Faezeh Ensan
Assistant Professor
Director of NSERC CREATE Program on the Responsible Development of AI (RAI)
Faculty of Engineering and Architectural Science
Department of Electrical, Computer, and Biomedical Engineering
Toronto, Ontario
fensan@torontomu.ca
Office:
(416) 979-5000 ext. 554904
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Bio/Research
When Faezeh Ensan began her bachelor’s studies in computer science, her goal was to become skilled in programming and software development. During her graduate studies, though, those goals evolved, and Ensan began working in the field of natural language processing. That eventually led her to tod...
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Bio/Research
When Faezeh Ensan began her bachelor’s studies in computer science, her goal was to become skilled in programming and software development. During her graduate studies, though, those goals evolved, and Ensan began working in the field of natural language processing. That eventually led her to today’s field of research: data science and information retrieval.
Her research focuses on building exciting tools that have shaped the information revolution, such as search engines, text retrieval techniques, automatic question-answering systems and recommendation engines. Contrary to traditional methods for information retrieval, Ensan applies computational semantics by capturing the meaning behind natural language to build next generation intelligent information processing techniques.
Knowledge graphs, formal ontologies and linked open data are at the heart of Ensan’s research program, and enable the semantic interpretation of textual content. To infer more meaning from larger volumes of text, Ensan has shifted her focus from manually created ontologies to crowdsourced information sources, like Wikipedia, and structured and formal representations of their knowledge in knowledge graphs. “By semantically modelling textual content through structured, interlinked data sources, we can gain greater insight into the endless streams of daily generated data," says Ensan.
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Her research focuses on building exciting tools that have shaped the information revolution, such as search engines, text retrieval techniques, automatic question-answering systems and recommendation engines. Contrary to traditional methods for information retrieval, Ensan applies computational semantics by capturing the meaning behind natural language to build next generation intelligent information processing techniques.
Knowledge graphs, formal ontologies and linked open data are at the heart of Ensan’s research program, and enable the semantic interpretation of textual content. To infer more meaning from larger volumes of text, Ensan has shifted her focus from manually created ontologies to crowdsourced information sources, like Wikipedia, and structured and formal representations of their knowledge in knowledge graphs. “By semantically modelling textual content through structured, interlinked data sources, we can gain greater insight into the endless streams of daily generated data," says Ensan.
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