What is he best known for?
The fields of study he is best known for:
Yu Wang spends much of his time researching Geotechnical engineering, Statistics, Bayesian probability, Probabilistic logic and Monte Carlo method.
His Geotechnical engineering study combines topics in areas such as Soil water and Representative elementary volume.
The study of Statistics is intertwined with the study of Compressed sensing in a number of ways.
The study incorporates disciplines such as Test data, Data mining, Probability and statistics and Marginal distribution in addition to Bayesian probability.
Yu Wang has researched Probabilistic logic in several fields, including Parametric statistics, Slope stability analysis, Bayesian hierarchical modeling, Algorithm and Upper and lower bounds.
His Monte Carlo method research includes themes of Conditional probability, Reliability engineering and Indoor air quality.
His most cited work include:
- Clinical characteristics of COVID-19-infected cancer patients: a retrospective case study in three hospitals within Wuhan, China. (622 citations)
- Network Traffic Classification Using Correlation Information (221 citations)
- When Intrusion Detection Meets Blockchain Technology: A Review (197 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Geotechnical engineering, Bayesian probability, Data mining, Internal medicine and Probabilistic logic.
His Geotechnical engineering study combines topics from a wide range of disciplines, such as Structural engineering, Limit state design, Soil properties and Spatial variability.
Bayesian probability is a subfield of Statistics that he tackles.
Yu Wang combines subjects such as Traffic classification and Machine learning, Cluster analysis, Artificial intelligence with his study of Data mining.
His study in Probabilistic logic is interdisciplinary in nature, drawing from both Characterization and Monte Carlo method.
He interconnects Reliability and Reliability engineering in the investigation of issues within Monte Carlo method.
He most often published in these fields:
- Geotechnical engineering (20.09%)
- Bayesian probability (10.28%)
- Data mining (9.11%)
What were the highlights of his more recent work (between 2019-2021)?
- Geotechnical engineering (20.09%)
- Internal medicine (8.88%)
- Spatial variability (4.67%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Geotechnical engineering, Internal medicine, Spatial variability, Oncology and Bayesian probability.
Yu Wang has researched Geotechnical engineering in several fields, including Geostatistics and Computer simulation.
His work on Hepatocellular carcinoma, Hazard ratio and Retrospective cohort study as part of his general Internal medicine study is frequently connected to In patient, thereby bridging the divide between different branches of science.
The Spatial variability study combines topics in areas such as Soil science, Soil water, Factor of safety and Finite element method.
His biological study spans a wide range of topics, including Algorithm, Compressed sensing and Parametric statistics.
His Algorithm research integrates issues from Soil horizon, Bayesian inference, Marginal distribution and Markov chain Monte Carlo.
Between 2019 and 2021, his most popular works were:
- Clinical characteristics of COVID-19-infected cancer patients: a retrospective case study in three hospitals within Wuhan, China. (622 citations)
- Detecting insider attacks in medical cyber–physical networks based on behavioral profiling (19 citations)
- Clinical characteristics of 17 patients with COVID-19 and systemic autoimmune diseases: a retrospective study. (15 citations)
In his most recent research, the most cited papers focused on:
His primary areas of study are Geotechnical engineering, Nonparametric statistics, Interpolation, Probabilistic logic and Algorithm.
His Landslide risk assessment study in the realm of Geotechnical engineering interacts with subjects such as Statistical analysis.
His Probabilistic logic research focuses on Soil water and how it relates to Probabilistic method, Borehole and Monte Carlo method.
His Algorithm research is multidisciplinary, incorporating perspectives in Soil horizon, Supervised learning and Markov chain Monte Carlo.
Yu Wang works mostly in the field of Markov chain Monte Carlo, limiting it down to concerns involving Bayesian inference and, occasionally, Spatial variability.
Yu Wang combines subjects such as Machine learning and Bayesian probability with his study of Multivariate interpolation.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
