What is he best known for?
The fields of study he is best known for:
- Algorithm
- Database
- Artificial intelligence
His primary areas of study are Data mining, Nearest neighbor search, Skyline, Search engine indexing and k-nearest neighbors algorithm.
In most of his Data mining studies, his work intersects topics such as Algorithm.
In general Nearest neighbor search study, his work on Best bin first often relates to the realm of Joins, thereby connecting several areas of interest.
As part of one scientific family, Yufei Tao deals mainly with the area of Skyline, narrowing it down to issues related to the Theoretical computer science, and often Distance based, Scalability and Uncertain data.
Yufei Tao works mostly in the field of Search engine indexing, limiting it down to concerns involving Object and, occasionally, Index, Data management and Probabilistic logic.
Yufei Tao focuses mostly in the field of k-nearest neighbors algorithm, narrowing it down to matters related to Query optimization and, in some cases, Web query classification, Tree, Aggregate and Closest facility.
His most cited work include:
- Progressive skyline computation in database systems (756 citations)
- An optimal and progressive algorithm for skyline queries (727 citations)
- Anatomy: simple and effective privacy preservation (581 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Data mining, Algorithm, Combinatorics, Theoretical computer science and k-nearest neighbors algorithm.
His research in Data mining intersects with topics in Spatial query and Search engine indexing.
His Algorithm research includes themes of Aggregate and Curse of dimensionality.
His Combinatorics research focuses on Discrete mathematics and how it relates to Range.
His Theoretical computer science research is multidisciplinary, incorporating perspectives in Approximation algorithm and Graph.
His study in k-nearest neighbors algorithm is interdisciplinary in nature, drawing from both Nearest neighbor search and Nearest-neighbor chain algorithm.
He most often published in these fields:
- Data mining (34.63%)
- Algorithm (22.44%)
- Combinatorics (18.54%)
What were the highlights of his more recent work (between 2016-2021)?
- Algorithm (22.44%)
- Combinatorics (18.54%)
- Discrete mathematics (9.76%)
In recent papers he was focusing on the following fields of study:
Algorithm, Combinatorics, Discrete mathematics, Parallel algorithm and Space are his primary areas of study.
His work on Time complexity and Matching as part of his general Algorithm study is frequently connected to Joins, Massively parallel and Quality, thereby bridging the divide between different branches of science.
His Combinatorics study incorporates themes from Amortized analysis and k-nearest neighbors algorithm.
His Discrete mathematics research incorporates themes from Classifier and Cluster analysis.
His study on Parallel algorithm also encompasses disciplines like
- Synthetic data and related Scalability, Dynamic data and Access method,
- Enumeration, Core and Enhanced Data Rates for GSM Evolution most often made with reference to Binary relation.
His studies in OPTICS algorithm integrate themes in fields like Data mining and Curse of dimensionality.
Between 2016 and 2021, his most popular works were:
- On the Hardness and Approximation of Euclidean DBSCAN (33 citations)
- Overlap Set Similarity Joins with Theoretical Guarantees (26 citations)
- Output-optimal Parallel Algorithms for Similarity Joins (20 citations)
In his most recent research, the most cited papers focused on:
- Algorithm
- Database
- Artificial intelligence
Yufei Tao mainly focuses on Algorithm, Joins, Discrete mathematics, Theoretical computer science and Classifier.
His Algorithm research focuses on Time complexity and Parallel algorithm.
His Time complexity research integrates issues from Dimension, Curse of dimensionality and Euclidean distance.
In the field of Theoretical computer science, his study on PageRank overlaps with subjects such as Node.
His Classifier research incorporates themes from Binary number and Oracle.
The study of Cluster analysis is intertwined with the study of Data mining in a number of ways.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
