What is he best known for?
The fields of study he is best known for:
- Statistics
- Algorithm
- Artificial intelligence
Sergei Vassilvitskii mainly focuses on Theoretical computer science, Algorithm, Cluster analysis, Graph and Approximation algorithm.
The study incorporates disciplines such as Mathematical optimization, Maximization, Computation and Matroid in addition to Theoretical computer science.
His Algorithm research is multidisciplinary, incorporating elements of Kendall tau distance, k-means clustering and Relevance.
His k-means clustering study which covers Constant that intersects with Polynomial.
Sergei Vassilvitskii combines subjects such as Center and Representation with his study of Cluster analysis.
His work in the fields of Graph, such as Subgraph isomorphism problem, overlaps with other areas such as Factor.
His most cited work include:
- k-means++: the advantages of careful seeding (4477 citations)
- A model of computation for MapReduce (453 citations)
- Scalable k-means++ (407 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Theoretical computer science, Algorithm, Advertising, Mathematical optimization and Common value auction.
The Theoretical computer science study which covers Inverted index that intersects with Boolean expression and Data mining.
His work carried out in the field of Algorithm brings together such families of science as Simple, Polynomial and k-means clustering.
His k-means clustering research is within the category of Cluster analysis.
Sergei Vassilvitskii has included themes like Online advertising, Marketing and Payment in his Advertising study.
His Common value auction research is multidisciplinary, incorporating elements of Incentive and Reservation price.
He most often published in these fields:
- Theoretical computer science (17.68%)
- Algorithm (14.63%)
- Advertising (14.02%)
What were the highlights of his more recent work (between 2016-2021)?
- Algorithm (14.63%)
- Theoretical computer science (17.68%)
- Mathematical optimization (11.59%)
In recent papers he was focusing on the following fields of study:
Sergei Vassilvitskii focuses on Algorithm, Theoretical computer science, Mathematical optimization, Cluster analysis and Simple.
His Algorithm research integrates issues from Low-rank approximation and Principal component analysis.
His Theoretical computer science research incorporates themes from Hierarchical clustering and Directed graph.
His work in the fields of Mathematical optimization, such as Approximation algorithm and Constraint, intersects with other areas such as Value.
His Cluster analysis study focuses mostly on CURE data clustering algorithm and Data stream clustering.
His Simple research is multidisciplinary, incorporating perspectives in Machine learning, Feature, Artificial intelligence and Process.
Between 2016 and 2021, his most popular works were:
- Fair Clustering Through Fairlets (141 citations)
- Competitive Caching with Machine Learned Advice (54 citations)
- Local search methods for k-means with outliers (45 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Algorithm
- Artificial intelligence
His scientific interests lie mostly in Common value auction, Reservation price, Theoretical computer science, Competitive analysis and Online algorithm.
In Reservation price, Sergei Vassilvitskii works on issues like Incentive, which are connected to Mathematical optimization.
His Mathematical optimization research focuses on Mathematical proof and how it connects with Cluster analysis.
His study in Theoretical computer science is interdisciplinary in nature, drawing from both Complement, Advice and Cache.
His Online algorithm study introduces a deeper knowledge of Algorithm.
Specifically, his work in Algorithm is concerned with the study of Approximation algorithm.
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