What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
His main research concerns Artificial intelligence, Machine learning, Quality control and genetic algorithms, Learning classifier system and Genetics.
His study focuses on the intersection of Artificial intelligence and fields such as Population-based incremental learning with connections in the field of Parallel genetic algorithm.
His research on Quality control and genetic algorithms concerns the broader Genetic algorithm.
He is interested in Meta-optimization, which is a field of Genetic algorithm.
John J. Grefenstette interconnects Algorithm design and Test functions for optimization in the investigation of issues within Meta-optimization.
His work in the fields of Genetics, such as Effective population size, intersects with other areas such as Allele frequency.
His most cited work include:
- Optimization of Control Parameters for Genetic Algorithms (2438 citations)
- Genetic Algorithms for the Traveling Salesman Problem (657 citations)
- Genome-wide survey of SNP variation uncovers the genetic structure of cattle breeds. (651 citations)
What are the main themes of his work throughout his whole career to date?
John J. Grefenstette spends much of his time researching Artificial intelligence, Machine learning, Genetic algorithm, Environmental health and Vaccination.
His study on Learning classifier system is often connected to Quality as part of broader study in Machine learning.
His Genetic algorithm study is concerned with the larger field of Mathematical optimization.
His Mathematical optimization research incorporates elements of Fitness landscape and Mutation rate.
Biostatistics is closely connected to Agent-based model in his research, which is encompassed under the umbrella topic of Environmental health.
As part of one scientific family, John J. Grefenstette deals mainly with the area of Vaccination, narrowing it down to issues related to the Demography, and often Pediatrics.
He most often published in these fields:
- Artificial intelligence (40.87%)
- Machine learning (20.87%)
- Genetic algorithm (17.39%)
What were the highlights of his more recent work (between 2010-2020)?
- Environmental health (11.30%)
- Vaccination (9.57%)
- Demography (6.96%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Environmental health, Vaccination, Demography, Agent-based model and Biostatistics.
He has included themes like Socioeconomic status, Community health and Knowledge management in his Environmental health study.
His work on Measles as part of general Vaccination research is frequently linked to Metropolitan area and Quantitative history, bridging the gap between disciplines.
His Demography research includes themes of Influenza vaccine, Gerontology, Pediatrics and Operations research.
His Agent-based model research incorporates themes from Poverty and Observational study.
His work carried out in the field of Biostatistics brings together such families of science as Statute and State.
Between 2010 and 2020, his most popular works were:
- Contagious Diseases in the United States from 1888 to the Present (175 citations)
- A systematic review of barriers to data sharing in public health (156 citations)
- FRED (A Framework for Reconstructing Epidemic Dynamics): an open-source software system for modeling infectious diseases and control strategies using census-based populations (120 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
The scientist’s investigation covers issues in Environmental health, Agent-based model, Vaccination, Biostatistics and Demography.
John J. Grefenstette combines subjects such as Health promotion, Knowledge management and Health policy with his study of Environmental health.
His work deals with themes such as Observational study and Socioeconomic status, which intersect with Agent-based model.
His work on Herd immunity and Measles as part of general Vaccination study is frequently linked to Quantitative history, Vaccine safety and Public education, therefore connecting diverse disciplines of science.
His Biostatistics research is multidisciplinary, incorporating elements of Closing, Closure, Case fatality rate, Productivity and Pediatrics.
His biological study spans a wide range of topics, including Measles vaccine, Health care and Immunology.
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