What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
Artificial intelligence, Machine learning, Information retrieval, Multi-label classification and Probabilistic logic are his primary areas of study.
The Artificial intelligence study combines topics in areas such as Spamming and Pattern recognition.
When carried out as part of a general Machine learning research project, his work on Semi-supervised learning and Ranking is frequently linked to work in Gaussian process and Rank, therefore connecting diverse disciplines of study.
His Information retrieval study integrates concerns from other disciplines, such as Site map and Web content.
His Multi-label classification research incorporates elements of Binary classification, Subspace topology and Search engine indexing.
Shipeng Yu interconnects Gold standard, Supervised learning, Majority rule and Multiple experts in the investigation of issues within Probabilistic logic.
His most cited work include:
- Learning From Crowds (975 citations)
- VIPS: a Vision-based Page Segmentation Algorithm (484 citations)
- Extracting content structure for web pages based on visual representation (325 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Artificial intelligence, Machine learning, Data mining, Information retrieval and Pattern recognition.
His work deals with themes such as Nonparametric statistics and Empirical research, which intersect with Artificial intelligence.
Shipeng Yu regularly links together related areas like Bayesian probability in his Machine learning studies.
His biological study spans a wide range of topics, including Document segmentation, Vision based and Natural language processing.
The Decision boundary and Feature vector research Shipeng Yu does as part of his general Pattern recognition study is frequently linked to other disciplines of science, such as Set, therefore creating a link between diverse domains of science.
His work deals with themes such as Gold standard, Supervised learning, Majority rule and Multiple experts, which intersect with Probabilistic logic.
He most often published in these fields:
- Artificial intelligence (50.00%)
- Machine learning (33.65%)
- Data mining (22.12%)
What were the highlights of his more recent work (between 2012-2016)?
- Artificial intelligence (50.00%)
- Machine learning (33.65%)
- Workflow (4.81%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Machine learning, Workflow, Data mining and Patient data.
As part of his studies on Artificial intelligence, Shipeng Yu often connects relevant areas like Computer vision.
His work on Machine learning is being expanded to include thematically relevant topics such as Empirical research.
As a part of the same scientific study, he usually deals with the Workflow, concentrating on Medical record and frequently concerns with Adverse effect and Public health.
Shipeng Yu has researched Data mining in several fields, including Medical physics and Cohort.
His Information retrieval research is multidisciplinary, incorporating perspectives in Theoretical computer science, Service and Healthcare system.
Between 2012 and 2016, his most popular works were:
- Predicting readmission risk with institution-specific prediction models (56 citations)
- Going Digital: A Survey on Digitalization and Large-Scale Data Analytics in Healthcare (51 citations)
- Leveraging Public Health Data for Prediction and Prevention of Adverse Events (40 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
His main research concerns Medical emergency, Readmission risk, Predictive modelling, Medicaid and Workflow.
Shipeng Yu interconnects Risk analysis, Regression analysis and Intensive care medicine in the investigation of issues within Medical emergency.
His Readmission risk research includes themes of Classification methods, Proportional hazards model and Simulation.
The concepts of his Predictive modelling study are interwoven with issues in Leverage and Emergency medicine.
The study incorporates disciplines such as Adverse effect, Public health and Medical record in addition to Workflow.
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