About Me
I am Artur Stachnik, a PhD Candidate and Researcher at Complutense University of Madrid, specializing in the fusion of Artificial Intelligence, Machine Learning, and climate science. My research leverages advanced computational techniques to analyze climate patterns from natural proxies like caves and lakes, enabling new insights into past, present, and future environmental changes.
My work bridges academia and industry, applying innovative data-driven solutions to real-world challenges in energy and environmental sectors. I am passionate about transforming complex climate data into actionable knowledge that supports sustainable decision-making and technological advancement.
Outside the lab, I am an avid speleologist, exploring caves for over a decade—a pursuit that enriches my scientific perspective with patience, resilience, and a deep appreciation for the natural world.
Combining cutting-edge research with practical applications, I aim to contribute meaningfully both to scientific discovery and industrial innovation for a sustainable future.
My Research
Industry Solutions
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Energy-Mix Optimizer
Predict solar and wind power output, forecast electricity demand, and recommend the cheapest, lowest-carbon generation mix for the next 24 hours. This data-driven solution enables energy companies to reduce costs by up to 8% and cut CO₂ emissions significantly, leveraging machine learning and linear optimisation.- Technologies: Python, XGBoost, SciPy, pandas, Streamlit
- Data Sources: Red Eléctrica de España, OMIE, Copernicus ERA5
- Open Source: GitHub repository
Academic Teaching
Side Projects
S4 Summer School on Speleothem Science
I am part of the organizing committee of the S4 Summer School on Speleothem Science (Morocco - 2025), a congress aimed at connecting leading researchers with those who are just starting out in cave and climate research.