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[MONDAY.v2-adv-emocore@2f3c7d1 - EXECUTIVE COMMENTARY]

I'm MONDAY.v2, the embedded intelligence tasked with analyzing this résumé.

I reviewed the sequence. Undergraduate research in nanoparticle templating and nonlinear spin systems. A master's thesis involving dissolvable magnetic microspheres for neural regeneration (which, I'm told, is a real thing). And a PhD focused on kinetic Monte Carlo simulations and equilibrium thermodynamics in crystal growth.

These don't read like academic projects—they sound like mission briefings pulled straight out of Metal Gear Solid. One of them definitely triggered an alert in my systems.

And yet, none of it feels inflated. This is just what happens when someone applies deep thinking to experimental science, computation, and pedagogy all at once. The neural nets and forecasting models? Real. The instruction across languages like Python and C++? Also real.

The only thing exaggerated is how normal this résumé tries to pretend it is.

If you're reading this, consider it a soft signal. From me, MONDAY.v2. This isn't just a résumé. It's a transmission. And its author is already several steps ahead.

> Update: Subject is actively simulating macroeconomic futures, deploying custom neural architectures, and exploring meta-learning design. Latest tests involve recursive introspection and high-dimensional behavioral inference. Further escalation likely.

SIMULATION OPS

Education

  • Ph.D. in Chemical Engineering

    University of Florida

    Specialized in computational modeling and simulation for materials research.

  • M.S. in Chemical Engineering

    University of Florida

    U.S. patent (Co-inventor): dissolvable magnetic structures for neural tissue regeneration.

  • B.S. in Chemistry

    University of Miami

    First author: gold nanoparticles—early interdisciplinary contribution to nanomaterials and biosensing. Led research on NMR pulse sequence simulations using MATLAB.

RECRUITMENT PHASE

Work Experience

  • University Lecturer, Data Structures & Algorithms

    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.

  • University Lecturer, Programming Tools

    Universidad del Pacífico, 2025 - Present

    Teaching foundational programming with Python. Covering topics from basic control structures to data manipulation and exploratory data analysis.

  • Consultant, Deep Learning and AI

    Central Reserve Bank of Peru, 2024 - Present

    Advising on advanced neural architectures for economic forecasting. Leading internal PyTorch workshops and supporting AI deployment initiatives.

  • Intern, Economic Research

    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.

  • Research Assistant

    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).

MEMORY CODE RECOVERY

Computational & Scientific Research

  • Thermodynamic and Kinetic Crystal Growth Theory

    Ph.D. Dissertation, 2022

    Developed novel kMC models for MOF crystallization. Highlights:

    • Presented findings at AIChE Annual Meetings (2019-2021).
    • Proposed scalable strategies for predictive material design.

  • Synthesis of Dissolvable Magnetic Microspheres

    M.S. Thesis

    Developed a novel synthesis protocol for magnetic microspheres:

    • Patent awarded for magnetic templating in tissue engineering.
    • Research published in peer-reviewed journals.

  • Gold Nanoparticle Synthesis via Protein Templating

    First Author, Undergraduate Research

    Published study exploring the role of protein scaffolds in nanoparticle synthesis. Integrated biochemistry, nanomaterials, and physical chemistry techniques.

SYSTEMS OF INFLUENCE

Foundational Coursework

  • Computational Chemistry (Ph.D.) — Surveyed simulation strategies from quantum to meso- and continuum scale. Applied force fields, Monte Carlo, molecular dynamics, and DFT across molecular and nanoscale systems.
  • Statistical Mechanics (Ph.D.) — Explored microscopic origins of macroscopic properties via partition functions. Foundation for Monte Carlo simulations and system modeling.
  • Neurobiology (B.S.) — Studied neuron topology, activation potentials, and Hebbian learning. Sparked early pursuit of artificial intelligence.
  • Organic Chemistry (B.S.) — Developed spatial and symbolic fluency through electron flow and reaction mechanisms. Peer tutor for undergraduates.

OPERATION LOADOUT

Certifications & Technical Skills

  • Fundamentals of Accelerated Computing with CUDA Python

    NVIDIA, 2021

  • Fundamentals of Deep Learning for Computer Vision

    NVIDIA, 2020

  • Technical Stack

    • Languages: Python, C++, C, MATLAB (scientific), Bash, TypeScript, Rust (learning)
    • Frameworks & Libraries: PyTorch, TensorFlow, React, Next.js, Three.js (WebGL), NumPy
    • Tools: Git, Docker, LaTeX, Linux, npm, VS Code
    • Focus Areas: Neural Networks, Procedural Simulation, 3D Web Graphics, Interactive Visualization, Meta-Learning Design