Noga Zaslavsky

Postdoctoral Fellow @ MIT

About me

I’m a K. Lisa Yang Integrative Computational Neuroscience (ICoN) Postdoctoral Fellow at MIT, where I collaborate with multiple labs including the Computational Psycholinguistics Lab, TEvLab, MetaConscious Group, and the Computational Cognitive Science Group. My research aims to understand language, learning, and reasoning from first principles, building on ideas and methods from machine learning and information theory. I’m particularly interested in finding computational 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, 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 cognition and guide the development of artificial agents that can communicate and collaborate with humans.

Before joining MIT, I completed my PhD under the advisorship of Naftali Tishby at the Center for Brain Sciences at the Hebrew University. I was also a visiting graduate student at UC Berkeley for two years, where I was affiliated with the LCLab, the Simons Institute, and ICSI. Before that, I was a research intern at IBM Project Debater. My BSc is in Computer Science and Cognitive Science from the Hebrew University.

Here’s my PhD Thesis and CV.

Selected Publications

All publications ≫

A Rate–Distortion view of human pragmatic reasoning

Zaslavsky, Hu, Levy. SCiL, 2021 / arXiv, 2020.

Efficient compression in color naming and its evolution

Zaslavsky, Kemp, Regier, Tishby. PNAS, 2018.
ELSC Prize for Outstanding Publication

Deep learning and the Information Bottleneck principle

Tishby and Zaslavsky. IEEE ITW, 2015.

Toward human-like object naming in artificial neural systems

Eisape, Levy, Tenenbaum, Zaslavsky. BAICS @ ICLR, 2020


Mycal Tucker, Roger Levy, Julie Shah, Noga Zaslavsky . Generalization and Translatability in Emergent Communication via Informational Constraints. InfoCog @ NeurIPS, 2022.


Mycal Tucker, Julie Shah, Roger Levy, Noga Zaslavsky . Trading off Utility, Informativeness, and Complexity in Emergent Communication. NeurIPS, 2022.

PDF Code Supplementary Material

Anna A. Ivanova, Martin Schrimpf, Stefano Anzellotti, Noga Zaslavsky, Evelina Fedorenko, Leyla Isik . Beyond linear regression: mapping models in cognitive neuroscience should align with research goals. NBDT, 2022.


Mycal Tucker, Julie Shah, Roger Levy, Noga Zaslavsky . Towards Human-Agent Communication via the Information Bottleneck Principle. RSS Workshop on Social Intelligence in Humans and Robots, 2022.

PDF RSS Workshop

Irene Zhou, Jennifer Hu, Roger Levy, Noga Zaslavsky . Teasing apart models of pragmatics using optimal reference game design. CogSci, 2022.

PDF Code Proceedings

Alicia Chen, Matthias Hofer, Moshe Poliak, Roger Levy, Noga Zaslavsky . The emergence of discrete and systematic communication in a continuous signal-meaning space. CogSci, 2022.

PDF Proceedings

Noga Zaslavsky*, Karee Garvin*, Charles Kemp, Naftali Tishby, Terry Regier . The evolution of color naming reflects pressure for efficiency: Evidence from the recent past. Journal of Language Evolution, 2022.

PDF Code Dataset DOI Preprint

Francis Mollica, Geoffrey Bacon, Noga Zaslavsky, Yang Xu, Terry Regier, Charles Kemp . The forms and meanings of grammatical markers support efficient communication. PNAS, 2021.

PDF Code Dataset DOI Preprint

Jennifer Hu, Roger Levy, Noga Zaslavsky . Scalable pragmatic communication via self-supervision. ICML Workshop on Self-Supervised Learning for Reasoning and Perception, 2021.

PDF ICML Workshop

Noga Zaslavsky*, Mora Maldonado*, Jennifer Culbertson . Let's talk (efficiently) about us: Person systems achieve near-optimal compression. CogSci, 2021.

PDF Proceedings


Language in the Mind and Brain (9.S52), Guest Lecturer

NeuroBridges Summer School, TA

Introduction to Information Processing and Learning, TA