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His main research interests revolve around human computation and its application to data management, data processing, machine learning, natural language processing and knowledge extraction. Specifically, he investigates novel active learning techniques, Bayesian models for truth inference and task scheduling optimization problems. Djellel worked for Schlumberger and Microsoft and is an ACM member and a Fulbright Alumni. He is currently a Moore-Sloan Fellow at the Center for Data Science at New York University.
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PROCEEDINGS OF THE 46TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, SIGIR 2023pp.781-790, (2023)
arxiv(2021)
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