CV

For the full version, see my LinkedIn.

Research Experience

Research Scientist, Meta Superintelligence Labs, Zurich (Oct 2025 – present)
- Media Generation team. Working on generative model distillation and real-time interactive video generation.
Visiting Researcher, Meta FAIR, Montreal (2024 – 2025)
- Working on multimodal vision+language understanding and generation, advised by Michal Drozdzal and Adriana Romero.
Research Scientist Intern, FAIR Labs, Meta AI, Montreal (2023)
- Worked on text-conditioned image generation, advised by Adriana Romero and Michal Drozdzal.
Research Intern, Element AI, Montreal (2020 – 2021)
- Worked on data-efficient ML and self-supervised visual representation learning, advised by Pau Rodríguez and David Vázquez.
Research Assistant, Image Processing Group, UPC, Barcelona (2018 – 2019)
- Worked on visual representation learning for sample-efficient reinforcement learning, advised by Xavier Giró and Víctor Campos.
Research Assistant, Architectures and Compilers Group, UPC, Barcelona (2016 – 2017)
- Worked on efficient cognitive computing to run deep neural networks on edge devices, advised by Antonio González and José María Arnau.

Computer Vision Engineer, Mediapro R&D, Barcelona (2019 – 2020)
- Real-time video analysis for automatic sports production.
Deep Learning Engineer, Restb.ai, Barcelona (2017 – 2019)
- Visual recognition problems involving real estate property images.

Ph.D. Computer Science, Mila / Université de Montréal, 2021 – 2025
- Thesis: Towards efficient, reliable and measurable vision-language systems
- Supervised by Prof. Aishwarya Agrawal. GPA: 4.3/4.3.
M.Sc. Computer Vision, Computer Vision Center / Universitat Autònoma de Barcelona, 2018 – 2020
- Rank 1/35. Grade: 9.58/10. Recognition of outstanding academic achievement.
- Thesis: Self-Supervised Visual Representation Learning for Remote Sensing.
B.Sc. Informatics Engineering (Computer Science), Universitat Politècnica de Catalunya, 2013 – 2017
- Rank 1/235. Grade: 9.52/10. Recognition of outstanding academic achievement.
- Thesis: Adapting Deep Neural Networks to a Low-Power Environment.

Languages: Python (proficient); C++, Java, C, CUDA, SQL (familiar)
ML Frameworks: PyTorch, HuggingFace (Transformers, Diffusers), vLLM, NumPy, OpenCV
Tools: Git, Docker, LaTeX, TensorBoard, Weights & Biases, Submitit
Systems: Linux/Unix, Slurm, distributed training/inference, parallel programming

J. Armand Bombardier Excellence Grant, Université de Montréal (2024, 2025)
Artificial Intelligence Grant, Université de Montréal (2024)
DIRO Excellence Grant, Université de Montréal (2023, 2024)
J.A. DeSève Excellence Grant, Université de Montréal (2022)
National End-of-Degree Award in University Education, Spanish Ministry of Education (2021)
Mila PhD Scholarship, Mila - Quebec AI Institute (2021)
Mitacs Accelerate International Grant, Mitacs Canada (2020)

Invited Talks: Deep Learning Barcelona Symposium 2025 — spotlight talk (recording)
Reviewer: CVPR 2026, CVPR 2025, ICLR 2025, ECCV 2024, CVPR 2024, NeurIPS 2023, ACL 2023, ICCV 2021
Teaching Assistant: Links between Computer Vision and Language, UdeM (2023); Postgraduate AI for Deep Learning, UPC School (2020); Summer School on Deep Learning for Vision, UPC (2019)