What is he best known for?
The fields of study he is best known for:
- Statistics
- Computer network
- Artificial intelligence
Vikram Krishnamurthy mainly focuses on Mathematical optimization, Markov process, Markov chain, Algorithm and Markov model.
His work deals with themes such as Expectation–maximization algorithm, Scheduling, Automatic repeat request and Fading, which intersect with Mathematical optimization.
His Markov process study which covers Optimal control that intersects with Retransmission.
The Markov chain study combines topics in areas such as Particle filter, Estimator, Maximum a posteriori estimation and Hidden Markov model.
His Algorithm research incorporates elements of Code division multiple access and Stochastic approximation.
His Markov model research is multidisciplinary, incorporating elements of White noise, Artificial intelligence and Pattern recognition.
His most cited work include:
- Particle filters for state estimation of jump Markov linear systems (631 citations)
- On-line estimation of hidden Markov model parameters based on the Kullback-Leibler information measure (252 citations)
- Algorithms for optimal scheduling and management of hidden Markov model sensors (222 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Mathematical optimization, Algorithm, Markov process, Markov chain and Artificial intelligence.
His Mathematical optimization study integrates concerns from other disciplines, such as Stochastic approximation, Stochastic process, Communication channel, Markov decision process and Applied mathematics.
Vikram Krishnamurthy has researched Communication channel in several fields, including Transmission and Computer network.
Vikram Krishnamurthy has included themes like Kalman filter, Code division multiple access, Hidden Markov model and Expectation–maximization algorithm in his Algorithm study.
Markov process is often connected to Control theory in his work.
His studies deal with areas such as Radar, Machine learning, Computer vision and Pattern recognition as well as Artificial intelligence.
He most often published in these fields:
- Mathematical optimization (33.33%)
- Algorithm (24.92%)
- Markov process (19.64%)
What were the highlights of his more recent work (between 2015-2021)?
- Mathematical optimization (33.33%)
- Markov decision process (10.40%)
- Algorithm (24.92%)
In recent papers he was focusing on the following fields of study:
Vikram Krishnamurthy focuses on Mathematical optimization, Markov decision process, Algorithm, Artificial intelligence and Partially observable Markov decision process.
His Mathematical optimization research integrates issues from State, Bounded function, Bayesian probability, Markov chain and Sequence.
Vikram Krishnamurthy does research in Markov chain, focusing on Markov property specifically.
His Markov decision process study combines topics from a wide range of disciplines, such as Linear programming, Scheduling, Optimal control and Estimator.
His Algorithm research is multidisciplinary, relying on both Kernel, Weak convergence, Covariance, Waveform and Hidden Markov model.
His Probability distribution course of study focuses on Sampling distribution and Markov process.
Between 2015 and 2021, his most popular works were:
- Adaptive Scheme for Caching YouTube Content in a Cellular Network: Machine Learning Approach (59 citations)
- Partially Observed Markov Decision Processes: From Filtering to Controlled Sensing (45 citations)
- A Distributed Coalition Game Approach to Femto-Cloud Formation (17 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Computer network
His primary areas of study are Algorithm, Friendship paradox, Markov chain, Mathematical optimization and Artificial intelligence.
The concepts of his Algorithm study are interwoven with issues in Posterior probability, Filter, Stochastic process, Covariance and Sequence.
He combines subjects such as Stochastic volatility, Convergence, Markov decision process, Applied mathematics and Hidden Markov model with his study of Markov chain.
His biological study focuses on Stochastic optimization.
His research on Stochastic optimization also deals with topics like
- Sampling distribution, which have a strong connection to Markov process,
- Search algorithm which connect with Stochastic approximation.
The study incorporates disciplines such as Function, Machine learning and Nonlinear filtering in addition to Artificial intelligence.
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