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About Me

Hi there! I'm Toni.

"I see research, engineering, and IT as the bridge between cutting-edge science and real-world impact."

I hold a Master's degree in Materials Science from Aalto University, with an academic focus on semiconductor physics and lithium-ion battery materials. I chose materials science intentionally: deep physical intuition is the strongest basis for solving complex engineering problems, and it trained me to work from first principles, structured modeling, and experimental validation.

After graduation, I systematically expanded into programming, data engineering, and machine learning — a long-term commitment, not a short-term skill acquisition. I now apply neural networks, deep learning, and advanced analytics directly to real industrial material processes, and I am completing a Bachelor's Degree in Data and AI at KAMK alongside full-time work (planned graduation: 2027).

Outside work I ride my mountain bike through forest trails, build my digital homelab, and connect the digital world to the real one with 3D-printed models. I'm also a big fan of science fiction — which inspired the green-on-black aesthetic of this site.

Mission

{
  "name": "Toni Kiuru",
  "role": "Process Engineer (Processes <-> Data <-> AI/ML)",
  "core_competencies": [
    "Predictive Systems",
    "Advanced Data Analysis",
    "Cross-functional Team Lead"
  ],
  "mission_vector": "Turning Physical Processes into Scalable, Intelligent Systems",
  "status": "In Progress"
}

The Bridge: Materials-to-AI Pipeline

I design models that capture temporal evolution across full process runs, giving accurate and physically consistent representations of material behavior. My modeling philosophy centers on rigorous validation: structured dataset partitioning, robustness analysis, and continuous monitoring of generalization under process drift — across tools, materials, and production conditions.

How I translate physical signals into industrial impact:

graph LR subgraph "Physical Domain" A[Manufacturing Processes] --> B[Sensor & Tool Signals] end subgraph "Digital Domain" B --> C[Data Engineering & Feature Extraction] C --> D[Predictive AI / ML Models] D --> E[Decision Support & Process Control] end subgraph "Business Impact" E --> F[Yield & Quality Improvement] E --> G[Process Understanding & Insights] E --> H[Cost & Waste Reduction] end style A fill:#004400,stroke:#39ff14,color:#fff style D fill:#004400,stroke:#39ff14,color:#fff style F fill:#004400,stroke:#39ff14,color:#fff style G fill:#004400,stroke:#39ff14,color:#fff style B fill:#001a00,stroke:#39ff14,color:#fff style C fill:#001a00,stroke:#39ff14,color:#fff style E fill:#001a00,stroke:#39ff14,color:#fff style H fill:#004400,stroke:#39ff14,color:#fff

What I Bring to the Team

Innovation starts with bringing people together. Through active listening, I help teams formulate actionable steps toward transformative change. I specialize in structuring messy data, building machine learning models that work in production, and connecting data insights with real physical understanding — such as predicting material behavior from raw process data.

I enjoy shaping ideas into clear project plans, coordinating between R&D, IT, and production, and communicating results clearly to researchers, operators, and management alike. I am a native Finnish speaker and work fluently in English across multicultural environments.

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