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My main research focus is on entity resolution (record linkage or de-duplication), where the goal is to remove duplicated information from large, noisy databases in the absence of unique identifiers. In my research, I develop flexible methods for entity resolution that are able to handle the uncertainty of the record linkage process and can be easily integrated with post-linkage statistical analyses, such as logistic regression or capture recapture. In addition, a strength of the methods I propose, is that they are able to maintain low error rates (precision and recall) and beat the state-of-the-art methods in the literature in terms of these error rates. Furthermore, I have developed the first performance bounds for a general class of entity resolution models, illustrating when the bounds hold in practice. I proposed a new methodology for entity resolution, realizing that the size of the clusters grows sub-linearly compared to the number of records, which contrasts with many other processes. In turn, this had led to proposing a general class of models for clustering of tasks with a sub-linear growth that are scalable, and illustrating their success for entity resolution.
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Journal of Survey Statistics and Methodologyno. 3 (2023): 513-517
Wiley StatsRef: Statistics Reference Onlinepp.1-9, (2022)
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