What is he best known for?
The fields of study he is best known for:
- Computer network
- Statistics
- Mathematical analysis
Xi-Ren Cao mainly focuses on Mathematical optimization, Markov chain, Markov process, Applied mathematics and Queueing theory.
His Mathematical optimization study integrates concerns from other disciplines, such as Markov kernel, Sensitivity and Partially observable Markov decision process, Markov decision process, Markov model.
His Markov chain study incorporates themes from Realization, Jackson network and Variance reduction.
His Variable-order Markov model study in the realm of Markov process interacts with subjects such as Inverse.
As a part of the same scientific study, Xi-Ren Cao usually deals with the Applied mathematics, concentrating on Queue and frequently concerns with Discrete event simulation, Steady state, Convergence and Measure.
His studies deal with areas such as Discrete mathematics, Convexity, Infinitesimal, Algorithm and Realization as well as Queueing theory.
His most cited work include:
- Perturbation Analysis of Discrete Event Dynamic Systems (426 citations)
- Perturbation analysis and optimization of queueing networks (258 citations)
- General approach to blind source separation (236 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Mathematical optimization, Markov process, Queueing theory, Markov decision process and Applied mathematics.
His Mathematical optimization research incorporates themes from Markov kernel, Markov model and Sensitivity.
His work carried out in the field of Markov process brings together such families of science as Decision theory and Markov chain.
His Queueing theory research incorporates elements of Queue, Realization, Throughput and Algorithm.
His Markov decision process study deals with Artificial intelligence intersecting with Machine learning.
His Applied mathematics research is multidisciplinary, incorporating perspectives in Poisson's equation, Sample path, Derivative, Infinitesimal and Discrete event simulation.
He most often published in these fields:
- Mathematical optimization (46.54%)
- Markov process (28.11%)
- Queueing theory (26.73%)
What were the highlights of his more recent work (between 2012-2021)?
- Mathematical optimization (46.54%)
- Optimization problem (6.45%)
- Stochastic control (5.99%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Mathematical optimization, Optimization problem, Stochastic control, Probability measure and Markov process.
The concepts of his Mathematical optimization study are interwoven with issues in State, Markov decision process, Ergodicity and Markov chain.
His Markov decision process research integrates issues from Markov kernel and Markov model.
His study in Stochastic control is interdisciplinary in nature, drawing from both Calculus, Non smooth, Applied mathematics and Post-modern portfolio theory.
Xi-Ren Cao has researched Probability measure in several fields, including Differential, Measure and Sensitivity.
The study incorporates disciplines such as Basis, Data-driven and Hybrid system in addition to Markov process.
Between 2012 and 2021, his most popular works were:
- A tutorial on event-based optimization--a new optimization framework (38 citations)
- SENSITIVITY ANALYSIS OF NONLINEAR BEHAVIOR WITH DISTORTED PROBABILITY (17 citations)
- SENSITIVITY ANALYSIS OF NONLINEAR BEHAVIOR WITH DISTORTED PROBABILITY (17 citations)
In his most recent research, the most cited papers focused on:
- Computer network
- Statistics
- Mathematical analysis
Xi-Ren Cao mostly deals with Mathematical optimization, Stochastic control, Markov process, Markov chain and Optimization problem.
He studies Search algorithm which is a part of Mathematical optimization.
His studies in Stochastic control integrate themes in fields like Martingale, Replicating portfolio, Post-modern portfolio theory, Non smooth and Calculus.
His study in Markov process concentrates on Markov kernel, Markov decision process, Markov model and Variable-order Markov model.
His biological study spans a wide range of topics, including Uniformization, Markov property and Partially observable Markov decision process.
His Optimization problem study integrates concerns from other disciplines, such as Measure, Complete market, Sensitivity, Probability measure and Incomplete markets.
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