Language and Cognition
Building systems that understand, generate, and reason about language.
Our NLP and cognitive science labs work on the foundations of how machines and humans handle meaning, from large language models and multilingual systems to causal reasoning, child language acquisition, and linguistic typology.
Labs in Language and Cognition
Schwartz NLP Lab, Prof. Roy Schwartz
Leads research in natural language processing with emphasis on model reliability, efficiency, and sustainability, spanning language understanding, generation, and practical AI tools.
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Feder Lab, Dr. Amir Feder
Combines causal inference with NLP to study how text can be used in causal estimation, structured reasoning, and decision-making under uncertainty.
SLAB, Prof. Gabriel Stanovsky
Gabi's lab develops natural language processing models which deal with real-world texts and help answer multi-disciplinary research questions, e.g., in archaeology, law, medicine, and more. In doing so, we deal with various challenges, including low-resource settings, multilingual modelling, and processing of large document collections.
NLP and Cognitive Science, Prof. Omri Abend
The lab focuses on semantic (meaning) representation from a computational perspective. Our research is tightly linked to machine learning and language technology, such as large language models and Bayesian modeling, but takes a basic-science approach, connecting it with core questions in child language acquisition, linguistic typology and the structure of narratives.
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Hope Lab, Dr. Tom Hope
Develops LLM-based agents for scientific discovery, medical diagnosis, and psychiatric applications. Also a research scientist at the Allen Institute for AI (Ai2).
Lab Website ↗