What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Algorithm
- Machine learning
His primary areas of investigation include Theoretical computer science, Data mining, Graph, Vertex and Reachability.
His work deals with themes such as Metric, Core, Set, Maximal set and Range, which intersect with Theoretical computer science.
His work on ID3 algorithm, Fractal tree index, Decision tree learning and FSA-Red Algorithm as part of general Data mining study is frequently linked to Hoeffding's inequality, bridging the gap between disciplines.
His Graph study combines topics in areas such as Transitive closure, Set cover problem, Directed acyclic graph and Bounded function.
His Vertex research is multidisciplinary, incorporating perspectives in Link analysis, Dense graph, Bipartite graph and Vertex.
His Transitive reduction study integrates concerns from other disciplines, such as Graph database, Minimum degree spanning tree, Spanning tree and Directed graph.
His most cited work include:
- 3-HOP: a high-compression indexing scheme for reachability query (169 citations)
- A Survey of Algorithms for Dense Subgraph Discovery (159 citations)
- Efficient decision tree construction on streaming data (139 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Data mining, Theoretical computer science, Graph, Artificial intelligence and Parallel computing.
Ruoming Jin has included themes like Cluster analysis, Graph, Set and Automatic summarization in his Data mining study.
His Theoretical computer science study combines topics from a wide range of disciplines, such as Metric, Mathematical optimization, Approximation algorithm, Scale and Robustness.
His Vertex study in the realm of Graph interacts with subjects such as Shortest distance.
His research in Artificial intelligence tackles topics such as Machine learning which are related to areas like Social network.
The various areas that he examines in his Parallel computing study include Scalability and Distributed shared memory.
He most often published in these fields:
- Data mining (31.82%)
- Theoretical computer science (27.27%)
- Graph (18.18%)
What were the highlights of his more recent work (between 2016-2021)?
- Theoretical computer science (27.27%)
- Artificial intelligence (13.64%)
- Robustness (5.19%)
In recent papers he was focusing on the following fields of study:
Ruoming Jin mostly deals with Theoretical computer science, Artificial intelligence, Robustness, Adversarial system and Differential privacy.
The concepts of his Theoretical computer science study are interwoven with issues in Data modeling, Autoencoder, Graph, Pairwise comparison and Graph.
Ruoming Jin has researched Graph in several fields, including Multi-label classification and Degree.
His Artificial intelligence study combines topics in areas such as Social media, Machine learning and Metric.
While the research belongs to areas of Metric, Ruoming Jin spends his time largely on the problem of Recommender system, intersecting his research to questions surrounding Data mining.
His work carried out in the field of Adversarial system brings together such families of science as Scalability and Leverage.
Between 2016 and 2021, his most popular works were:
- Privacy-aware smart city: A case study in collaborative filtering recommender systems (20 citations)
- Density-Adaptive Local Edge Representation Learning with Generative Adversarial Network Multi-label Edge Classification (11 citations)
- Heterogeneous Gaussian mechanism: Preserving differential privacy in deep learning with provable robustness (10 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Algorithm
- Machine learning
His scientific interests lie mostly in Theoretical computer science, Differential privacy, Robustness, Adversarial system and Noise.
His studies in Theoretical computer science integrate themes in fields like Autoencoder, Matching, Consistency, Pairwise comparison and Homophily.
His Autoencoder research integrates issues from Data modeling, Feature learning, Generative adversarial network and Graph.
Ruoming Jin has researched Differential privacy in several fields, including Property, Deep learning, Artificial intelligence and Scale.
His Robustness study frequently intersects with other fields, such as Gaussian noise.
His research integrates issues of Scalability, Distributed computing and Leverage in his study of Adversarial system.
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