What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Machine learning
- Statistics
Her primary scientific interests are in Artificial intelligence, Machine learning, Genetics, Computer vision and Probabilistic logic.
Her study brings together the fields of Pattern recognition and Artificial intelligence.
Gene, Gene expression, Expression quantitative trait loci, Regulation of gene expression and Genetic variation are the subjects of her Genetics studies.
Her study in Expression quantitative trait loci is interdisciplinary in nature, drawing from both Quantitative trait locus, Immune system and Genotype-Tissue Expression.
Her Computer vision study which covers Kalman filter that intersects with Simultaneous localization and mapping, Mobile robot and Robot.
Her studies deal with areas such as Relational database, Data mining, Statistical relational learning and Statistical model as well as Probabilistic logic.
Her most cited work include:
- The Genotype-Tissue Expression (GTEx) project (4397 citations)
- The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans (3327 citations)
- Support vector machine active learning with applications to text classification (2490 citations)
What are the main themes of her work throughout her whole career to date?
Daphne Koller spends much of her time researching Artificial intelligence, Machine learning, Bayesian network, Algorithm and Inference.
Daphne Koller has researched Artificial intelligence in several fields, including Computer vision and Pattern recognition.
A large part of her Machine learning studies is devoted to Unsupervised learning.
Her Bayesian network research integrates issues from Variable-order Bayesian network, Stochastic process, Theoretical computer science and Markov process.
Her research in Algorithm focuses on subjects like Mathematical optimization, which are connected to Markov decision process.
Her work deals with themes such as Data mining, Statistical model and Statistical relational learning, which intersect with Probabilistic logic.
She most often published in these fields:
- Artificial intelligence (45.62%)
- Machine learning (18.83%)
- Bayesian network (17.77%)
What were the highlights of her more recent work (between 2010-2020)?
- Artificial intelligence (45.62%)
- Bayesian network (17.77%)
- Pattern recognition (9.02%)
In recent papers she was focusing on the following fields of study:
Daphne Koller mainly focuses on Artificial intelligence, Bayesian network, Pattern recognition, Machine learning and Algorithm.
Her Artificial intelligence study combines topics in areas such as Computer vision and TRECVID.
She has included themes like Probabilistic logic, Theoretical computer science, Inference and Markov process in her Bayesian network study.
Her research investigates the connection with Pattern recognition and areas like Latent variable which intersect with concerns in Conditional probability distribution.
Her Machine learning study combines topics in areas such as Variation, Biobank, Cross-sectional data and Face.
Daphne Koller interconnects Process, Representation, Set and Posterior probability in the investigation of issues within Algorithm.
Between 2010 and 2020, her most popular works were:
- The Genotype-Tissue Expression (GTEx) project (4397 citations)
- The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans (3327 citations)
- Systematic analysis of breast cancer morphology uncovers stromal features associated with survival. (498 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Daphne Koller focuses on Genetics, Artificial intelligence, Immune system, Gene and Pattern recognition.
Her Artificial intelligence research incorporates elements of Machine learning, Computer vision and TRECVID.
In her research on the topic of Gene, Regulation of gene expression, Identification and Genome is strongly related with Computational biology.
Her work carried out in the field of Pattern recognition brings together such families of science as Object detection, Latent variable, Statistical model and Monocular camera.
Her work in Expression quantitative trait loci addresses subjects such as Genotype-Tissue Expression, which are connected to disciplines such as Biomedicine, Human disease, Underlying disease, Genetic association and Tissue bank.
Her study explores the link between Graphical model and topics such as Active shape model that cross with problems in Probabilistic logic.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
