Toni Kiuru
Materials Scientist | Applied AI Research | Computational Engineering
"A curious lifelong learner from Finland. I find inspiration in building and developing impactful systems to drive growth."
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, where I developed a strong foundation in physics, chemistry, and data-driven problem solving. My academic focus included semiconductor physics and lithium-ion battery materials, combining theoretical understanding with experimental insight.
From early on, I was equally drawn to computational thinking. I chose materials science intentionally, recognizing that deep physical intuition provides the strongest basis for solving complex engineering problems. This background trained me to approach phenomena through first principles, structured modelling, and experimental validation.
After graduation, I deliberately expanded into programming, data engineering, and machine learning, viewing it as a long-term commitment rather than a short-term skill acquisition. I built my expertise systematically in neural networks, deep learning, and advanced analytics, applying these methods to real industrial material processes.
Currently, I am completing a Bachelor’s Degree in Data and AI at KAMK alongside my work to formalize and certify my existing expertise, while staying aligned with modern tools and expanding my professional network. My planned graduation year is 2027.
I'm a big fan of science fiction, and the classic green-on-black aesthetic of the genre inspired the theme of this website. When I'm outside, I enjoy riding my mountain bike through forest trails. Indoors, I keep building my digital Homelab and connecting the digital world to the real world with a 3D printer models, bringing ideas to life. This mix of outdoor adventure and creative work keeps me motivated and curious.
Work Life & 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"
}
What I Bring to the Team
I have experienced firsthand that innovation starts with bringing people together. Through active listening, I help teams formulate actionable steps to create transformative change. I help teams structure messy data, build machine learning models that actually work in production, and connect data insights with real physical understanding. For example, I've developed models to predict material behaviour from process data.
I enjoy shaping ideas into clear project plans, coordinating between R&D, IT, and production, and making sure every deliverable adds real value. I bring structured thinking, curiosity, and a collaborative mindset and I communicate results clearly, whether to researchers, operators, or management. I am a native Finnish speaker and work fluently in English in multicultural environments.
The Bridge: Materials-to-AI Pipeline
In my work, I focus on modelling complex material and process systems. I design models that capture temporal evolution across full process runs, enabling more accurate representation of physical behaviour. I develop models that capture the temporal dynamics of production, enabling accurate and physically consistent representations of material behavior. My approach emphasizes rigorous validation, including structured dataset partitioning, robustness analysis, and continuous monitoring of model generalization under process drift. Ensuring reliability across tools, materials, and production conditions forms a central principle of my modeling philosophy.
How I translate physical signals into industrial impact.
Where Data Meets Real World
My background in materials and computational science allows me to bridge the gap between physical understanding and machine learning. I approach data not merely as numerical input, but as a window into the physical phenomena that drive material performance.
- Applied AI/ML: I work fluently with Python-based workflows (TensorFlow, PyTorch, Scikit-learn) to develop models for predictive material behaviour, quality control, anomaly detection, and predictive maintenance.
- Operational Integration: I focus on deploying models as actual decision-support tools in operational environments, ensuring they remain physically meaningful and practically usable.
Innovation & Impact
I thrive in environments where innovation is measured by results. My approach combines theoretical understanding with a data-driven mindset to solve complex manufacturing challenges.
Example Highlights
- Yield Optimization: I have led initiatives resulting in historically high yields and a 40% reduction in yield costs within a single year.
- Digital Transformation: Pioneered data-driven quality systems, including the implementation of predictive maintenance pipelines and deep learning defect classification models.
- Metrology Mastery: Extensive experience with SEM, confocal/optical microscopy, and laser-based measurement systems to ensure data quality through MSA and Gage R&R.
Collaborative Leadership
I believe that the best solutions are created through trust, clarity, and shared goals. I am comfortable bridging the gap between technical and operational teams. Whether it's sparring with equipment engineers, mentoring R&D teams, or building cross-functional material strategies with global suppliers.
Experience
Sr. Process Engineer @ Murata
Vantaa, Finland (2022 – Present)
Silicon-Glass Fusion Process Ownership
I manage product/process qualifications, optimization, cost reduction, and yield improvement projects. My role involves executing Engineer Change Notices (planning, validation, documentation, safe-launch, reporting) and ensuring robust process controls.
-
Key Responsibilities:
- Responsible of silicon-glass fusion process to ensure structural integrity and directing post-fusion workflows.
- Metrology tool controls (MSA, Gage R&R).
- Implementing process controls and continuous improvements (CI) to reduce variation.
- Leading root-cause investigations (FTA, 8D).
- Overseeing purchases and supplier quality projects.
- Financial assessments and ROI analyses.
