What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
His primary areas of investigation include Data mining, Artificial intelligence, Machine learning, Data science and Interpretability.
His Data mining research focuses on subjects like Dimensionality reduction, which are linked to Feature selection and Data cube.
His work on Artificial neural network and Deep learning as part of general Artificial intelligence research is often related to Medical classification and Dynamic network analysis, thus linking different fields of science.
The study incorporates disciplines such as Topic model and Health records in addition to Machine learning.
His Data science research includes elements of Domain, Scalability, Petabyte, Text mining and Key.
His work carried out in the field of Recurrent neural network brings together such families of science as Clinical events, Logistic regression, Generalizability theory and Medical emergency.
His most cited work include:
- Social influence analysis in large-scale networks (672 citations)
- GraphScope: parameter-free mining of large time-evolving graphs (507 citations)
- RETAIN: An interpretable predictive model for healthcare using reverse time attention mechanism (459 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Machine learning, Data mining, Deep learning and Artificial neural network are his primary areas of study.
His Artificial intelligence research incorporates elements of Natural language processing and Pattern recognition.
While the research belongs to areas of Machine learning, Jimeng Sun spends his time largely on the problem of Benchmark, intersecting his research to questions surrounding Hyperparameter.
The various areas that he examines in his Data mining study include Data stream, Theoretical computer science, Algorithm and Dimensionality reduction.
Deep learning is often connected to Graph in his work.
His study in Recurrent neural network is interdisciplinary in nature, drawing from both Electronic health record and Logistic regression.
He most often published in these fields:
- Artificial intelligence (47.55%)
- Machine learning (30.88%)
- Data mining (26.96%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (47.55%)
- Deep learning (21.57%)
- Machine learning (30.88%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Deep learning, Machine learning, Artificial neural network and Inference.
His work deals with themes such as Natural language processing and Pattern recognition, which intersect with Artificial intelligence.
The concepts of his Deep learning study are interwoven with issues in Recurrent neural network, Intensive care and Benchmark.
His Machine learning research integrates issues from Graph, Field, Code, Disease and Drug discovery.
His work is dedicated to discovering how Artificial neural network, Reduction are connected with Gaussian noise and Regularization and other disciplines.
His research in Distributed Computing Environment intersects with topics in Key and Data mining.
Between 2018 and 2021, his most popular works were:
- Hierarchical reinforcement learning for course recommendation in MOOCs (27 citations)
- Optimizing sparse tensor times matrix on GPUs (15 citations)
- Ensuring electronic medical record simulation through better training, modeling, and evaluation. (11 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
Jimeng Sun spends much of his time researching Artificial intelligence, Deep learning, Artificial neural network, Machine learning and Term.
His specific area of interest is Artificial intelligence, where Jimeng Sun studies Robustness.
His Deep learning study combines topics in areas such as Graph neural networks and Theoretical computer science.
His study explores the link between Artificial neural network and topics such as Reduction that cross with problems in Gaussian noise and Regularization.
Jimeng Sun focuses mostly in the field of Machine learning, narrowing it down to matters related to Field and, in some cases, Drug discovery.
The study incorporates disciplines such as Scalability and Uniqueness in addition to Mathematical optimization.
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