What is he best known for?
The fields of study he is best known for:
- Statistics
- Algorithm
- Artificial intelligence
His scientific interests lie mostly in Distributed algorithm, Mathematical optimization, Distributed computing, Algorithm and Stochastic approximation.
The Distributed algorithm study combines topics in areas such as Gossip protocol, Wireless sensor network, Inference, Constrained optimization and Economic dispatch.
His Mathematical optimization research includes elements of Estimation theory, Algorithm design, Estimator, Rate of convergence and Observability.
His Distributed computing study incorporates themes from Control engineering, Network topology, Information theory and Distributed generation.
His research investigates the connection with Algorithm and areas like Stochastic process which intersect with concerns in Markov process, Random dynamical system, Invariant measure and Vector quantization.
His Stochastic approximation research includes themes of Consensus and Sensor array.
His most cited work include:
- Gossip Algorithms for Distributed Signal Processing (657 citations)
- Distributed Consensus Algorithms in Sensor Networks With Imperfect Communication: Link Failures and Channel Noise (563 citations)
- Distributed Parameter Estimation in Sensor Networks: Nonlinear Observation Models and Imperfect Communication (363 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Mathematical optimization, Algorithm, Convergence, Distributed computing and Distributed algorithm.
He interconnects Rate of convergence and Observability, Controllability, Control theory in the investigation of issues within Mathematical optimization.
His studies deal with areas such as Stochastic process, Wireless sensor network, Markov process and Estimator as well as Algorithm.
His Wireless sensor network research integrates issues from Network topology and Topology.
His research integrates issues of Information exchange, Distributed generation, Topology and Electric power system in his study of Distributed computing.
His studies in Distributed algorithm integrate themes in fields like Almost surely, Iterative method and Stochastic approximation.
He most often published in these fields:
- Mathematical optimization (29.78%)
- Algorithm (19.10%)
- Convergence (16.01%)
What were the highlights of his more recent work (between 2018-2021)?
- Mathematical optimization (29.78%)
- Stochastic optimization (5.34%)
- Convergence (16.01%)
In recent papers he was focusing on the following fields of study:
Soummya Kar mainly focuses on Mathematical optimization, Stochastic optimization, Convergence, Network topology and Applied mathematics.
His study looks at the relationship between Mathematical optimization and topics such as Rate of convergence, which overlap with Variance reduction and Gradient method.
As a part of the same scientific study, Soummya Kar usually deals with the Convergence, concentrating on Stochastic gradient descent and frequently concerns with Stochastic approximation, Matching and Sampling.
His biological study spans a wide range of topics, including Probabilistic logic, Residual and Topology.
His research investigates the connection between Global optimization and topics such as Gaussian noise that intersect with problems in Distributed algorithm.
His Distributed algorithm study contributes to a more complete understanding of Distributed computing.
Between 2018 and 2021, his most popular works were:
- Distributed stochastic optimization with gradient tracking over strongly-connected networks (39 citations)
- Resilient Distributed Estimation: Sensor Attacks (32 citations)
- MATCHA: Speeding Up Decentralized SGD via Matching Decomposition Sampling (30 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Algorithm
His primary scientific interests are in Stochastic optimization, Convergence, Mathematical optimization, Rate of convergence and Tracking.
His Convergence research is multidisciplinary, incorporating elements of Gradient descent and Applied mathematics.
His Mathematical optimization study combines topics in areas such as Function, Distributed algorithm, Gaussian noise and Maxima and minima.
In his study, Linear programming, Asymptotically optimal algorithm, Sublinear function, Stochastic approximation and Matching is inextricably linked to Stochastic gradient descent, which falls within the broad field of Rate of convergence.
His Matching study integrates concerns from other disciplines, such as Information exchange, Network topology and Topology.
He has researched Tracking in several fields, including Distributed computing, Control, Computation and Empirical risk minimization.
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