I am Adrián Rodríguez-Muñoz, a 4th year grad student at MIT EECS under the supervision of Prof. Antonio Torralba. My research focuses on learning how to use all data effectively, such as low-quality and out-of-distribution data in generative models, and even procedurally generated data in vision models. Previously, I worked on efficient adversarial robustness. In Summer 2025 I did an internship at Amazon Research, working on retrieval augmented generation (RAG) with diffusion models.

Prior to my PhD, I studied Mathematics and Data Science and Engineering at CFIS-UPC until 2022. During my undergrad, I did internships at the Barcelona Supercomputing Center, the biotech company ZeClinics, and the finance firms Aspect Capital (London) and Susquehanna International Group (Dublin).

Click here for my CV.

Publications

[Neurips 2025 - Spotlight ] Ambient Diffusion Omni: Training Good Models with Bad Data

Adrián Rodríguez-Muñoz*, Giannis Daras*, Adam Klivans, Antonio Torralba, Constantinos Daskalakis

[paper] [webpage] [code]

[ICML 2025] Separating Knowledge and Perception with Procedural Data

Adrián Rodríguez-Muñoz, Manel Baradad, Phillip Isola, Antonio Torralba

[paper] [webpage] [code]

[ECCV 2024] Characterizing Model Robustness with Natural Input Gradients

Adrián Rodríguez-Muñoz, Tongzhou Wang, Antonio Torralba

[paper] [webpage] [code]

[CVPR 2024 - Highlight ] A Vision Check-up for Language Models

Pratyusha Sharma*, Tamar Rott Shaham*, Manel Baradad, Stephanie Fu, Adrián Rodríguez-Muñoz, Shivam Duggal, Phillip Isola, Antonio Torralba

[paper] [webpage]

Aliasing is a Driver of Adversarial Attacks

Adrián Rodríguez-Muñoz, Antonio Torralba

[paper] [webpage] [code]