About
Data Science & AI student at Universidad Politécnica de Madrid (UPM), focused on building production-grade ML systems for quantitative finance. Currently architecting AI pipelines for institutional research at UPM as an AI Software Engineer & Architect.
Passionate about rough volatility modeling, neural portfolio optimization, and making ML testing rigorous.
Featured Projects
- Quant Finance Engine — Institutional-grade, event-driven backtesting system with iTransformer forecasting, InvAD crisis detection, and NeuralHRP allocation. (Python, PyTorch, XGBoost)
- FractalSig — Hybrid JAX/PyTorch model for rough volatility generation with Besov-wavelet decoder achieving 70x improvement. (JAX, PyTorch)
- TrueRisk — Climate emergency management platform with ML-powered risk scoring for 52 Spanish provinces. 2nd place at Cubepath 2026 Hackathon. (FastAPI, XGBoost, LightGBM, LSTM, TFT, Next.js)
- checkllm — A pytest plugin and CLI for testing LLM-powered applications with deterministic checks and LLM-as-judge evaluation. (Python)
Open Source Contributions
Contributor to PyTorch (Meta), Hugging Face Transformers, OpenAI Triton, NVIDIA TensorRT-Model-Optimizer, Lightning AI, vLLM, and Tauric Research TradingAgents.
Education
Bachelor's Degree in Data Science and Artificial Intelligence — Universidad Politécnica de Madrid (UPM). Honors (Matrícula de Honor) in Data Science Programming and Natural Language Processing.
Certifications
- AWS Certified AI Practitioner — Amazon Web Services
- Azure AI Fundamentals — Microsoft
- Financial Markets — Yale University (Risk Management & Behavioral Finance)
Technologies
Python, PyTorch, JAX, XGBoost, Scikit-learn, Pandas, NumPy, LangChain, Docker, AWS, Azure, Git, React, Next.js, Tailwind CSS, Three.js, SQL, Bash