Software Engineering · Applied ML · Research

I research, design and build artificial intelligence and software solutions.

AI Researcher at VRAIN
Master's in AI
Computer Engineer

I conduct AI research, developing R&D solutions, spanning both software and AI, that range from the initial research question to a reproducible implementation. Do you have a technical problem to solve?

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Selected projects

A selection of work with a clear problem, method and verifiable results.

Four chest X-rays from the test set and, below them, their Grad-CAM++ maps for emphysema, with the model's attention focused on the lung fields.

01 / Medical Imaging

Multi-label chest X-ray classification with DenseNet-121

Detecting 14 pathologies at once in 112,000 chest X-rays, with an asymmetric loss, per-pathology calibrated thresholds and Grad-CAM++ to audit the model.

Diagram of the app in three cards. An audio waveform goes into Whisper, which produces transcript lines, and then into a 4-bit LLM that writes a short summary. The last card lets you choose between Qwen 14B and Phi-3 mini.

02 / Generative AI

Local video transcription and summarisation with Whisper and a quantised LLM

A web app that transcribes and summarises videos with Whisper and a quantised 14B LLM, all locally and with no external APIs.

Four greyscale skin lesions. Next to each one, the same image with the ground-truth mask outline in amber and the model's prediction outline in blue.

03 / Medical Imaging

Melanoma segmentation with U-Nets built from scratch in PyTorch

Two U-Nets coded from scratch to outline skin lesions: one trained from scratch and one with a frozen pretrained ResNet-50 encoder.

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Research & publications

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Conference · 2026 · ER 2026

Conceptual Modeling for Semantically-Guided Feature Selection in Machine Learning: A Case Study on Mohs Micrographic Surgery

Francisco Miralles, Edu Nagore Insa, Salvador Francisco Navarro Aljibe, Ruggero Moro, Eduardo Nagore Enguídanos, Oscar Pastor

A conceptual modeling approach to incorporate clinical domain knowledge into feature selection for Machine Learning models.

DOI : Conceptual Modeling for Semantically-Guided Feature Selection in Machine Learning: A Case Study on Mohs Micrographic Surgery

Conference · 2026 · MODELSWARD 2026

A Conceptual Model-Based Platform for Semi-Automatic Report Generation to Streamline Medical Diagnostics of Lung Cancer and PET/CT Data Management

Francisco Miralles Ferrer, José Fabián Reyes Román, Pedro Abreu Sánchez, Jesús Carreño Bolufer, Elisa Verónica Caballero Calabuig, Oscar Pastor

Development of a conceptual model and a model-based software platform to support lung cancer diagnosis through PET/CT data management and semi-automatic generation of structured medical reports.

DOI : A Conceptual Model-Based Platform for Semi-Automatic Report Generation to Streamline Medical Diagnostics of Lung Cancer and PET/CT Data Management