What is he best known for?
The fields of study he is best known for:
- Statistics
- Mathematical analysis
- Machine learning
Assaf Zeevi mainly investigates Mathematical optimization, Queueing theory, Dynamic pricing, Revenue management and Revenue.
The study incorporates disciplines such as Regret, Multi-armed bandit and Markov process in addition to Mathematical optimization.
As part of one scientific family, he deals mainly with the area of Queueing theory, narrowing it down to issues related to the Linear programming, and often Total cost, Asymptotic analysis and Stochastic programming.
Assaf Zeevi has researched Dynamic pricing in several fields, including Stochastic modelling and Demand curve.
His work carried out in the field of Revenue management brings together such families of science as Discount points, Parametric statistics, Curse of dimensionality, Minimax and Upper and lower bounds.
His studies deal with areas such as Algorithm, Online algorithm, Microeconomics and Parametric family as well as Revenue.
His most cited work include:
- Dynamic Pricing Without Knowing the Demand Function: Risk Bounds and Near-Optimal Algorithms (279 citations)
- Security of quantum key distribution with entangled photons against individual attacks (186 citations)
- Beyond Correlation: Extreme Co-movements Between Financial Assets (180 citations)
What are the main themes of his work throughout his whole career to date?
Assaf Zeevi focuses on Mathematical optimization, Regret, Revenue management, Minimax and Revenue.
His biological study spans a wide range of topics, including Upper and lower bounds, Random variable and Applied mathematics.
His Regret research integrates issues from Variation and Mathematical economics.
The Revenue management study combines topics in areas such as Algorithm, Queueing theory and Econometrics.
His Revenue study frequently intersects with other fields, such as Dynamic pricing.
In his research on the topic of Dynamic pricing, Price optimization is strongly related with Demand curve.
He most often published in these fields:
- Mathematical optimization (45.08%)
- Regret (22.13%)
- Revenue management (13.93%)
What were the highlights of his more recent work (between 2017-2021)?
- Mathematical optimization (45.08%)
- Regret (22.13%)
- Machine learning (4.92%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Mathematical optimization, Regret, Machine learning, Artificial intelligence and Selection.
His work deals with themes such as Sampling, Estimator and Random variable, which intersect with Mathematical optimization.
His research integrates issues of Algorithm and Time horizon in his study of Regret.
His work in Time horizon tackles topics such as Complete information which are related to areas like Revenue and Dynamic pricing.
His research in Revenue intersects with topics in Product, Microeconomics, Demand curve and System dynamics.
His Selection research includes elements of Variety, Computation and Reduction.
Between 2017 and 2021, his most popular works were:
- MNL-Bandit: A Dynamic Learning Approach to Assortment Selection (34 citations)
- Optimal Exploration-Exploitation in a Multi-Armed-Bandit Problem with Non-stationary Rewards (30 citations)
- A General Approach to Multi-Armed Bandits Under Risk Criteria (17 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Mathematical analysis
- Machine learning
Assaf Zeevi mainly focuses on Regret, Mathematical optimization, Multi-armed bandit, Performance metric and Machine learning.
His Mathematical optimization study focuses on Variation in particular.
The concepts of his Multi-armed bandit study are interwoven with issues in Minimax, Time horizon, Distribution, Pointwise and Complement.
Performance metric combines with fields such as Random variable, Center, Conjunction, Set and Relation in his research.
His work on Selection and Multinomial logistic regression as part of general Machine learning study is frequently connected to Dynamic learning, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
You can notice a mix of various disciplines of study, such as Simple and Oracle, in his Artificial intelligence studies.
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