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Noga Zaslavsky

BCS Fellow in Computation, MIT

About me

I’m a BCS Fellow in Computation at the MIT Department of Brain and Cognitive Sciences, where I collaborate with several labs including the Computational Psycholinguistics Lab, EvLab, TedLab, and the Computational Cognitive Science Group. My research aims to understand language and cognition from first principles, building on ideas and methods from machine learning and information theory. I’m particularly interested in computational principles that can account for the ability to maintain efficient semantic representations for learning and communication in complex environments. I believe that such principles could advance our understanding of human cognition and guide the development of human-like artificial intelligence.

Before joining MIT, I did my PhD under the supervision 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 is my PhD Thesis and my CV.

Selected Publications

All publications ≫

A Rate–Distortion view of human pragmatic reasoning

Zaslavsky, Hu, and Levy. arXiv, 2020.

Efficient compression in color naming and its evolution

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

Toward human-like object naming in artificial neural systems

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

Semantic categories of artifacts and animals reflect efficient coding

Zaslavsky, Regier, Tishby and Kemp. CogSci 2019.

Color naming reflects both perceptual structure and communicative need

Zaslavsky, Kemp, Tishby and Regier. topiCS, 2019.
CogSci Computational Modeling Prize

Deep learning and the Information Bottleneck principle

Tishby and Zaslavsky. IEEE ITW, 2015.

Teaching

I served as a TA in the following courses at the Hebrew University:

  • Introduction to Machine Learning – undergraduate-level course at the CS department. I also co-organized a machine learning hackathon for this course.

  • Introduction to Information Processing and Learning – graduate-level course, covering topics in machine learning, information theory, and statistics.