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.
Lab Website ↗
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 — Dr. 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.
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).