Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)
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Professor Bashar Alhafni serves as Assistant Professor of Natural Language Processing at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), where he directs the Arabic AI Modeling (Aram) Lab. His research centers on Arabic natural language processing, with particular emphasis on building human-centered language technologies. Specific areas of focus include grammatical error detection and correction, dialectal Arabic text normalization, text simplification, readability assessment, machine translation, and controlled natural language generation. He is especially interested in developing Arabic NLP applications that support education in Arabic and contribute to social good.
Prior to joining MBZUAI, Professor Alhafni earned his Ph.D. in Computer Science from New York University, where he was a member of the Computational Approaches to Modeling Language Lab at New York University Abu Dhabi. His doctoral research focused on controlled Arabic natural language generation with applications in AI for education and social impact, including the development of models and systems for Arabic grammatical error correction, text normalization, and user-centric text rewriting, as well as contributions to open-source tools and language models supporting Arabic NLP research. He holds an M.Sc. in Computer Science from the University of Southern California, where he conducted research at the Information Sciences Institute on low-resource machine translation and event-relation extraction, and a B.Sc. in Computer Science from the University of Bridgeport. Beyond academia, he has conducted applied AI and NLP research at companies including Grammarly and Dataminr, contributing to personalized text generation, multilingual NLP, and extractive summarization technologies.
Dr. Thamar Solorio at MBZUAI receives $1M from Google.org to pioneer resource-lean AI for Arabic dialects, tackling NLP challenges and boosting UAE research leadership.
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