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.
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.
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.
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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Social influence analysis in large-scale networks
Jie Tang;Jimeng Sun;Chi Wang;Zi Yang.
knowledge discovery and data mining (2009)
The TPR*-tree: an optimized spatio-temporal access method for predictive queries
Yufei Tao;Dimitris Papadias;Jimeng Sun.
very large data bases (2003)
Doctor AI: Predicting Clinical Events via Recurrent Neural Networks
Edward Choi;Mohammad Taha Bahadori;Andy Schuetz;Walter F. Stewart.
Proceedings of the 1st Machine Learning for Healthcare Conference (2016)
RETAIN: An interpretable predictive model for healthcare using reverse time attention mechanism
Edward Choi;Mohammad Taha Bahadori;Jimeng Sun;Joshua Kulas.
neural information processing systems (2016)
Using recurrent neural network models for early detection of heart failure onset.
Edward Choi;Andy Schuetz;Walter F Stewart;Jimeng Sun.
Journal of the American Medical Informatics Association (2017)
GraphScope: parameter-free mining of large time-evolving graphs
Jimeng Sun;Christos Faloutsos;Spiros Papadimitriou;Philip S. Yu.
knowledge discovery and data mining (2007)
Beyond streams and graphs: dynamic tensor analysis
Jimeng Sun;Dacheng Tao;Christos Faloutsos.
knowledge discovery and data mining (2006)
Streaming pattern discovery in multiple time-series
Spiros Papadimitriou;Jimeng Sun;Christos Faloutsos.
very large data bases (2005)
Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review.
Cao Xiao;Edward Choi;Jimeng Sun.
Journal of the American Medical Informatics Association (2018)
Temporal recommendation on graphs via long- and short-term preference fusion
Liang Xiang;Quan Yuan;Shiwan Zhao;Li Chen.
knowledge discovery and data mining (2010)
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