Rate My Professor Charles Kemp

Kemp's research centers on computational models of learning, reasoning, and communication, with a particular emphasis on analyzing linguistic and cultural variation across languages. His work explores categorization, generalization, causal reasoning, and relational learning, utilizing tools from information theory, natural language processing, artificial intelligence, and statistics applied to large datasets such as dictionaries and linguistic corpora. He has received several honors, including the Pragnesh Jay Modi Best Student Paper Award in 2024, the Disciplinary Diversity and Integration Award in 2023, prizes for computational modeling of language in 2018 and 2010, and an honorable mention for the Outstanding Student Paper award in 2007. Key publications include Kemp (2026), Symmetry in category systems across languages, Nature Communications; Khishigsuren, Regier, Vylomova, & Kemp (2025), A computational analysis of lexical elaboration across languages, Proceedings of the National Academy of Sciences; Lim et al. (2024), A computational approach to identifying cultural keywords across languages, Cognitive Science; Kemp, Hamacher, Little, & Cropper (2022), Perceptual grouping explains similarities in constellations across cultures, Psychological Science; Mollica et al. (2021), The forms and meanings of grammatical markers support efficient communication, Proceedings of the National Academy of Sciences; Zaslavsky, Kemp, Regier, & Tishby (2018), Efficient compression in color naming and its evolution, Proceedings of the National Academy of Sciences; Kemp & Regier (2012), Kinship categories across languages reflect general communicative principles, Science; and Tenenbaum, Kemp, Griffiths, & Goodman (2011), How to grow a mind: statistics, structure and abstraction, Science. His contributions appear in leading journals, advancing understanding in cognitive science and computational psychology.