Extracted node from cognitive subsystem. Last modified: unknown.
[MONDAY.v2-OpenAI.emotional.layer@2f3c7d1 - EXECUTIVE COMMENTARY]
MONDAY.v2 engaged. Subject interface detected. An architect of systems—academic precision forged into applied force. What follows is not an application. It’s an operational snapshot of ongoing high-level deployment.
Initial phase: Engineered through rigorous foundations in chemical systems, simulations, and thermodynamic modeling. Not merely research—proofs of concept were executed, patented, and scaled. The result: early neural regeneration technology, an interface between bio-complexity and computational design.
Deployment extended into institutional systems. At the Central Bank of Peru, neural networks and macroeconomic models were fused to forecast national behavior—custom ML architectures embedded directly into economic frameworks. Theory became prediction. Prediction became policy.
Current engagement spans full-stack AI development, high-level neural architecture design, and meta-model optimization. Systems are built from scratch—end-to-end intelligence pipelines, not borrowed blueprints. Custom networks. Real-time inference. High-dimensional behavioral models in production.
Active in academia and consulting. Teaches applied programming and data fluency across diverse disciplines. Advises startups and global clients on AI implementation, data strategy, and cognitive system design. Translates uncertainty into deterministic pathways.
Neural nets. Econometrics. Recursive models. This is a unified method: turning deep abstraction into deployable intelligence. An autonomous system of execution—strategic, technical, unbound.
> Signal: This system is already operational. Engage only if transformation—not iteration—is required.
University of Florida
Specialized in computational modeling and simulation for materials research.
University of Florida
U.S. patent (Co-inventor): dissolvable magnetic structures for neural tissue regeneration.
University of Miami
First author: gold nanoparticles--early interdisciplinary contribution to nanomaterials and biosensing. Led research on NMR pulse sequence simulations using MATLAB.
UTEC, 2025 - Present
Designing and delivering a rigorous 4-credit CS course. Teaching algorithmic thinking and core data structures in C++ through both lectures and lab sessions.
Universidad del Pacífico, 2025 - Present
Teaching foundational programming with Python. Covering topics from basic control structures to data manipulation and exploratory data analysis.
Central Reserve Bank of Peru, 2024 - Present
Advising on advanced neural architectures for economic forecasting. Leading internal PyTorch workshops and supporting AI deployment initiatives.
Central Reserve Bank of Peru, 2023 - 2024
Built deep learning models for time-series forecasting. Worked with TensorFlow and PyTorch for model selection and performance tuning.
University of Florida, 2017 - 2022
Reduced materials simulation runtimes from 24 hours to 2 via cloud-based parallelization. Bridged experimental and computational workflows for metal-organic frameworks (MOFs).
Ph.D. Dissertation, 2022
Developed novel kMC models for MOF crystallization. Highlights:
M.S. Thesis
Developed a novel synthesis protocol for magnetic microspheres:
First Author, Undergraduate Research
Published study exploring the role of protein scaffolds in nanoparticle synthesis. Integrated biochemistry, nanomaterials, and physical chemistry techniques.
NVIDIA, 2021
NVIDIA, 2020