What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Statistics
- Machine learning
Barbara S. Minsker focuses on Genetic algorithm, Mathematical optimization, Data mining, Evolutionary computation and Bayesian network.
Her work deals with themes such as Sampling, Probabilistic-based design optimization and Water resource management, which intersect with Genetic algorithm.
In her study, Interpolation is inextricably linked to Kriging, which falls within the broad field of Mathematical optimization.
Anomaly detection and Data stream mining are among the areas of Data mining where the researcher is concentrating her efforts.
Her Evolutionary computation study combines Machine learning and Artificial intelligence studies.
Her Bayesian network research is multidisciplinary, relying on both Potable water, Contamination and Bayesian probability.
Her most cited work include:
- State of the Art for Genetic Algorithms and Beyond in Water Resources Planning and Management (418 citations)
- Evolutionary algorithms and other metaheuristics in water resources (337 citations)
- Striking the Balance: Long-Term Groundwater Monitoring Design for Conflicting Objectives (175 citations)
What are the main themes of her work throughout her whole career to date?
Barbara S. Minsker spends much of her time researching Mathematical optimization, Genetic algorithm, Groundwater, Data mining and Groundwater remediation.
Her work in the fields of Mathematical optimization, such as Pareto principle, Multi-objective optimization and Meta-optimization, overlaps with other areas such as Nonlinear system.
The Genetic algorithm study combines topics in areas such as Sampling, Field and Local search, Artificial intelligence.
Her Sampling research integrates issues from Software, Kriging and Interpolation.
Her work on Aquifer as part of general Groundwater research is often related to Term, thus linking different fields of science.
Her Data mining research incorporates elements of Dynamic Bayesian network, Bayesian network and Bayesian probability.
She most often published in these fields:
- Mathematical optimization (28.16%)
- Genetic algorithm (23.56%)
- Groundwater (13.79%)
What were the highlights of her more recent work (between 2011-2019)?
- Decision support system (6.32%)
- Green infrastructure (2.87%)
- Environmental resource management (6.32%)
In recent papers she was focusing on the following fields of study:
Her primary areas of study are Decision support system, Green infrastructure, Environmental resource management, Combined sewer and Cyberinfrastructure.
Her biological study spans a wide range of topics, including Evolutionary algorithm, Machine learning and Model predictive control.
Barbara S. Minsker has researched Model predictive control in several fields, including Genetic algorithm, Computational resource and Operations research.
Her work in Environmental resource management addresses subjects such as Probabilistic logic, which are connected to disciplines such as Flood myth.
Her studies in Mathematical optimization integrate themes in fields like Range and Constraint.
Her study in the field of Evolutionary computation is also linked to topics like Spatial extent and Multi criteria.
Between 2011 and 2019, her most popular works were:
- Evolutionary algorithms and other metaheuristics in water resources (337 citations)
- Interactive Genetic Algorithm with Mixed Initiative Interaction for multi-criteria ground water monitoring design (52 citations)
- Patterns of regional hydroclimatic shifts: An analysis of changing hydrologic regimes (34 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
Her scientific interests lie mostly in Artificial intelligence, Machine learning, Field, Sustainability and Evolutionary computation.
Her research in the fields of Decision support system and Perceptron overlaps with other disciplines such as Heavy equipment and Dryness.
Her Machine learning research is multidisciplinary, incorporating perspectives in Terrain and Water content.
Her Field study incorporates themes from Management science and Big data.
Among her research on Sustainability, you can see a combination of other fields of science like Knowledge management, Document classification, Region specific, Premise and Text mining.
Her study in Evolutionary computation is interdisciplinary in nature, drawing from both Genetic algorithm and Transient.
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