Final-year AI Engineering student at the University of Alicante.
I'm interested in what happens after the notebook: how models get deployed, monitored, and what they actually do to the people on the other side of their decisions.
Currently:
- 🔬 Working on fairness auditing and explainability for ML systems
- 🏗️ Building end-to-end data pipelines (Airflow · Spark · Delta Lake)
- 📚 Preparing my final year thesis
- 🌱 Learning MLOps in depth — FastAPI, Docker, model monitoring
Languages — Python · SQL · Bash
ML — PyTorch · scikit-learn · LightGBM · SHAP · Fairlearn
Data — Spark · Airflow · Delta Lake · MinIO · pandas
Infra — Docker · Linux · Git
hmda-fairness-audit — Detecting and mitigating racial bias in mortgage lending decisions across 283K real loan applications. Cut the racial approval gap from 34.6 to 13.7 percentage points using Fairlearn, and quantified exactly what it cost in model performance.
lunarlander-policy-gradients — Policy gradients on LunarLander. An entropy bonus set 10x too high taught the agent to hover forever instead of landing — reward looked fine, episode duration gave it away. Diagnosed and fixed.
graph-neural-networks — From GNN fundamentals to an NP-hard problem: a controlled study of when graph structure actually helps (GCN goes from 0.53 to perfect accuracy depending on homophily), then solving Maximum Clique with a GNN + Replicator Dynamics hybrid.
llm-systems-from-scratch — The modern LLM stack built from scratch on a 1.5B CPU model: prompt engineering, RAG (with hybrid semantic + lexical search), and LangChain agents. Focused on where each technique breaks, not just where it works.
vae-wae-generative-models — Generative models from scratch: VAE (KL) vs Wasserstein Autoencoder (MMD), compared across 2D images and 3D voxel grids (MNIST, CIFAR-10, ModelNet10). Focused on where each latent regularizer breaks, not just where it works.
LinkedIn · dennisgarciasolera@gmail.com
Open to internship opportunities — ML in production, data engineering.