About me
I am a PhD student in theoretical physics at the Institute of Cosmos Sciences (ICCUB), Universitat de Barcelona, funded by an FPI fellowship (Ministerio de Ciencia e Innovación) as part of the project Dark Energy and the Origin of the Universe.
My research program focuses on hydrodynamics from a holographic perspective: from transport and far-from-equilibrium dynamics to phase transitions in the early universe and turbulence. I am interested in how AdS/CFT can provide quantitative and conceptual tools to understand fluid phenomena in regimes where standard perturbative methods are limited.
A central theme in my work is the interface between general relativity, quantum field theory, and fluid dynamics. In practice, I study reconstruction and inversion problems where gravitational dual geometries are inferred from boundary observables, with emphasis on nonlinear behavior and physically interpretable effective descriptions.
Methodologically, I combine analytic approaches with numerical methods and machine learning techniques, especially physics-informed neural networks (PINNs), to solve stiff nonlinear systems that appear in holography and relativistic physics. My goal is to build models that are both numerically robust and faithful to the underlying physics.
Before my PhD I completed an MSc in Theoretical Physics at the Instituto de Física Teórica (IFT), UAM-CSIC — where my thesis on Quantum Chaos in de Sitter Space and Centaur Geometries was supervised by Juan F. Pedraza and Ayan Kumar Patra — and a BSc in Physics at the Universidad Complutense de Madrid.
Research interests
AdS/CFT & Holography
Using the AdS/CFT correspondence to study strongly-coupled QFTs via their gravitational duals. Current focus: reconstructing holographic bulk geometries from boundary thermodynamic data with PINNs.
Physics-Informed Neural Networks
Developing efficient PINN architectures for stiff, nonlinear PDEs in theoretical physics — multi-head networks, unimodular solution-space regularization, and embedding-based approaches.
Phase Transitions in the Early Universe
Applying holographic methods to probe cosmological first-order phase transitions relevant to baryogenesis and the gravitational-wave background, and to neutron star merger equations of state.
Turbulence & Geometry of Flows
Studying turbulence and hydrodynamic instabilities through holography and the geometry of flows, as a member of the Simons Collaboration for the Geometry of Flows.
Numerical Methods for Theoretical Physics
Using deep learning as a numerical tool to compute observables in holography, GR, and cosmology — complementing or replacing spectral and finite-difference methods.
AI for Scientific Discovery
Building autonomous AI systems capable of literature synthesis and hypothesis generation across disciplines, as part of the Denario project.
Selected publications
Full list and citation tracking: Google Scholar · INSPIRE-HEP · ORCID · arXiv.
Teaching
The machine-learning module of the Mathematical and Statistical Techniques course in the Master's in Astrophysics, Particle Physics and Cosmology at the University of Barcelona. The lectures introduce neural networks as a numerical tool for physics, with a hands-on focus on physics-informed neural networks (PINNs) for solving differential equations. All notebooks, examples, and exercises are openly available on GitHub.
Interested in the lectures or in using the materials? The notebooks are self-contained and free to use — feel free to reach out with questions or feedback.
Curriculum Vitae
PhD supervision
Education
Research experience
Selected talks
All nine talks, plus 20+ schools and workshops (Simons Collaboration for the Geometry of Flows at IAS Princeton, ETH Zurich, CERN, GGI Florence, Benasque), are listed in the PDF.
Teaching
Media & outreach
Skills & tools
Contact
Happy to discuss research, collaborations, or PhD-level topics in holography, PINNs, early-universe cosmology, or turbulence.