I am a Physics Simulation Engineer and researcher with a PhD in cloth simulation. I work across the whole simulation stack, from GPU-accelerated physics engines in C++/CUDA to robotics simulation and control. My recent focus is digital twins and the real2sim side of the real2sim2real loop: system identification that calibrates high-fidelity simulators from captured data, closing the sim-to-real gap for the teams that train robots in simulation. I pair a strong mathematical and physics foundation with a background in video games, game engines, and virtual reality.
May 2025 - Present
Madrid, Spain
SceniX has built a hybrid simulation system that provides the full set of capabilities needed for developing and deploying robotics applications. This includes data creation, policy creation, training, evaluation, and on-going predictive monitoring & edge-case discovery. This hybrid simulation system fundamentally expedites the speed of development and the reliability of deployment.
May 2025 - Present
Sep 2019 - May 2024
Madrid, Spain
SEDDI is a science-backed software company that is redesigning the way fashion goes to market. Their collaborative cloud-native apparel simulation and 3D CAD solutions are used by brands, mills, and manufacturers to easily create digital textiles, garments, and human forms.
Sep 2019 - May 2024
Nov 2018 - Jun 2024
Madrid, Spain
King Juan Carlos University is a Spanish public research university located in the southern area of the Community of Madrid.
Jan 2023 - Jun 2024
Nov 2018 - Jul 2019
Dec 2017 - Apr 2018
Valencia, Spain
Transmedia group that develops audiovisual content able to reach today’s global audiences. The position was in the Brave Zebra division, dedicated to outsourcing and co-production.
Dec 2017 - Apr 2018
Mar 2017 - Nov 2017
Castellón, Spain
The Institute of New Imaging Technologies is a university research institute of Jaume I University, devoted to research that advances knowledge and technological innovation in imaging, improving citizens’ quality of life and companies’ competitiveness.
Mar 2017 - Nov 2017
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Sep 2019 - Jul 2024
PhD in High Fidelity Cloth SimulationMentions: Cum Laude & Industrial DoctoratePublications:
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Sep 2018 - Sep 2019
Master’s Degree in Computer Graphics, Video Games and VRGrade: 9.51 out of 10 |
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Sep 2014 - Jul 2018
Degree in Video Game Design & DevelopmentGrade: 8.04 out of 10 |
Digital garments are set to revolutionize the apparel industry in the way we design, produce, market, sell and try-on real garments. But for digital garments to play a central role, from designer to consumer, they must be a faithful digital replica of their real counterpart: a digital twin. Yet, most industry-grade tools used in the apparel industry do not focus on accuracy, but rather on producing fast and plausible drapes for interactive editing and quick feedback, thus limiting the value and the potential of digital garments. The key to accuracy lies in using the proper underlying simulation technology, well documented in the academic literature but historically sidelined in the apparel industry in favor of simulation speed. In this paper, we describe our industry-grade cloth simulation engine, built with a strong focus on accuracy rather than sheer speed. Using a global integration scheme and adopting state of the art simulation practices from the Computer Graphics field, we evaluate a wide range of algorithms to improve its convergence and overall performance. We provide qualitative and quantitative insights on the cost and capabilities of each of these features, with the aim of giving valuable feedback and useful guidelines to practitioners seeking to implement an accurate and robust draping simulator.
To deploy yarn-level cloth simulations in production environments, it is paramount to design very efficient implementations, which mitigate the cost of the extremely high resolution. To this end, nodal discretizations aligned with the regularity of the fabric structure provide an optimal setting for efficient GPU implementations. However, nodal discretizations complicate the design of robust and controllable bending. In this paper, we address this challenge, and propose a model of bending that is both robust and controllable, and employs only nodal degrees of freedom. We extract information of yarn and fabric orientation implicitly from the nodal degrees of freedom, with no need to augment the model explicitly. But most importantly, and unlike previous formulations that use implicit orientations, the computation of bending forces bears no overhead with respect to other nodal forces such as stretch. This is possible by tracking optimal orientations efficiently. We demonstrate the impact of our bending model in examples with controllable anisotropy, as well as ironing, wrinkling, and plasticity.