2021 - IEEE Fellow For contributions to machine learning and reasoning under uncertainty
2019 - ACM Fellow For contributions to machine learning, reasoning under uncertainty, and responsible data science
2013 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For significant contributions to methods which combine probabilistic and logical representations in machine learning, knowledge discovery, graph mining, network analysis, and database systems.
Lise Getoor spends much of her time researching Artificial intelligence, Probabilistic logic, Machine learning, Data mining and Statistical relational learning. Lise Getoor interconnects Natural language processing, Structure and Social network in the investigation of issues within Artificial intelligence. Her study in Probabilistic logic is interdisciplinary in nature, drawing from both Probabilistic database, Graphical model, Relational database and Inference.
In her research, Graph theory, Network classification and Viral marketing is intimately related to Collective classification, which falls under the overarching field of Machine learning. The various areas that Lise Getoor examines in her Data mining study include Domain, Graph, Information extraction, Information retrieval and Resolution. Her work deals with themes such as Online machine learning, Unsupervised learning, Instance-based learning, Structured prediction and Relational model, which intersect with Statistical relational learning.
Lise Getoor mainly investigates Artificial intelligence, Probabilistic logic, Machine learning, Data mining and Inference. Lise Getoor has researched Artificial intelligence in several fields, including Graph, Social network, Natural language processing, Relational model and Statistical relational learning. She has included themes like Relational calculus, Data science and Logical conjunction in her Statistical relational learning study.
Her Probabilistic logic research is multidisciplinary, incorporating perspectives in Probabilistic database, Scalability, Theoretical computer science and Graph. As a part of the same scientific family, Lise Getoor mostly works in the field of Data mining, focusing on Information retrieval and, on occasion, Cluster analysis. Her study looks at the relationship between Inference and topics such as Graphical model, which overlap with Markov chain.
Lise Getoor mainly focuses on Artificial intelligence, Probabilistic logic, Statistical relational learning, Inference and Machine learning. Her Natural language processing research extends to the thematically linked field of Artificial intelligence. Her Probabilistic logic study combines topics from a wide range of disciplines, such as Similarity, Theoretical computer science, Recommender system and Human–computer interaction.
The concepts of her Statistical relational learning study are interwoven with issues in Graphical model and Data science. Her biological study spans a wide range of topics, including Structure, Structured prediction and Data mining. Her studies in Data mining integrate themes in fields like Graph and Information retrieval.
Her primary areas of study are Probabilistic logic, Artificial intelligence, Inference, Theoretical computer science and Machine learning. Her Probabilistic logic research includes elements of Graphical model, Web service, Human–computer interaction and Personalization. Lise Getoor combines subjects such as Intersection and Software system with her study of Artificial intelligence.
Her Inference research integrates issues from Affect, Structured prediction, Mathematical optimization, Generalization and Statistical relational learning. As part of the same scientific family, Lise Getoor usually focuses on Theoretical computer science, concentrating on Scalability and intersecting with Metadata. Lise Getoor works mostly in the field of Machine learning, limiting it down to topics relating to Similarity and, in certain cases, Transitive relation, as a part of the same area of interest.
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Collective Classification in Network Data
Prithviraj Sen;Galileo Namata;Mustafa Bilgic;Lise Getoor.
Ai Magazine (2008)
Introduction to statistical relational learning
Lise Getoor;Ben Taskar.
Learning Probabilistic Relational Models
Nir Friedman;Lise Getoor;Daphne Koller;Avi Pfeffer.
international joint conference on artificial intelligence (1999)
Link mining: a survey
Lise Getoor;Christopher P. Diehl.
Sigkdd Explorations (2005)
Collective entity resolution in relational data
Indrajit Bhattacharya;Lise Getoor.
ACM Transactions on Knowledge Discovery From Data (2007)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Lise Getoor;Ben Taskar.
To join or not to join: the illusion of privacy in social networks with mixed public and private user profiles
Elena Zheleva;Lise Getoor.
the web conference (2009)
Qing Lu;Lise Getoor.
international conference on machine learning (2003)
Preserving the privacy of sensitive relationships in graph data
Elena Zheleva;Lise Getoor.
knowledge discovery and data mining (2007)
Entity resolution: theory, practice & open challenges
Lise Getoor;Ashwin Machanavajjhala.
very large data bases (2012)
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