Applied Systems Thinking
Background in scientific reasoning (biology, chemistry, physics) applied to software, energy systems, and real-world problem solving.
Technical Transition
Self-directed study in Python, automation, and emerging AI tools, with current focus on electrical systems relevant to EV and solar infrastructure.
Evidence of Discipline
Two decades of structured experimentation in health, training, and finance — building repeatable systems, not chasing trends.
Current Focus
– Python programming, software fundamentals, AI & AI agents
– Electrical systems foundations for solar and EV applications
– Applied analysis across technology, markets, and systems
Flagship Project
1️⃣ The Problem
Many households that would benefit most from solar energy lack access due to upfront costs, financing complexity, and infrastructure barriers.
2️⃣ The Direction
I’m exploring how low-cost photovoltaic systems, simple IoT controls, and usage-based microtransactions could enable incremental access to solar power without large upfront commitments.
3️⃣ Current Stage
This project is currently in an early research and learning phase. My focus is on understanding photovoltaic systems, installation, electrical fundamentals, and adjacent EV infrastructure through hands-on learning, coursework, and industry exposure.
This project informs what I choose to study and where I seek experience — from physics, software and automation to electrical systems, energy infrastructure, and real-world deployment constraints.
Philosophy
I’m drawn to fields where logic meets physical reality—I am deeply curious about the future of technology where systems can be studied, improved, and scaled over time. I value fundamentals, clarity, and long-term compounding over speed or hype. My goal is to grow into increasingly applied engineering roles through demonstrated competence, not credentials alone. Inspired by Richard Feynman, Elon Musk.