معرفی
Belen Masia is a tenured Associate Professor in the Computer Science Department at Universidad de Zaragoza, Spain. She is affiliated with the Graphics & Imaging Lab (part of the I3A Institute) and the Vision, Image and Neurodevelopment Group (within the IIS Aragon Institute). Her research bridges computational imaging, applied perception, and virtual reality, focusing on modeling human visual behavior and improving graphics/vision algorithms through perceptual insights.
- Education: Ph.D. in Computer Science (Eurographics PhD Award 2015), postdoctoral work at Max Planck Institute for Informatics.
- Research Highlights:
- Virtual Reality: Studying user behavior, saliency prediction, multimodal perception, and cinematography in VR.
- Appearance Modeling: Developing intuitive material representations and metrics for editing.
- Applied Perception: Leveraging human vision insights to diagnose defects in non-verbal patients.
- Computational Displays: Exploring HDR imaging and display optimization.
- Scientific Awards:
- Eurographics Young Researcher Award 2017
- Eurographics PhD Award 2015
- MIT Technology Review Top Ten Innovators Below 35 in Spain 2014
- NVIDIA Graduate Fellowship 2012
- Leonardo Fellowship from BBVA Foundation 2020
- Leadership & Editorial Roles:
- Co-chair of Full Papers track at Eurographics 2026
- Associate Editor for ACM Transactions on Graphics, Computers and Graphics, and ACM Transactions on Applied Perception
- Co-founder of DIVE Medical, a startup for automated visual function diagnosis
- PhD Students:
- Dario Lanza (2025, Modeling, Perception and Editing of Volumetric Materials)
- Daniel Martin (2024, Computational Models of Visual Attention in VR, Best PhD Thesis Award EGSE)
- Julia Guerrero-Viu (2023, WiGRAPH Rising Star)
- Sandra Malpica (2023, VR Gaze Behavior)
- Manuel Lagunas (2021, BBVA/SCIE Young Researcher Award)
- Ana Serrano (2019, Eurographics PhD Award & Unizar Outstanding Thesis)
Collaborations & Grants: Involved in the EU-funded PRIME Innovative Training Network (predictive rendering and appearance reproduction) and leading projects on deep learning for pediatric visual diagnosis.
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