Daniel Khashabi is an assistant professor of computer science and a member of the Center for Language and Speech Processing.
Khashabi’s work focuses on the computational foundations of intelligent behavior within various mediums of communication, particularly natural language. This involves developing formalisms that characterize and result in natural language processing systems that are capable of understanding and reasoning with—and about—an uncertain world while being general enough to handle a broader space of contexts.
He obtained a PhD (2019) from the University of Pennsylvania and a BSc (2012) from Amirkabir University of Technology (Tehran Polytechnic). Before joining Johns Hopkins, he was a postdoctoral fellow at the Allen Institute for AI from 2019 to 2022.
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The Conference on Language Modeling is an academic venue with the goal of creating a community of researchers focused on understanding, improving, and critiquing the development of language modeling technology.
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A Johns Hopkins study finds that large language models are more likely to generate irrelevant or harmful responses when operating in underrepresented languages.
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In AI we trust?
CategoriesGiven AI models’ ubiquity and influence, how and why should we trust their decisions? Can we be certain their predictions are free of biases or errors? Johns Hopkins AI experts discuss.