CV

Research experience, education, awards, and technical background.

Contact Information

Name Tomás Guija Valiente
Professional Title PhD Student
Email tomasguija@gmail.com
Website https://www.linkedin.com/in/tomas-guija-valiente/

Professional Summary

PhD student with a background in mathematics and computer science and an MSc in Artificial Intelligence. My research focuses on machine learning, computer vision and generative AI for longitudinal modeling, patient simulation and digital twins. I develop generalizable methods that extend beyond medical imaging, guided by the belief that the field should both benefit from and contribute to advances in machine learning and computer vision. I also contribute actively to open-source scientific software.

Experience

  • 2025 - present

    Madrid, Spain

    Machine Learning and Computer Vision Researcher
    PROMISE – LAIMBIO
    Research focused on generative deep learning methods for 3D computer vision, with applications to medical imaging. This work includes RARF, a rectified-flow model achieving 5th place at the BraTS 2026 Challenge, and SynthRCT, a conditional deformation-generation framework presented as an oral at MIART @ MICCAI 2026; both resulted in peer-reviewed publications.
  • 2026 - 2026

    Remote

    Open Source Contributor, Python Software Foundation
    Google Summer of Code – DIPY
    Contributed to DIPY through GSoC 2026, implementing and benchmarking new similarity metrics for deformable medical image registration, alongside performance and robustness improvements to the registration framework. Project Page
  • 2025 - 2025

    Madrid, Spain

    Machine Learning Researcher
    ML Circle
    Machine Learning researcher working on R&D projects and technology consulting, with experience designing generative AI models and Transformer-based architectures. My research focused on generative modeling for vector graphics, leading to the publication of “DesigNet: Learning to Draw Vector Graphics as Designers Do” in Computers & Graphics.

Education

Awards

  • 2026
    Best Full Paper Award · CEIG 2026
    Eurographics SE

    Awarded for “DesigNet: Learning to Draw Vector Graphics as Designers Do.”

  • 2025
    José Cuena Award
    Universidad Politécnica de Madrid

    Recognition for one of the best academic records in the MSc in Artificial Intelligence.

  • 2025
    Second Place · EUDIS Defence Hackathon 2025
    European Commission

    Second place for the design plan and roadmap of “Warfighter Care Tool.”

  • 2024
    Honours in Final Degree Project
    Universidad de Sevilla

    Awarded for “Variational topological autoencoder for single-cell data.”

  • 2019
    Highest Entrance Admission Score · Faculty of Mathematics
    Universidad de Sevilla

    Entered with a score of 13.87/14.00.

  • 2015
    Fourth Place · Spanish Rubik's Cube National Final
    World Cube Association

    Fourth place in Pyraminx and finalist in several events across the 2014 and 2015 championships.

Publications

Languages

Spanish : Native.
English : C1, Cambridge CAE.
French : B2, DELF.

Technical Skills

Languages: Python, C, C#, R, Matlab, Java, JavaScript.
ML & Deep Learning: PyTorch, Lightning, TensorFlow, Keras, scikit-learn, Wandb, RunPod.
Computer Vision: OpenCV, MONAI, TorchIO, SimpleITK.
Scientific Computing: NumPy, SciPy, Pandas, Matplotlib.
Medical Imaging: DIPY, ANTsPy, NiBabel.
Development Tools: Git, GitHub, Docker, Conda, Linux, Unity.

Open Source

  • DIPY / Python Software Foundation: GSoC 2026 contributor across image registration, segmentation, performance, and scientific computing. Selected contributions: Mutual Information for SyN (#4067), Local Cross-Correlation for affine registration (#4078), and multithreaded displacement-field composition (#4073).
  • Hugging Face: Contributions to timm and PEFT.
  • Kornia: Computer vision contributions, including Kimi-VL implementation alignment (#3968).
  • NV-Generate-CTMR: Added staged model offloading for low-VRAM inference (#46).

Research Interests

  • Generative AI, Computer Vision, Deep Learning, Machine Learning, AI safety and alignment, Open Source Scientific Software.