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.
Industry Experience
- 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.
Education
- 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.
Technical Skills
- 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
Selected Awards
- 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)
Academic Service
- 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)
Languages
- English (fluent), French (advanced, C1), Catalan (native), Spanish (native)
