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- Sean P. Meyn

Discipline name
D-index
D-index (Discipline H-index) only includes papers and citation values for an examined
discipline in contrast to General H-index which accounts for publications across all
disciplines.
Citations
Publications
World Ranking
National Ranking

Mathematics
D-index
51
Citations
18,536
261
World Ranking
716
National Ranking
360

Engineering and Technology
D-index
55
Citations
19,136
286
World Ranking
1466
National Ranking
584

2002 - IEEE Fellow For contributions to stochastic control, dynamic optimization, and control of large networks.

- Statistics
- Mathematical analysis
- Artificial intelligence

His main research concerns Markov process, Mathematical optimization, Markov chain, Ergodic theory and Applied mathematics. His research in Markov process intersects with topics in Algorithm, Statistical physics, Lyapunov function and Simulation. His Mathematical optimization research includes elements of Queueing theory and Job shop scheduling.

His Markov chain study incorporates themes from Discrete mathematics, Q-learning and Resolvent. Sean P. Meyn usually deals with Applied mathematics and limits it to topics linked to Stability and Convergence. The various areas that Sean P. Meyn examines in his Markov model study include Range and Mathematical economics.

- Markov Chains and Stochastic Stability (4450 citations)
- STABILITY OF MARKOVIAN PROCESSES III: FOSTER- LYAPUNOV CRITERIA FOR CONTINUOUS-TIME PROCESSES (718 citations)
- Control Techniques for Complex Networks (444 citations)

Sean P. Meyn mostly deals with Mathematical optimization, Markov process, Applied mathematics, Markov chain and Optimal control. His biological study spans a wide range of topics, including Markov decision process and Queueing theory. Sean P. Meyn has included themes like Ergodic theory, Algorithm, Stochastic control and Lyapunov function in his Markov process study.

His research integrates issues of Bounded function, Ergodicity and Combinatorics in his study of Ergodic theory. Sean P. Meyn has researched Applied mathematics in several fields, including Stochastic approximation, Stability, Control theory, Nonlinear system and Markov chain Monte Carlo. Markov chain is closely attributed to Discrete mathematics in his work.

- Mathematical optimization (32.28%)
- Markov process (21.33%)
- Applied mathematics (20.46%)

- Applied mathematics (20.46%)
- Optimal control (13.54%)
- Mathematical optimization (32.28%)

Sean P. Meyn mainly investigates Applied mathematics, Optimal control, Mathematical optimization, Stochastic approximation and Reinforcement learning. His Applied mathematics research is multidisciplinary, incorporating perspectives in Markov process, Duality, Markov chain, Nonlinear system and Function approximation. His Markov process research is multidisciplinary, relying on both Ergodic theory and Stochastic control.

His Function approximation research is multidisciplinary, incorporating elements of Range, Stability and Reproducing kernel Hilbert space. His study in Mathematical optimization is interdisciplinary in nature, drawing from both Quality of service, State space, Decentralised system and Energy storage. His biological study spans a wide range of topics, including Function, Algorithm, Uniform boundedness and Bellman equation.

- Distributed Control Design for Balancing the Grid Using Flexible Loads (16 citations)
- Ordinary Differential Equation Methods for Markov Decision Processes and Application to Kullback--Leibler Control Cost (12 citations)
- Electricity rates for the zero marginal cost grid (12 citations)

- Statistics
- Mathematical analysis
- Artificial intelligence

Sean P. Meyn mainly focuses on Quality of service, Applied mathematics, Reinforcement learning, Mathematical optimization and Stochastic approximation. His Applied mathematics research incorporates elements of Markov process, Function approximation and Nonlinear system. His work in Markov process tackles topics such as Mean squared error which are related to areas like Ergodic theory.

In the field of Reinforcement learning, his study on Q-learning overlaps with subjects such as HVAC. His work on Best response as part of general Mathematical optimization study is frequently linked to Weighting, bridging the gap between disciplines. His Stochastic approximation research is multidisciplinary, relying on both Mathematical economics, Stochastic game, Nash equilibrium, Unobservable and Epsilon-equilibrium.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Markov Chains and Stochastic Stability

Sean Meyn;Richard L. Tweedie.

**(1993)**

7573 Citations

Stability of Markovian processes III: Foster–Lyapunov criteria for continuous-time processes

Sean P. Meyn;R. L. Tweedie.

Advances in Applied Probability **(1993)**

926 Citations

Control Techniques for Complex Networks

Sean Meyn.

**(2007)**

625 Citations

The O.D. E. Method for Convergence of Stochastic Approximation and Reinforcement Learning

V. S. Borkar;S. P. Meyn.

Siam Journal on Control and Optimization **(2000)**

518 Citations

Stability of Markovian processes II: continuous-time processes and sampled chains

Sean P. Meyn;R. L. Tweedie.

Advances in Applied Probability **(1993)**

454 Citations

Stability of queueing networks and scheduling policies

P.R. Kumar;S.P. Meyn.

IEEE Transactions on Automatic Control **(1995)**

410 Citations

Computable Bounds for Geometric Convergence Rates of Markov Chains

Sean P. Meyn;R. L. Tweedie.

Annals of Applied Probability **(1994)**

383 Citations

Exponential and Uniform Ergodicity of Markov Processes

D. Down;S. P. Meyn;R. L. Tweedie.

Annals of Probability **(1995)**

355 Citations

Stability of Markovian processes. I : Criteria for discrete-time chains

Sean P. Meyn;R. L. Tweedie.

Advances in Applied Probability **(1992)**

349 Citations

Stability and convergence of moments for multiclass queueing networks via fluid limit models

J.G. Dai;S.P. Meyn.

IEEE Transactions on Automatic Control **(1995)**

338 Citations

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