"The only way to do great work is to love what you do." – F. Faggin

"Talk is cheap. Show me the code." – L. Torvalds

Interdisciplinary engineer with background in power systems and mathematical modeling. Focused on the development of scalable data pipelines and machine learning systems. Expertise in transitioning analytical models from experimentation (Notebooks) to high-throughput production environments (AWS Serverless).

• Systems Architecture: Designing serverless, event-driven pipelines (AWS Lambda/API Gateway).
• Algorithmic Efficiency: Implementation of robust learning models (Random Forest, Clustering) with focus on pre-processing, data integrity, and pipeline reproducibility.
• Deterministic Modeling: Power systems stability and transient analysis.

Focus: High-latency reduction and class imbalance mitigation. Implemented an end-to-end pipeline using SMOTE and Random Forest. Modularized inference logic for AWS Lambda deployment, ensuring separation between the training phase (Notebooks) and the production handler (inference.py).

Focus: Process optimization and data governance. Applied evidence-based decision-making models to optimize public administrative flows, emphasizing algorithmic transparency and operational throughput.

Focus: Unsupervised learning and feature variance. Implementation of clustering techniques for behavioral segmentation, focusing on statistical distance metrics and actionable feature extraction.

Focus: Numerical computation. Mathematical modeling of synchronous machines and electromagnetic fields. Addresses transient stability via numerical algorithms.

My goal is to minimize complexity and maximize system reliability. If you're looking for clean, maintainable, and battle-tested code, let's talk.