What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Algorithm
Vahab Mirrokni focuses on Approximation algorithm, Mathematical optimization, Combinatorics, Combinatorial optimization and Submodular set function.
Vahab Mirrokni has included themes like Maximization and Bipartite graph, Graph in his Approximation algorithm study.
The concepts of his Mathematical optimization study are interwoven with issues in Competitive analysis and Price of anarchy.
In general Combinatorics, his work in Graph theory is often linked to K-independent hashing, Feature hashing and Dynamic perfect hashing linking many areas of study.
His Combinatorial optimization research is multidisciplinary, incorporating perspectives in Combinatorial optimization problem, Purchasing and Matroid.
He focuses mostly in the field of Submodular set function, narrowing it down to topics relating to Maximum cut and, in certain cases, Constraint satisfaction problem.
His most cited work include:
- Locality-sensitive hashing scheme based on p-stable distributions (2270 citations)
- Maximizing Non-monotone Submodular Functions (345 citations)
- Optimal marketing strategies over social networks (321 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Mathematical optimization, Common value auction, Approximation algorithm, Combinatorics and Microeconomics.
His Mathematical optimization study frequently draws parallels with other fields, such as Mechanism design.
His Common value auction study integrates concerns from other disciplines, such as Online advertising, Budget constraint, Incentive, Incentive compatibility and Regret.
The Approximation algorithm study which covers Cluster analysis that intersects with Data mining.
Vahab Mirrokni interconnects Discrete mathematics and Upper and lower bounds in the investigation of issues within Combinatorics.
His Nash equilibrium study combines topics from a wide range of disciplines, such as Game theory and Price of anarchy.
He most often published in these fields:
- Mathematical optimization (35.05%)
- Common value auction (18.43%)
- Approximation algorithm (17.52%)
What were the highlights of his more recent work (between 2019-2021)?
- Mathematical optimization (35.05%)
- Common value auction (18.43%)
- Mechanism design (6.95%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Mathematical optimization, Common value auction, Mechanism design, Microeconomics and Bidding.
His studies in Mathematical optimization integrate themes in fields like Time complexity, Exponential time hypothesis and Reservation price.
His work in Common value auction tackles topics such as Regret which are related to areas like Probability distribution, Revenue management, Artificial neural network, Crowdsourcing and Artificial intelligence.
His studies deal with areas such as Reduction, Participation constraint and Limit as well as Mechanism design.
His work on Bid shading, Profit and Incentive as part of his general Microeconomics study is frequently connected to Value, thereby bridging the divide between different branches of science.
As part of the same scientific family, Vahab Mirrokni usually focuses on Bidding, concentrating on Budget constraint and intersecting with Entropy.
Between 2019 and 2021, his most popular works were:
- Product Ranking on Online Platforms (5 citations)
- Dual Mirror Descent for Online Allocation Problems (5 citations)
- Causal Inference with Bipartite Designs. (4 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Algorithm
His primary areas of investigation include Mathematical optimization, Common value auction, Bidding, Theoretical computer science and Revenue management.
His work often combines Mathematical optimization and Power studies.
His Common value auction research is multidisciplinary, incorporating elements of Display advertising, Online advertising, Knowledge management and Incentive, Incentive compatibility.
His Bidding research includes themes of Entropy and Budget constraint.
His Theoretical computer science study integrates concerns from other disciplines, such as Leverage, Object, Propensity score matching, Causal inference and Causal model.
His Revenue management research incorporates elements of Probability distribution, Regret and Dual.
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