Letters
Vol. 15, pp. 300–304, 2026
S. Mishra, S. P. Dash
Considers RIS-aided wireless communication with one-sided ASK and an optimal noncoherent maximum-likelihood detection rule.
doi: 10.1109/LWC.2025.3624154§ 03
Electronic System Design Lab, IIT Bhubaneswar
Collaborate
I’m always glad to chat about causal discovery, identifiability, or scalable structure-learning methods.
sambitmi@usc.edu