David Ginsbourger
David Ginsbourger publishes mostly in Artificial Intelligence, Computational Theory and Mathematics and Environmental Engineering, on topics such as Gaussian Processes and Bayesian Inference, Advanced Multi-Objective Optimization Algorithms and Reservoir Engineering and Simulation Methods.
- World rank
- #234,839 of 1,633,909 ranked researchers
- Rank in United States
- #92,728 of 358,040
- Works
- 39
- Citations
- 245
- Citations per work
- 6.3
What does David Ginsbourger research?
Shares of their own output, by the field each of their topics belongs to. The grey slice is everything not listed.
- 1Artificial Intelligence25% of their works
- 2Computational Theory and Mathematics17% of their works
- 3Environmental Engineering12% of their works
- 4Ocean Engineering12% of their works
- 5Management Science and Operations Research10% of their works
- 6Statistics, Probability and Uncertainty10% of their works
Research topics
- Gaussian Processes and Bayesian Inference
- Advanced Multi-Objective Optimization Algorithms
- Reservoir Engineering and Simulation Methods
- Probabilistic and Robust Engineering Design
- Groundwater flow and contamination studies
- Soil Geostatistics and Mapping
- Optimal Experimental Design Methods
- Machine Learning and Algorithms
Which keywords describe David Ginsbourger's research?
The keywords of their largest research topics: Machine Learning, Deep Learning, Gaussian Processes, Uncertainty Quantification, Evolutionary Algorithms, Multiobjective Optimization, Sparse Regression and Variational Inference.
- Active Learning
- Response Surface Methodology
- Geostatistics
- Digital Soil Mapping
- Polynomial Chaos
- Data Assimilation
- Variational Inference
- Multiobjective Optimization
- Uncertainty Quantification
- Deep Learning
- Machine Learning
- Gaussian Processes
- Evolutionary Algorithms
- Sparse Regression
- Optimization
- Ensemble Kalman Filter
- Sensitivity Analysis
- Experimental Design
- Groundwater Flow
- Transport Modeling
- Semi-Supervised Learning
Size is their works in the topics tagged with each word, from their 8 largest topics. Each links to the topic it comes from most.
All 21 words, with their numbers
- Machine Learning143 topics
- Deep Learning112 topics
- Gaussian Processes112 topics
- Uncertainty Quantification102 topics
- Evolutionary Algorithms91 topic
- Multiobjective Optimization91 topic
- Sparse Regression91 topic
- Variational Inference91 topic
- Optimization82 topics
- Data Assimilation51 topic
- Ensemble Kalman Filter51 topic
- Polynomial Chaos51 topic
- Sensitivity Analysis51 topic
- Digital Soil Mapping31 topic
- Experimental Design31 topic
- Geostatistics31 topic
- Groundwater Flow31 topic
- Response Surface Methodology31 topic
- Transport Modeling31 topic
- Active Learning21 topic
- Semi-Supervised Learning21 topic
Where does David Ginsbourger work?
David Ginsbourger's main affiliation in the publication record is Institute of Mathematical Statistics, United States.
How many publications and citations does David Ginsbourger have?
Science Explorer counts 39 works and 245 citations for David Ginsbourger, ranking #234,839 of 1,633,909 researchers worldwide on the composite score.
Ranked on field-normalised excellence (50%), output (30%) and citations (20%). Counts come from OpenAlex author records, which occasionally merge different people who share a name or split one person into several.
Papers, co-authors, who cited this work and researchers on the nearest topics are in the interactive view on the map. Is this your page? Request a correction or removal.