Mining Engineer specializing in data-driven and AI-enabled solutions for ore control, geostatistics, and mine planning.

I design and implement analytical and machine learning frameworks that improve decision-making in mining operations, with a focus on geological uncertainty modeling, ore routing optimization, and operational risk analysis.

• Geostatistical simulation and uncertainty-aware mine planning
• Ore control, misroute tracking, and dilution analysis
• Stockpile exposure, degradation, and value-risk modeling
• Mining data pipelines and analytics using Python, SQL, and Power BI

M.S. in Mining Engineering – Michigan Technological University

Developed a multi-loss Wasserstein Generative Adversarial Network (WGAN-GP) framework for geostatistical simulation, enabling improved modeling of spatial variability and uncertainty in geological systems.

• Geostatistical Simulation with WGAN-GP
  Developed a multi-loss generative adversarial network framework for modeling geological uncertainty and generating spatially consistent realizations for mine planning.

• Mine Misroute / Targeting Dilution Tracking
  Designed a data pipeline and analytics workflow integrating Snowflake, Python, and MineSight to improve visibility and control of ore misrouting in operations.

• Stockpile Exposure & Age Risk Model
  Built an analytical framework to quantify time-dependent degradation and value loss in stockpiles, supporting better blending and reclaim decisions.

• Mining Operations Dashboards
  Developed Power BI dashboards for tracking broken reserves, routing performance, and operational metrics to support short-range planning and execution.

Python · SQL · Snowflake · Power BI · MineSight · Geostatistics · Machine Learning · Data Engineering

Mining Engineering · Geostatistics · Mineral Resource Modeling · Mine Planning · Applied Machine Learning · Digital Mining Systems

• LinkedIn: linkedin.com/in/charlesokaiaddai
• Email: caddaiokai@gmail.com