Soummya Kar

Professor

Carnegie Mellon University

United States

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About

Professor Soummya Kar is a faculty member at Carnegie Mellon University in the United States. His research focuses on various aspects of operations research, including quantum algorithms, multiagent systems, and optimal policy design. He has contributed to the fields of computational imaging and privacy-preserving consensus mechanisms through his recent publications.

Articles (18)

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2025

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2022

Collaborators (8)

Jose Moura

Phil L and Marsha Dowd University Professor

Carnegie Mellon University

UNITED STATES

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Aswin Sankaranarayanan

Assistant Professor

Carnegie Mellon University

UNITED STATES

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Stefan Vlaski

Lecturer

Imperial College London

UNITED KINGDOM

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Antonio Pedro Aguiar

Professor

Universidade do Porto Faculdade de Engenharia

PORTUGAL

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João Paulo Fernandes

Professor Associado

University of Porto

PORTUGAL

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Gabriel Falcao

Assistant Professor

University of Coimbra

PORTUGAL

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Bruno Sinopoli

Washington University in St. Louis

UNITED STATES

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Ali Sayed

Professor and Dean of Engineering

École Polytechnique Fédérale de Lausanne

SWITZERLAND

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Soummya Kar

Research Interests

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About

Articles (18)

Computational Imaging for Long-Term Prediction of Solar Irradiance

A Systematic Mapping Study on Quantum and Quantum-inspired Algorithms in Operations Research

Model-Free Learning and Optimal Policy Design in Multiagent MDPs Under Probabilistic Agent Dropout

Distributed Center-Based Clustering: A Unified Framework

Clustered Control of Transition-Independent MDPs

Privacy-Preserving Average Consensus Through Network Augmentation

A One-Shot Framework for Distributed Clustered Learning in Heterogeneous Environments

NONLINEAR CONSENSUS plus INNOVATIONS UNDER CORRELATED HEAVY-TAILED NOISES: MEAN SQUARE CONVERGENCE RATE AND ASYMPTOTICS

Networked Signal and Information Processing: Learning by multiagent systems

DISTRIBUTED RECURSIVE ESTIMATION UNDER HEAVY-TAIL COMMUNICATION NOISE*

Large Deviations for Products of Non-Identically Distributed Network Matrices With Applications to Communication-Efficient Distributed Learning and Inference

NONLINEAR GRADIENT MAPPINGS AND STOCHASTIC OPTIMIZATION: A GENERAL FRAMEWORK WITH APPLICATIONS TO HEAVY-TAIL NOISE

Secure Distributed Optimization Under Gradient Attacks

A Fast Randomized Incremental Gradient Method for Decentralized Nonconvex Optimization

Distributed Gradient Flow: Nonsmoothness, Nonconvexity, and Saddle Point Evasion

Cascading verification initiated by switching attacks through compromised digital relays

FAST DECENTRALIZED NONCONVEX FINITE-SUM OPTIMIZATION WITH RECURSIVE VARIANCE REDUCTION

Matcha: A Matching-Based Link Scheduling Strategy to Speed up Distributed Optimization

Collaborators (8)

Jose Moura

Aswin Sankaranarayanan

Stefan Vlaski

Antonio Pedro Aguiar

João Paulo Fernandes

Gabriel Falcao

Bruno Sinopoli

Ali Sayed

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