STAN ROOZEN

Geochemist specializing in thermodynamic modeling, multivariable data analytics, and ab-initio methods. Skilled in statistical learning, large dataset fitting, supercomputing, and workflow automation for mineral exploration and industrial applications. Integrates geoscience, physics, chemistry, and data science to develop innovative solutions. Passionate about coding, uncertainty quantification, and problem-solving, with a strong ability to self-learn. Extensive field experience in structurally complex ore-forming systems. Detail-oriented, collaborative, and committed to data-driven exploration success.

• Buret, Y. et al., 2017, Geology, 45, 7, 623-626, Zircon petrochronology in Cu-porphyry systems, https://doi.org/10.1130/G38994.1
• Roozen S., 2016, Magmatic controls on Cu-Mo-Au porphyry formation, ETH Master Thesis, Dr. C. Heinrich
• Van Hinsberg, V. et al., 2018, Geosciences, 8, 10, 367, Metamorphic controls on orogenic Au via thermodynamic modeling, https://doi.org/10.3390/geosciences8100367
• Dachs, E. et al., 2021, Contrib Mineral Petrol, 176, 23, Thermodynamic modeling of Fe-Mg-Al biotites, https://doi.org/10.1007/s00410-020-01771-4
• Roozen S., 2025, Solid solution modeling of tourmaline, McGill PhD Thesis, Dr. V. van Hinsberg, Combines crystal-chemical, calorimetric, and quantum methods to refine a solid solution model.