Full-Stack Engineer • Research Engineer | Robotics, Control, Power Systems, and Scientific Tooling

I build and research systems at the intersection of high-stakes power systems dynamics and modern engineering tools. My work bridges the gap between theoretical scientific discovery and robust, deployable software solutions.

Core Competencies:

• Power Systems & Control: Dynamic frequency estimation (PMU), low-inertia grid stability, distributed control, and graph-structured systems.
• Scientific Computing: Benchmarking frameworks, Monte Carlo simulation, and reproducible research pipelines.
• Software Engineering: Full-Stack development (TypeScript/Python), backend architecture, and real-time system design (FPGA focus).
• Intelligent Systems: Machine Learning for engineering discovery and multi-agent coordination.

My current work focuses on developing rigorous methods and infrastructure to analyze the dynamic behavior of modern electrical systems:

• Dynamic Estimation: Dynamic frequency, RoCoF estimation, and advanced tracking methods (EKF/Kalman filters) for low-inertia power grids.
• Control & Networks: Distributed control theory, graph-structured systems, and locality-aware coordination strategies.
• Hardware Implementation: Architecting real-time estimation systems optimized for hardware (FPGA-oriented pipelines).
• Benchmarking: Developing reproducible frameworks to stress-test and compare complex scientific methods against realistic grid disturbances.

A specialized benchmarking framework designed to rigorously evaluate the latency, robustness, and computational cost of frequency and RoCoF estimation algorithms in modern power systems. Goal: To move beyond idealized tests by simulating composite disturbances (harmonics, phase jumps) that mimic real-world IBR grid failures. Key Contributions:

• Developed a stress-oriented evaluation pipeline using high-fidelity dual-rate numerical simulations.
• Analyzed the fundamental Latency vs. Robustness Trade-off across diverse estimators (EKF, PLLs, Data-driven methods).
• Quantified failure modes under composite disturbance scenarios to identify limitations in current testing methodologies.

A research direction focused on creating highly efficient, hardware-native implementations of dynamic estimators for power applications. Goal: To transition simulation-grade algorithms (like Kalman filters) into deployable, real-time estimation cores running directly on FPGAs. Focus: Parallel processing structures, minimizing latency, and optimizing signal-processing pipelines for low-latency grid control.

Building practical applications that blend robust engineering with modern software architecture.

• Product Focus: Developing full-stack platforms (using TypeScript/React) and scientific tools to ensure research is reproducible, scalable, and reusable.
• Engineering Stack: Proficient in Python, PyTorch, Numerical Simulation, distributed control theory, and modern web stacks (Node.js, React).

Master's in Robotics and Industrial Automation | Universidad Internacional de Valencia (VIU) (Focus: Sensing, Control, Manufacturing Environments)

Master's in Big Data and Artificial Intelligence | Universidad Isabel I & Structuralia (Focus: Machine Learning, Data Workflows, Applied AI Engineering)

Master's in Electronics and Computer Engineering | Universidad de los Andes (Background: Embedded Systems, Controls, Communications)

I am actively seeking collaborations in research and engineering that involve:

• Dynamic power system modeling and control.
• Developing novel benchmarking frameworks for complex systems.
• Designing hardware/software co-design solutions (FPGA/ML).
• Distributed control, robotics, or intelligent systems.

Contact: 📧 mayorgajl@proton.me | 🔗 [Portfolio Link] | 🔗 [GitHub Link]