2023 - Research.com Best Female Scientist Award
2023 - Research.com Computer Science in United States Leader Award
2022 - Research.com Best Female Scientist Award
2019 - ACM AAAI Allen Newell Award For seminal contributions to machine learning and probabilistic models, the application of these techniques to biology and human health, and for contributions to democratizing education.
2014 - Fellow of the American Academy of Arts and Sciences
2011 - Member of the National Academy of Engineering For contributions to representation, inference, and learning in probabilistic models with applications to robotics, vision, and biology.
2007 - ACM Prize in Computing For her work on combining relational logic and probability that allows probabilistic reasoning to be applied to a wide range of applications, including robotics, economics, and biology.
2004 - Fellow of the MacArthur Foundation
2004 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For significant contributions to the integration of logic and probability and development of methods for reasoning, learning, and decision making under uncertainty.
1996 - Fellow of Alfred P. Sloan Foundation
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.
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.
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.
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.
The Genotype-Tissue Expression (GTEx) project
John Lonsdale;Jeffrey Thomas;Mike Salvatore;Rebecca Phillips.
Nature Genetics (2013)
The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans
Kristin G. Ardlie;David S. Deluca;Ayellet V. Segrè.
Support vector machine active learning with applications to text classification
Simon Tong;Daphne Koller.
Journal of Machine Learning Research (2002)
FastSLAM: a factored solution to the simultaneous localization and mapping problem
Michael Montemerlo;Sebastian Thrun;Daphne Koller;Ben Wegbreit.
national conference on artificial intelligence (2002)
A Gene-Coexpression Network for Global Discovery of Conserved Genetic Modules
Joshua M. Stuart;Eran Segal;Daphne Koller;Stuart K. Kim.
Toward optimal feature selection
Daphne Koller;Mehran Sahami.
international conference on machine learning (1996)
Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data
Eran Segal;Michael Shapira;Aviv Regev;Aviv Regev;Dana Pe'er.
Nature Genetics (2003)
Max-Margin Markov Networks
Ben Taskar;Carlos Guestrin;Daphne Koller.
neural information processing systems (2003)
SCAPE: shape completion and animation of people
Dragomir Anguelov;Praveen Srinivasan;Daphne Koller;Sebastian Thrun.
international conference on computer graphics and interactive techniques (2005)
The Immunological Genome Project: networks of gene expression in immune cells
Tracy S P Heng;Michio W Painter;Kutlu Elpek;Veronika Lukacs-Kornek.
Nature Immunology (2008)
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