Yossi Gandelsman

I am a Member of Technical Staff at OpenAI, working on generative models and multimodal understanding, and an assistant professor at TTIC (on leave). Previously, I was an AI researcher at Reve.

I received my Ph.D. from UC Berkeley, where I was advised by Prof. Alexei Efros, and my M.Sc. from the Weizmann Institute of Science, where I was advised by Prof. Michal Irani.

During my Ph.D., I spent time as a Research Scientist at Meta, Adobe, and Transluce. Before starting my Ph.D., I was a member of the Perception Team at Google Research (now Google DeepMind).

Selected Publications

Please visit my google scholar for the full list of publications


LeSAMP qualitative image-generation comparisons

Learning Sampling Parameters for Diffusion Models

  • Arisrei Lim and Yossi Gandelsman
  • Preprint
  • paper
Qualitative test-time-training trajectories for vision-language models

Test-Time Training for Modality Order Consistency in Vision-Language Models

DuSPiT qualitative comparison with pixel-space diffusion baselines

DuSPiT: Dual-Branch Sub-Patch Pixel Diffusion Transformer

  • Yunpeng Bai, Yossi Gandelsman and Michaël Gharbi
  • Preprint
  • paper | code

Neuron Populations Exhibit Divergent Selectivity with Scale

Jailbreaking Vision-Language Models Through the Visual Modality

  • Aharon Azulay*, Jan Dubinski*, Zhuoyun Li*, Atharv Mittal* and Yossi Gandelsman
  • ICML 2026
  • paper | project page | code

The Unreasonable Effectiveness of Text Embedding Interpolation for Continuous Image Steering

In-Context Representation Hijacking

Vision Transformers Don't Need Trained Registers

Same Task, Different Circuits: Disentangling Modality-Specific Mechanisms in VLMs

Interpreting the Repeated Token Phenomenon in Large Language Models

  • Itay Yona, Jamie Hayes, Ilia Shumailov, Federico Barbero and Yossi Gandelsman
  • ICML 2025
  • paper | code

Interpreting and Editing Vision-Language Representations
to Mitigate Hallucinations

  • Nick Jiang*, Anish Kachinthaya*, Suzie Petryk† and Yossi Gandelsman†
  • ICLR 2025
  • paper | project page | code

Interpreting the Second-Order Effects of Neurons in CLIP

Interpreting the Weight Space of Customized Diffusion Models

The More You See in 2D, the More You Perceive in 3D

  • Xinyang Han*, Zelin Gao*, Angjoo Kanazawa, Shubham Goel† and Yossi Gandelsman†
  • CVPR 2024 [Spotlight]
  • paper | project page | code

Interpreting CLIP's Image Representation via
Text-Based Decomposition

Rosetta Neurons: Mining the Common Units in a Model Zoo

  • Amil Dravid*, Yossi Gandelsman*, Alexei A. Efros and Assaf Shocher
  • ICCV 2023
  • paper | project page | code

Test-Time Training with Masked Autoencoders

Visual Prompting via Image Inpainting

  • Amir Bar*, Yossi Gandelsman*, Trevor Darrell, Amir Globerson and Alexei A. Efros
  • NeurIPS 2022
  • paper | project page | code

MyStyle: A Personalized Generative Prior

  • Yotam Nitzan, Kfir Aberman, Qiurui He, Orly Liba, Michal Yarom, Yossi Gandelsman, Inbar Mosseri, Yael Pritch and Daniel Cohen-Or
  • SIGGRAPH Asia 2022 (journal track)
  • paper | project page | video

Deep ViT Features as Dense Visual Descriptors

  • Shir Amir, Yossi Gandelsman, Shai Bagon and Tali Dekel
  • ECCVW 2022 ["WIMF" Best Spotlight Presentation]
  • paper | project page | code

Explaining in Style:
Training a GAN to explain a classifier in StyleSpace

  • Oran Lang*, Yossi Gandelsman*, Michal Yarom*, Yoav Wald*, Gal Elidan, Avinatan Hassidim, William T. Freeman, Phillip Isola, Amir Globerson, Michal Irani and Inbar Mosseri
  • ICCV 2021
  • paper | project page | code | video | blog post

Semantic Pyramid for Image Generation

  • Assaf Shocher*, Yossi Gandelsman*, Inbar Mosseri, Michal Yarom, Michal Irani, William T. Freeman and Tali Dekel
  • CVPR 2020 [Oral]
  • paper | project page | video

Double-DIP: Unsupervised Image Decomposition via
Coupled Deep-Image-Priors