What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Statistics
- Machine learning
Data mining, Artificial intelligence, Machine learning, Relational database and Statistical relational learning are her primary areas of study.
The study incorporates disciplines such as Class, Homophily and Dynamic network analysis in addition to Data mining.
Her work on Learning classifier system, Active learning and Value of information as part of general Artificial intelligence study is frequently connected to User modeling, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
Her Machine learning study combines topics from a wide range of disciplines, such as Dependency, Inference and Social network.
Her Relational database research includes themes of Mode, Tree, Statistical model, Pattern recognition and Autocorrelation.
The various areas that she examines in her Statistical relational learning study include Relational knowledge and Commit.
Her most cited work include:
- Modeling relationship strength in online social networks (592 citations)
- Iterative Classification in Relational Data (297 citations)
- Relational Dependency Networks (271 citations)
What are the main themes of her work throughout her whole career to date?
Her main research concerns Artificial intelligence, Machine learning, Data mining, Statistical relational learning and Relational database.
The Inference and Ensemble learning research Jennifer Neville does as part of her general Artificial intelligence study is frequently linked to other disciplines of science, such as Task, therefore creating a link between diverse domains of science.
Her studies examine the connections between Machine learning and genetics, as well as such issues in Statistical hypothesis testing, with regards to Statistical power and Anomaly detection.
Jennifer Neville interconnects Theoretical computer science, Social network, Sampling, Class and Graph in the investigation of issues within Data mining.
Her work is dedicated to discovering how Statistical relational learning, Graph are connected with Information retrieval and other disciplines.
Her research in Relational database intersects with topics in External Data Representation, Data model, Synthetic data and Autocorrelation.
She most often published in these fields:
- Artificial intelligence (48.45%)
- Machine learning (40.99%)
- Data mining (37.27%)
What were the highlights of her more recent work (between 2016-2021)?
- Artificial intelligence (48.45%)
- Machine learning (40.99%)
- Social network (12.42%)
In recent papers she was focusing on the following fields of study:
Her primary scientific interests are in Artificial intelligence, Machine learning, Social network, Theoretical computer science and Cluster analysis.
Her Artificial intelligence research integrates issues from Natural language processing and Statistical relational learning.
As part of one scientific family, Jennifer Neville deals mainly with the area of Machine learning, narrowing it down to issues related to the Synthetic data, and often Type I and type II errors.
Her Social network research is multidisciplinary, incorporating elements of Feature learning and Information retrieval.
Jennifer Neville combines subjects such as Embedding, Inference, Probabilistic logic, Graph and Node with her study of Theoretical computer science.
She has included themes like Domain, Normalization and Decision tree, Data mining in her Cluster analysis study.
Between 2016 and 2021, her most popular works were:
- Graphlet decomposition: framework, algorithms, and applications (53 citations)
- Exploring Student Check-In Behavior for Improved Point-of-Interest Prediction (24 citations)
- Goodness-of-Fit Testing for Discrete Distributions via Stein Discrepancy (22 citations)
In her most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Machine learning
Her primary areas of study are Theoretical computer science, Goodness of fit, Gaussian process, Graph and Node.
Her research integrates issues of Artificial neural network, Embedding and Inference in her study of Theoretical computer science.
Her Artificial neural network research is included under the broader classification of Artificial intelligence.
Her Goodness of fit study integrates concerns from other disciplines, such as Normalization, Applied mathematics and Point process.
Her study looks at the relationship between Graph and fields such as Information retrieval, as well as how they intersect with chemical problems.
The concepts of her Node study are interwoven with issues in Computational complexity theory, Graph neural networks and Reduction.
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