My research aims to understand the computational principles that underlie language, learning, and reasoning, building on ideas and methods from machine learning and information theory. I’m particularly interested in finding optimality principles that explain how we use language to represent the environment; how this representation can be learned in humans and in artificial neural networks; how it interacts with other cognitive functions, such as perception, action, social reasoning, and decision making; and how it evolves over time and adapts to changing environments and social needs. I believe that such principles could advance our understanding of human and artificial cognition, as well as guide the development of interactive artificial agents that are more human-like, interpretable, and data efficient.

Areas 

➡️ Cognitive Science

➡️ Computational Linguistics

➡️ Information Theory

➡️ Machine Learning

➡️ AI

Profile: https://as.nyu.edu/faculty/noga-zaslavsky.html