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Session 6b | AI and Machine Learning for Mine Operations

Stream 2
Wednesday, November 18, 2026
11:00 AM - 1:05 PM
Waterside Room

Speaker

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Stephanie Bream
Principal Geologist
AMC Consultants

AI-powered mine reconciliation: enhancing operational efficiency

11:25 AM - 11:50 AM

Biography

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.
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Mr Jingyu Luo
Student
McGill University - Department of Mining, Metals and Materials Engineering

Learning interpretable dispatch heuristics for real-time open-pit truck dispatching under uncertainty

11:50 AM - 12:15 PM

Biography

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.
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Mrs Lidiia Shchichko
PhD Candidate
Concordia University

L2Ofor2SP: Learning to optimize for two-stage stochastic programs

12:15 PM - 12:40 PM

Biography

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.
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Victor Balboa Espinoza
PhD Student
McGill University

Reinforcement learning-based stochastic optimization of short-term production scheduling and shovel allocation incorporating truck dispatching policies and environmental constraints.

12:40 PM - 1:05 PM

Biography

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.
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