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Daniel Victor Samarov

A native of the Boston, MA area, earned a B.A. in mathematics and economics from Rutgers University, Rutgers College and a Ph.D. in statistics from the University of North Carolina at Chapel Hill (under the advisorship of J.S. Marron, Yufeng Liu and Alexander Trophsha).

Pre-doctoral professional engagements: underwriter for the Chubb group of insurance companies; Teaching Assistant, introductory statistics; Graduate research assistant, Bioinformatics group, Becton Dickinson Technologies (BDT) (under the supervision of Perry Haaland); Consulting statistician, Bioinformatics group, BDT.

Post-doctoral professional engagements: Mathematical Statistician the National Institute of Standards and Technology, Statistical Engineering Division (2009 to present).

Professional interests: machine learning, bioinformatics, multivariate statistics, nonparametric statistics, sparse coding, computational statistics, big data, spatial statistics, blind-source separation, (non-negative) matrix factorization.

Other interests: Rock climbing, CrossFit, swimming, traveling.


Using Replicates in Information Retrieval Evaluation

Ellen M. Voorhees, Daniel V. Samarov, Ian M. Soboroff
This paper explores a method for more accurately estimating the main effect of the system in a typical test-collection-based evaluation of information retrieval
Created July 30, 2019