Julien Herrero is a postdoctoral fellow in the COSMO lab at McGill University working in geostatistics, and more specifically on the development of new orebody modeling methods. He is currently working on kernel methods to produce a new high-order geostatistical simulation workflow. His research also focuses on probabilistic inverse problems, Bayesian methods, and uncertainty quantification for subsurface modeling. With a background in geostatistics, geophysics, and reservoir engineering, he developed approaches such as transdimensional MCMC during his PhD thesis (Nancy school of Geology, France, 2022-2025) to better characterize geological structures and their uncertainties. This work aims to improve the reliability of subsurface models for applications in geosciences and energy.

The author holds a background in Geological Engineering, developed through field experience in exploration and structural geology. Building on this foundation, he pursued a Master’s degree in Mining Engineering at the Colorado School of Mines, where he specialized in geostatistical resource estimation, reserve modeling, and mine planning. During this period, he focused on integrating quantitative methods, uncertainty modeling, and optimization into mineral resource evaluation. In addition to his mining engineering training, he completed a Master’s degree in Data Science, strengthening his expertise in machine learning, advanced statistics, artificial intelligence, and computational modeling. He is currently pursuing a PhD in Mining Engineering, with research centered on strategic stochastic mine planning and uncertainty-based decision support. His academic and professional goal is to build a strong bridge between geology, mining engineering, and data science by developing data-driven, risk-aware frameworks for mineral resource estimation and mine planning. Through interdisciplinary research and industry collaboration, he aims to contribute to more responsible, optimized, and sustainable resource development.