Portrait of Adriana Romero Soriano

Adriana Romero Soriano

Core Industry Member
Canada CIFAR AI Chair
Adjunct professor, McGill University
Research Scientist, Meta AI Research (FAIR)
Research Topics
Computer Vision
Deep Learning
Generative Models

Biography

Adriana Romero-Soriano is a research scientist in the Fundamental AI Research (FAIR) team at Meta, adjunct professor at McGill University, core industry member of Mila – Quebec Artificial Intelligence Institute and a Canada CIFAR AI Chair.

Romero-Soriano’s research lies at the intersection of generative models, computer vision and responsible AI, while her most recent work focuses on improving the quality, controllability, consistency and representation diversity of visual content creation systems.

She received her PhD from the University of Barcelona, where she worked with Carlo Gatta, and then spent two years as a postdoctoral researcher at Mila with Yoshua Bengio.

Current Students

Reyhane Askari Hemmat
Collaborating researcher - Université de Montréal
reyhane.askari.hemmat@mila.quebec
Sumana Basu
Collaborating Alumni - McGill University
Principal supervisor :
Doina Precup
sumana.basu@mail.mcgill.ca
Website
Github
Google Scholar
Aarash Feizi
PhD - McGill University
Principal supervisor :
Reihaneh Rabbany
feiziaar@mila.quebec
Website
Github
Google Scholar
Soumya Sharma
PhD - McGill University
Principal supervisor :
Golnoosh Farnadi
soumya.sharma@mila.quebec
Website
Github
Google Scholar

Publications

RoboJEPA: Scaling Robotic Latent World Models
Artem Zholus
Nicolas Beltran-Velez
Jianhao Yuan
Sarath Chandar
Tushar Nagarajan
Daniel Severo
Koustuv Sinha
Michal Drozdzal
Adriana Romero Soriano
Jeannette Bohg
Nicolas Ballas
Mahmoud Assran
Latent world models have shown a remarkable ability to predict future states and to plan in the real world. In practice, however, we lack a … (see more)principled way to estimate how their capabilities scale with model size, data, and compute, an open problem that slows progress in the field. In this work we present RoboJEPA, a world model based on the Joint Embedding Predictive Architecture (JEPA) and trained on a large-scale dataset spanning 12 robotic embodiments. We show that RoboJEPA's imagination error, the error of its latent rollouts, follows a second-order power law in compute, allowing us to predict model quality well beyond the scale at which the law is fit. We further show that downstream robotic planning performance improves predictably with compute, and that imagination error is strongly correlated with it, making it a reliable proxy for real-robot evaluation. Finally, we demonstrate that latent world models can be deployed zero-shot as robotic agents, planning toward a single goal image to solve tasks requiring long-horizon planning on real hardware. We release all model checkpoints together with our training and robot deployment code. To our knowledge, this is the first work to establish scaling laws for multi-embodiment robotic world models trained on real robot data, and RoboJEPA, at 8B parameters, is the largest JEPA predictor model trained to date.
2026-10-06
arXiv (preprint)
doi.org
arxiv.org
DIG In: Evaluating Disparities in Image Generations with Indicators for Geographic Diversity
Melissa Hall
Candace Ross
Adina Williams
Nicolas Carion
Michal Drozdzal
Adriana Romero Soriano
The unprecedented photorealistic results achieved by recent text-to-image generative systems and their increasing use as plug-and-play conte… (see more)nt creation solutions make it crucial to understand their potential biases. In this work, we introduce three indicators to evaluate the realism, diversity and prompt-generation consistency of text-to-image generative systems when prompted to generate objects from across the world. Our indicators complement qualitative analysis of the broader impact of such systems by enabling automatic and efficient benchmarking of geographic disparities, an important step towards building responsible visual content creation systems. We use our proposed indicators to analyze potential geographic biases in state-of-the-art visual content creation systems and find that: (1) models have less realism and diversity of generations when prompting for Africa and West Asia than Europe, (2) prompting with geographic information comes at a cost to prompt-consistency and diversity of generated images, and (3) models exhibit more region-level disparities for some objects than others. Perhaps most interestingly, our indicators suggest that progress in image generation quality has come at the cost of real-world geographic representation. Our comprehensive evaluation constitutes a crucial step towards ensuring a positive experience of visual content creation for everyone.
2024-05-06
International Conference on Learning Representations (Accept (Poster))
doi.org
openreview.net