STEELE
LABORATORIES

Bridging the gap between physical hardware and quantum-ready simulation.

> SULLIVAN R. STEELE
> DATA SCIENTIST & AVID MAKER

INITIALIZE_PORTFOLIO()

01. THE MAKER-SCIENTIST

I do not just analyze data; I build the systems that generate it. From synthetic data generation for aquaculture to embedded sensor networks for agriculture, my work operates at the intersection of rigorous code and physical reality.

🧬

Simulation & Physics

Investigating algorithms for Atomic Stability (Idea 111) and Electron Cloud Raytracing (Idea 122).

âš¡

IoT & Embedded Systems

Microcontroller technologies, agricultural sensor threads (Idea 512), and robotics.

📊

Synthetic Data

Solving data scarcity by training AI on computer-simulated datasets (Fish Paper).

02. FEATURED CASE STUDY

Solving the "Cold Start" Problem in AI

Challenge: Training accurate computer vision models for fish detection without expensive manual data labeling.

Solution: Developed a "Virtual Simulation Aided AI Model." I simulated fish schooling behavior to create a synthetic dataset.

READ_PUBLICATION
91.8%
Accuracy using only 10% Real Data
$10k+
Saved in Labeling Costs (Conservation Fund)

03. HYPOTHESIS LOG

Selected concepts from my archive of 850+ inventions (2017–Present).
Filtered for: Simulation, Physics, IoT

PHYSICS SIM

Gravitational & Atomic Visualization

Ref #108/110: Simulation of gravitational fields mapped to colors. Investigating atomic attraction modeling.

RENDERING

Molecular Raytracing

Ref #122: Chemical bonding program using raytracing to visualize electron clouds of molecules.

HARDWARE

Smart Seedling Watcher

Ref #514: Computer vision system that tracks growth rates and diagnoses plant health issues autonomously.

ROBOTICS

Infrastructure Railbot

Ref #460: Autonomous robot for railroad tracks using CV to detect debris and structural anomalies.

AI AGENTS

Ant Pheromone Sim

Ref #733: Agent-based model where AI sets scent trails to direct swarm behavior.

DATA ANALYSIS

Biometric Synchronization

Ref #441: Study correlating musician breathing patterns with tempo dynamics in non-wind instruments.

04. CAPABILITIES

// sullivan_steele_profile.json
{
  "name": "Sullivan R. Steele",
  "education": "B.S. Data Analytics, Shepherd University",
  "languages": [
    "Python", "R", "Rust", "C++", "JavaScript"
  ],
  "specialties": [
    "Synthetic Data Generation",
    "Legacy System Modernization",
    "Microcontroller Technologies"
  ],
  "impact": {
    "Workforce_WV": "Saved $1000+ rewriting libraries",
    "Conservation_Fund": "Saved $10,000 via ML optimization"
  },
  "contact": "sullivanrsteele@gmail.com"
}
INITIATE_CONTACT()
```