Aviv Netanyahu

I am a PhD student in EECS at MIT CSAIL, advised by Prof. Pulkit Agrawal, as part of the Improbable AI Lab.

I earned my MSc from the Faculty of Mathematics and Computer Science at the Weizmann Institute of Science, advised by Prof. Shimon Ullman, where I worked on Computer Vision. Before that, I received my BSc in Mathematics and Computer Science from the Hebrew University of Jerusalem.

Email  /  Twitter  /  LinkedIn

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Research

I am interested in studying how to deploy agents in new environments with minimum to no supervision. My research focuses, in this context, on inferring how agents interact with the environment and with other agents in ways that are generalizable to new settings. Issues related to this research include combinatorial and out-of-distribution generalization, object-centric learning, and learning from demonstrations.

PontTuset Discovering Generalizable Spatial Goal Representations via Graph-based Active Reward Learning
Aviv Netanyahu*, Tianmin Shu*, Joshua B. Tenenbaum, Pulkit Agrawal
ICML 2022 / ICLR OSC workshop 2022 / RLDM 2022
project page / arXiv

Graph-based one-shot reward learning via active learning for object rearrangement tasks.

PontTuset PHASE: PHysically-grounded Abstract Social Events for Machine Social Perception
Aviv Netanyahu*, Tianmin Shu*, Boris Katz, Andrei Barbu, Joshua B. Tenenbaum
AAAI 2021 / NeurIPS SVRHM workshop 2020 (Oral Presentation, Best Paper Award)
project page / arXiv / talk / code

A dataset for inferring physically grounded social interactions between two agents.

PontTuset Image Interpretation by Iterative Bottom-Up Top-Down Processing
Shimon Ullman, Liav Assif, Alona Strugatski, Ben-Zion Vatashsky, Hila Levi, Aviv Netanyahu, Adam Yaari
under review
arXiv / code

Extraction and representation of scene components, such as objects and their parts, people, and places, individual properties, and relations and interactions between them by combining bottom-up and top-down networks, interacting through a symmetric bi-directional communication between them.

PontTuset Cyclical Bottom-Up Top-Down Neural Networks for Relational Reasoning
MSc thesis 2018

A system for selective visual relationship detection that achieves out-of-distribution and combinatorial generalization.

Teaching
Machine Learning (6.867), MIT Fall 2021
Deep Learning for Control (6.S090), MIT Winter 2021
Logic and Set Theory, Reichman University Spring 2017
Linear Algebra II (80135), Hebrew University of Jerusalem Spring 2016
Service
Organizer Computational Sensorimotor Learning (CSL) Seminar, MIT 2021
Outreach Officer GW6 2020, Lecturer 'Rishonot BaMada' 2017-2019, Coding instructor 'Project Mehamemet' 2016-2019

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