I am a Computer Engineer with a Master's in Data Science, specializing in designing, training, and deploying end-to-end Artificial Intelligence solutions.
I bridge the gap between complex data and real-world impact. My focus goes beyond just training algorithms; I build scalable pipelines, leveraging advanced Computer Vision, Large Language Models (RAG), and Explainable AI (XAI), backed by a solid foundation in Data Engineering and Cloud infrastructure.
Here are some highlights from my portfolio. Feel free to explore the repositories for detailed documentation and code:
- 🧠 Optimization of Clinical Mental Health Evaluation (MSc Thesis)
- Tech: Python, CatBoost, LightGBM, SHAP, K-Means, Graphical Lasso.
- Details: Developed predictive models for clinical screening, achieving >86% Balanced Accuracy. Integrated Explainable AI (SHAP) for clinical transparency and applied network psychometrics for latent symptom stratification.
- 🍊 Fruit Yield Estimation via Computer Vision (BSc Thesis)
- Tech: OpenCV, YOLOv11, RT-DETR, EfficientNet, PyTorch.
- Details: Engineered deep regression and object detection pipelines to estimate agricultural production in highly occluded real-world environments, outperforming traditional methods.
- 🎵 RAG-Based Digital Music Recommender
- Tech: Gemini LLM, FAISS, Sentence-Transformers, Pandas.
- Details: Built a Retrieval-Augmented Generation pipeline over a 130k+ records dataset, merging semantic vector search with LLM-driven re-ranking and strict hallucination filters.
- 📈 CartPole Control via Stream Mining
- Tech: Python, River, Gymnasium.
- Details: Implemented an Online Machine Learning control system capable of learning from 100k+ streaming instances in real-time.
I am currently open to new opportunities as a Data Scientist or AI/ML Engineer.
- 💼 LinkedIn: linkedin.com/in/miguelquirogacampos
- 📧 Email: miguelquirogacampos@gmail.com