-
My Impact (Data & AI):
- Digital Transformation: Spearheaded initiatives delivering end-to-end data solutions, from novel data-collection strategies to fully automated analytics.
- AI Integration: Developed novel process controls leveraging AI for predictive maintenance and smarter production workflows.
- Visualization: Created Power BI and JMP dashboards for enhanced analysis.
- Mentorship: Mentoring engineers in machine learning and deep-learning initiatives.
Click_to_see_manufacturing_processes
Thermal glass-silicon fusion
Edgegrinding
Edgeprofilemeter
Grinding
Washing
Etching Processes
Thermal Relaxation Processes
Polishing Processes
Metrology Tools
Click_to_see_data_and_ai_innovations
Glass-Silicon Fusion Data and AI solution
Grinding Process Control via novel data innovations
High-impact feasibility studies with significant cost-reduction potential
Ownership and development of BI solutions
Mentorship via Python
Thesis Worker @ Aalto University
Espoo, Finland (2021)
- Investigated the degradation of lithium-ion battery materials (NMC811 and graphite) for second-life applications.
- Conducted extensive material characterization and data analysis as part of a Business Finland commission.
Process Operator @ Okmetic
Vantaa, Finland (2012 – 2017)
- Began my career in a cleanroom environment specializing in Double-Sided Polishing (DSP) of semiconductor wafers.
- Operated precision equipment, performed maintenance, troubleshooting, and quality control.
- utilized data to monitor processes and ensure quality standards were met.
- Expanded role to include Single-Sided Polishing (Okamoto XLSSP) and wafer sorting, contributing to process development and loss minimization.
Skills
| Category | Skills |
|---|---|
| Materials Science | MEMS, Li-ion technology, Semiconductor Technology, Microfabrication |
| Machine Learning | Machine Learning, Deep learning, TensorFlow, PyTorch, Scikit-learn, Model Context Protocol, Hugging Face |
| Data Engineering | Python, SQL, ETL Pipelines, Data Analysis |
| Tools & Platforms | Git, SVN, Power BI, JMP, Linux, Postgres, DuckDB, dbt, Ollama, Antigravity, Gemini CLI, Claude Code, Perplexity |
| Soft Skills | Leadership, Team Collaboration, Communication, Problem Solving |
Home Project examples
fintraffic_railway_data_pipeline
A professional-grade data platform setup for analyzing Finnish Railway data. I got hands-on experience with DuckDB, dbt, and Evidence while designing an end-to-end analytical workflow.
GNN image classifier
This project applies data augmentation to overcome limited labeled data in Wafer Automatic Visual Inspection (WAVI), improving classification accuracy with fewer samples. I learned how smart augmentation can significantly reduce manual labeling effort while keeping AI models robust and effective.
MultiAgentPromptingForCodingTasks
This is one of my early experiments using crewAI to build an LLM multi-agent workflow for coding tasks. This project deepened my understanding of prompt engineering and agent coordination.
Q-learning-vs-ExpectedSarsa_in_cliff_world
A visual comparison between Q-learning and Expected Sarsa reinforcement learning algorithms, highlighting their exploration–safety trade-offs. It strengthened my intuition about balancing performance and stability in RL systems.
Reinforcement Learning - Lunar Lander v3
An implementation of a neural-network-based RL agent for the classic Lunar Lander v3 game. I learned to design an environment-aware, action-driven training loop from scratch.
Snake Game - Click to play
A classic Snake game implementation where you can play against AI or a friend.
- Check out more on my GitHub.
- School projects in internal [GitLab]
Education
Master's Degree | Aalto University (2021) : Major: Functional Materials : Thesis: Analysis of Lithium-Ion Battery Cathode-Anode Materials for Second-Life Applications
Bachelor's Degree | Kajaanin ammattikorkeakoulu KAMK (2025 – Present) : Major: Data and Artificial Intelligence
Bachelor's Degree | Aalto University (2015) : Major: Applied Materials Science : Minor: Industrial Engineering and Management
Awards & Certifications
Data adn AI
- Google Advanced Data Analytics (Coursera)
- Data Scientist: NLP Specialist (Codecademy)
- Intermediate Machine Learning (Codecademy)
- Data Scientist: Machine Learning (Codecademy)
- Business Intelligence Data Analyst (Codecademy)
- Reinforced Learning Specialization (Coursera)
Other
- Kennesaw University - Six Sigma Green Belt (Coursera)
- KAIST - Supply Chain Management (Coursera)
- Darthmouth - Strategic Leadership - Part1 (Coursera)
Contact
I am always open to discussing new opportunities, collaborations, or just chatting about tech and science.