What is he best known for?
The fields of study he is best known for:
- Computer network
- The Internet
- Microeconomics
His primary areas of study are Microeconomics, Computer network, Mathematical economics, Resource allocation and Real-time computing.
His work in the fields of Microeconomics, such as Competition and Variable pricing, overlaps with other areas such as Resource, Arbitrage pricing theory and Average cost pricing.
His Computer network study combines topics from a wide range of disciplines, such as Overlay network, The Internet and Distributed computing.
His study in the fields of Internet access under the domain of The Internet overlaps with other disciplines such as End to end congestion control.
In general Mathematical economics, his work in Game theory and Nash equilibrium is often linked to Scalability and Empirical distribution function linking many areas of study.
His study explores the link between Resource allocation and topics such as Telecommunications network that cross with problems in Price of anarchy, Network model, End user and Congestion game.
His most cited work include:
- A buffer-based approach to rate adaptation: evidence from a large video streaming service (584 citations)
- Efficiency Loss in a Network Resource Allocation Game (453 citations)
- End-to-end congestion control for the internet: delays and stability (362 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Microeconomics, Mathematical optimization, Mathematical economics, Nash equilibrium and Computer network.
His study looks at the intersection of Microeconomics and topics like Quality with Value.
His Mathematical optimization study integrates concerns from other disciplines, such as Externality, Queueing theory, Regret, Multi-armed bandit and Benchmark.
His work on Stochastic game, Markov perfect equilibrium and Sequential game as part of his general Mathematical economics study is frequently connected to Empirical distribution function, thereby bridging the divide between different branches of science.
His Nash equilibrium research includes themes of Resource allocation, Price of anarchy, Bidding, Marginal cost and Game theory.
His Computer network research integrates issues from Distributed computing and Overlay network, The Internet.
He most often published in these fields:
- Microeconomics (24.72%)
- Mathematical optimization (23.60%)
- Mathematical economics (15.17%)
What were the highlights of his more recent work (between 2018-2021)?
- Mathematical optimization (23.60%)
- Regret (10.11%)
- Stochastic modelling (3.93%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Mathematical optimization, Regret, Stochastic modelling, Econometrics and Control.
Ramesh Johari has included themes like Multi-armed bandit and Markov chain in his Mathematical optimization study.
His biological study spans a wide range of topics, including Covariate, Greedy algorithm, Unobservable, Residual and Time horizon.
His Stochastic modelling investigation overlaps with other areas such as Mean field limit, Face, Statistical parameter, Queueing theory and Queue.
His Econometrics research incorporates elements of Supply and demand and Market power, Competition.
Combining a variety of fields, including Leverage, Price discrimination, Information disclosure, Microeconomics and Revenue, are what the author presents in his essays.
Between 2018 and 2021, his most popular works were:
- Competition and Efficiency of Coalitions in Cournot Games With Uncertainty (4 citations)
- Learning Unknown Service Rates in Queues: A Multiarmed Bandit Approach (4 citations)
- Experimental Design in Two-Sided Platforms: An Analysis of Bias (3 citations)
In his most recent research, the most cited papers focused on:
- Computer network
- The Internet
- Microeconomics
Ramesh Johari spends much of his time researching Mathematical optimization, Regret, Multi-armed bandit, Greedy algorithm and Stochastic modelling.
His work carried out in the field of Mathematical optimization brings together such families of science as Queueing theory and Service.
His Regret research is multidisciplinary, relying on both Covariate, Unobservable and Residual.
His Multi-armed bandit research includes elements of Prior probability, Bayesian probability and Time horizon.
His Stochastic modelling research encompasses a variety of disciplines, including Contraction, Face, Mean field limit, Scheduling and Queue.
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