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 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.
2004 - Fellow of the MacArthur Foundation
1996 - Fellow of Alfred P. Sloan Foundation
The scientist’s investigation covers issues in Artificial intelligence, Machine learning, Genetics, Probabilistic logic and Computer vision. Her Artificial intelligence study frequently draws connections between adjacent fields such as Pattern recognition. Her is doing research in Gene, Gene expression, Genome, Expression quantitative trait loci and Genetic variation, both of which are found in Genetics.
Her Gene expression study integrates concerns from other disciplines, such as Regulator and Transcription factor. Her Probabilistic logic research includes themes of Relational database, Data mining, Statistical relational learning and Statistical model. The study incorporates disciplines such as Kalman filter, Robot and Simultaneous localization and mapping in addition to Computer vision.
Daphne Koller mainly focuses on Artificial intelligence, Machine learning, Bayesian network, Algorithm and Probabilistic logic. Daphne Koller works mostly in the field of Artificial intelligence, limiting it down to topics relating to Computer vision and, in certain cases, Robot. Her Machine learning study frequently involves adjacent topics like Classifier.
Her research on Bayesian network also deals with topics like
Daphne Koller mostly deals with Artificial intelligence, Bayesian network, Algorithm, Inference and Genetics. Her Artificial intelligence study combines topics from a wide range of disciplines, such as TRECVID, Machine learning, Computer vision and Pattern recognition. Her work on Discriminative model as part of general Machine learning study is frequently linked to Human health, therefore connecting diverse disciplines of science.
Daphne Koller has included themes like Probabilistic logic, Theoretical computer science, Markov process and Markov chain in her Bayesian network study. She has researched Algorithm in several fields, including Process, Representation, Set and Posterior probability. Her work on Approximate inference as part of general Inference study is frequently connected to Belief propagation and Bounded function, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
Her main research concerns Genetics, Artificial intelligence, Immune system, Gene and Computational biology. Her Artificial intelligence research is multidisciplinary, incorporating perspectives in TRECVID, Computer vision and Pattern recognition. Her Computational biology study incorporates themes from Regulation of gene expression and Gene expression, Transcription factor, Transcriptional regulation.
Her Gene expression research incorporates elements of Expression quantitative trait loci and Genetic association. While the research belongs to areas of Graphical model, Daphne Koller spends her time largely on the problem of Active shape model, intersecting her research to questions surrounding Probabilistic logic. Her studies in Object detection integrate themes in fields like Machine learning and Discriminative model.
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) pilot analysis: Multitissue gene regulation in humans
Kristin G. Ardlie;David S. Deluca;Ayellet V. Segrè.
The Genotype-Tissue Expression (GTEx) project
John Lonsdale;Jeffrey Thomas;Mike Salvatore;Rebecca Phillips.
Nature Genetics (2013)
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)
Learning Probabilistic Relational Models
Nir Friedman;Lise Getoor;Daphne Koller;Avi Pfeffer.
international joint conference on artificial intelligence (1999)
SCAPE: shape completion and animation of people
Dragomir Anguelov;Praveen Srinivasan;Daphne Koller;Sebastian Thrun.
international conference on computer graphics and interactive techniques (2005)
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