Paul N. Bennett

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Senior Researcher, Context, Learning, and User Experience for Search (CLUES), Microsoft Research

E-mail: paul.n.bennett@microsoft.com
Mail: One Microsoft Way, Redmond WA 98052-6399, USA

Research Activities:

I am interested in the development, improvement, and analysis of machine learning methods with a focus on systems that can aid in the automatic analysis of natural language as components of adaptive systems or information retrieval systems. My current focus is on contextual and personalized search. I am also actively engaged in enriched information retrieval, active sampling and learning, hierarchical and large-scale classification, and human computation and preferences.

My past work has examined a variety of areas — primarily ensemble methods, calibrating classifiers, search query classification and characterization, and redundancy and diversity, but also extending to transfer learning, machine translation, recommender systems, and knowledge bases. In addition to my research, I engage in a variety of professional service activities for the machine learning, data mining, and information retrieval communities.

While at Microsoft Research, I have been lucky enough to work with a variety of great interns — either as primary mentor or in broader collaborations.

Before coming to Microsoft, I obtained my Ph.D. from the Computer Science Department at Carnegie Mellon under Jaime Carbonell and John Lafferty.   Prior to that I worked with Ray Mooney and Robert Causey during my undergraduate days in the Computer Science, Philosophy, and Plan II Honors departments at the University of Texas at Austin.

 With colleagues from Microsoft and elsewhere, I am currently a Track Co-organizer of the TREC 2014 Web Track. Also, we are looking for great contributions for the TOIS Special Issue on Contextual Search and Recommendation.




Publications and presentations by primary subject (or by year).

Contextual and Personalized Search Active Learning, Active Sampling, and Active Evaluation Human Computation and Preferences Text Classification, Text Analysis, and Hierarchical Classification
Robust Ensembles of Classifiers and Rankers Calibration Transfer Learning Recommender Systems
Machine Translation Other Information Retrieval: Change, Redundancy, Diversity, Query Performance Other Publications

Contextual and Personalized Search

Active Learning, Active Sampling, and Active Evaluation

Human Computation and Preferences

Text Classification, Text Analysis, and Hierarchical Classification

Robust Ensembles of Classifiers and Rankers

Calibration

Transfer Learning

Recommender Systems

Machine Translation

Other Information Retrieval: Change, Redundancy, Diversity, Query Performance

Other Publications:



Professional Service Activities

Tutorials:

Reviewing and Organizational Service:


Former Intern Collaborators


Last Updated: 04/15/2014
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