What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Organic chemistry
- Chromatography
Peter W. Carr mostly deals with Chromatography, Analytical chemistry, High-performance liquid chromatography, Reversed-phase chromatography and Phase.
Peter W. Carr brings together Chromatography and Cubic zirconia to produce work in his papers.
The study incorporates disciplines such as Elution, Column chromatography, Volumetric flow rate and Chemical engineering, Particle size in addition to Analytical chemistry.
His biological study deals with issues like Hydrocarbon, which deal with fields such as Octanol.
His Phase research is multidisciplinary, relying on both Hydrophilic interaction chromatography, Adsorption, Silanol, Aqueous solution and Chromatography column.
Within one scientific family, Peter W. Carr focuses on topics pertaining to Hydrogen bond under Solvation, and may sometimes address concerns connected to Solvent and Physical chemistry.
His most cited work include:
- The chemical interpretation and practice of linear solvation energy relationships in chromatography (385 citations)
- Chemistry of zirconia and its use in chromatography (338 citations)
- Fast, comprehensive two-dimensional liquid chromatography. (321 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Chromatography, Analytical chemistry, Reversed-phase chromatography, High-performance liquid chromatography and Phase.
The Chromatography study combines topics in areas such as Selectivity and Adsorption.
His Adsorption study frequently links to other fields, such as Inorganic chemistry.
His study in Analytical chemistry is interdisciplinary in nature, drawing from both Solvation, Volumetric flow rate and Column.
His work in Solvation tackles topics such as Solvatochromism which are related to areas like Hydrogen bond.
He frequently studies issues relating to Silanol and Phase.
He most often published in these fields:
- Chromatography (49.33%)
- Analytical chemistry (25.72%)
- Reversed-phase chromatography (19.19%)
What were the highlights of his more recent work (between 2011-2021)?
- Artificial intelligence (9.40%)
- Chromatography (49.33%)
- Computer vision (5.76%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Artificial intelligence, Chromatography, Computer vision, Ecology and Machine learning.
His work on Entropy, Feature extraction and Video tracking as part of his general Artificial intelligence study is frequently connected to Re identification, thereby bridging the divide between different branches of science.
His Chromatography study combines topics from a wide range of disciplines, such as Phase, Column and Analytical chemistry.
His research integrates issues of Selectivity and Column chromatography in his study of Phase.
His biological study spans a wide range of topics, including Porosity and Volumetric flow rate.
His studies in Computer vision integrate themes in fields like Trajectory and Robustness.
Between 2011 and 2021, his most popular works were:
- Argoverse: 3D Tracking and Forecasting With Rich Maps (226 citations)
- Argoverse: 3D Tracking and Forecasting with Rich Maps (118 citations)
- Reefs and islands of the Chagos Archipelago, Indian Ocean: why it is the world's largest no-take marine protected area. (115 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Organic chemistry
- Statistics
Peter W. Carr mainly focuses on Artificial intelligence, Computer vision, Machine learning, Tracking data and Human–computer interaction.
His work on Video tracking, Motion and Feature extraction as part of general Artificial intelligence research is frequently linked to Basketball and Re identification, thereby connecting diverse disciplines of science.
He works mostly in the field of Video tracking, limiting it down to concerns involving Point cloud and, occasionally, Lidar.
His work carried out in the field of Computer vision brings together such families of science as Convolution, Computation, Robustness and Trajectory.
As a part of the same scientific family, he mostly works in the field of Machine learning, focusing on Representation and, on occasion, Adversarial system.
His research in Tracking data tackles topics such as Entropy which are related to areas like Data mining.
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