What is he best known for?
The fields of study he is best known for:
- Operating system
- Programming language
- Database
Jingren Zhou mostly deals with Database, Distributed computing, Query optimization, Materialized view and Scripting language.
His Database transaction study in the realm of Database interacts with subjects such as Index.
As a member of one scientific family, he mostly works in the field of Distributed computing, focusing on Scalability and, on occasion, Cloud computing, Scheduling, Fair-share scheduling and Two-level scheduling.
His Query optimization research is included under the broader classification of Data mining.
His Scripting language study combines topics from a wide range of disciplines, such as Explicit parallelism, Joins and Parallel processing.
His Parallel processing research is multidisciplinary, incorporating perspectives in Programming language, SQL, Statement, Programming paradigm and Scope.
His most cited work include:
- SCOPE: easy and efficient parallel processing of massive data sets (736 citations)
- Implementing database operations using SIMD instructions (227 citations)
- Apollo: scalable and coordinated scheduling for cloud-scale computing (215 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Theoretical computer science, Artificial intelligence, Data mining, Computation and Database.
His Theoretical computer science research incorporates themes from Scalability, Embedding, Graph embedding, Graph and Differential privacy.
Jingren Zhou interconnects Matching and Machine learning in the investigation of issues within Artificial intelligence.
His Query optimization study in the realm of Data mining connects with subjects such as Plan.
His Query optimization research is multidisciplinary, relying on both Distributed computing, Sargable and Scripting language.
His biological study spans a wide range of topics, including Discrete mathematics, Speedup, Parallel computing and Bipartite graph.
He most often published in these fields:
- Theoretical computer science (23.81%)
- Artificial intelligence (13.61%)
- Data mining (13.61%)
What were the highlights of his more recent work (between 2018-2021)?
- Theoretical computer science (23.81%)
- Artificial intelligence (13.61%)
- Machine learning (12.24%)
In recent papers he was focusing on the following fields of study:
Jingren Zhou spends much of his time researching Theoretical computer science, Artificial intelligence, Machine learning, Graph and Graph.
His Theoretical computer science study combines topics in areas such as Embedding, Graph embedding, Representation, Differential privacy and Extension.
In the field of Machine learning, his study on Artificial neural network overlaps with subjects such as Generalization.
His Graph study incorporates themes from Time complexity, Algorithm and Computation.
The concepts of his Graph study are interwoven with issues in Information retrieval, Automatic summarization and Information integration.
His research investigates the connection between Sampling and topics such as Pipeline that intersect with problems in Data mining.
Between 2018 and 2021, his most popular works were:
- Representation Learning for Attributed Multiplex Heterogeneous Network (77 citations)
- AliGraph: a comprehensive graph neural network platform (47 citations)
- Is a Single Vector Enough?: Exploring Node Polysemy for Network Embedding (30 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Programming language
- Artificial intelligence
His primary areas of investigation include Theoretical computer science, Artificial intelligence, Machine learning, Recommender system and Differential privacy.
His Theoretical computer science research is multidisciplinary, incorporating elements of Graph, Embedding, Lift, Correctness and Complete bipartite graph.
His study in Lift is interdisciplinary in nature, drawing from both Property, Sampling and Speedup.
His study in the fields of Artificial neural network, Representation, Modality and Deep learning under the domain of Artificial intelligence overlaps with other disciplines such as Modal.
His Machine learning research includes elements of Matching and Independence.
His studies in Recommender system integrate themes in fields like Language model, Variety and Feature learning.
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