Ph.D. Candidate at Mines Paris – PSL and Data Scientist at Eramet, passionate about leveraging data and AI to enhance safety, optimize operations, and drive sustainable performance in the mining industry. Dedicated to Operational Excellence 4.0, I aim to embed AI and data at the core of day-to-day operations by aligning advanced analytics with Lean Six Sigma principles — actively bridging the gap between technical innovation and on-the-ground adoption to unlock real business value.

Stephanie Bream is a Principal Geologist with over 19 years in the mining industry, experienced in mine geology, resource estimation, and compliance. She’s worked globally, managed major projects, and delivered significant cost savings. Skilled in industry software, she leads teams effectively and aligns geological work with business goals. Stephanie also brings commercial experience as a former COO of a startup featured on Shark Tank Australia.

Jingyu Luo is a Postdoctoral Researcher at McGill University in Montreal, Canada. He holds a Ph.D. in Operations Research from Ghent University, Belgium. His current work addresses complex planning and scheduling problems in the mining sector. He focuses on developing interpretable dispatch heuristics and decision support frameworks under operational uncertainty using machine learning algorithms. By integrating discrete-event simulation with advanced heuristic generation methods, including Genetic Programming and Large Language Models, he aims to provide transparent and efficient operational control. Previously, his research focused on the automated design of priority rules for resource-constrained project scheduling problems. His academic studies have been published in journals including Expert Systems with Applications, Swarm and Evolutionary Computation, and Annals of Operations Research.

Lidiia Shchichko is a PhD candidate at Concordia University (Montreal, Canada), conducting research at the intersection of operations research and machine learning for integrated mining value chains. Her work focuses on developing learning-augmented optimization frameworks for large-scale two-stage stochastic programs (2SP), motivated by complex mine-to-plant scheduling, transportation, and processing decisions.

Victor Balboa-Espinoza is a Mining Engineering PhD student at McGill University’s COSMO Laboratory. Leveraging his prior industry and research experience in Chile with organizations like Codelco and SMI-ICE-Chile, his work bridges applied mining engineering with advanced computing. Victor specializes in sustainable stochastic optimization and short-term production planning in mining complexes. By integrating adaptive mathematical frameworks and reinforcement learning techniques, his research incorporates carbon footprint reduction directly into fleet management and operational planning, driving the transition toward intelligent, low-emission mining operations.